ai Archives - SmartFrame

Google AI traffic drop: Here’s what publishers are doing about it

Matt Golowczynski — Tue, 09 Dec 2025 11:59:24 +0000

AI Overviews have caused publishers plenty of headaches this year. So how are they fighting back?

2025 has been a tough year for many publishers – and many are pointing the finger in one direction.

AI summaries are now widely reported to have caused significant drops in traffic across publisher sites as the year has gone on. Google’s AI Overviews are cited as the main culprit, with publishers who have traditionally relied on organic search feeling the greatest impact.

The Gemini-powered AI Mode that the search giant released earlier in the year has also been identified as a factor, although as a relatively new tool, there is less data to show what kind of impact this is having on its own.

What’s changing

This isn’t the only area where changes are reducing traffic to publisher sites.

Alongside traditional search engines – where AI summaries have joined paid advertising in nudging out organic results – a growing reliance on platforms such as ChatGPT and generative-AI search engines like Perplexity is coming at the expense of more conventional search behavior.

The integration of AI tools into other platforms, such as Meta AI appearing under news stories on Facebook and Grok within X, has also meant that users who weren’t necessarily seeking AI assistance to begin with are easily drawn into using these tools.

These join demographic shifts that are also redistributing traffic, such as younger audiences increasingly seeking information through video content published on social media platforms.

Publishers that depend on traffic to sustain their business models are understandably concerned.

For many, eyeballs aren’t only about ad impressions, but also key in promoting memberships and subscriptions, generating affiliate sales, and cross-promoting other titles in the same stable.

The need to be agile, of course, is hardly new; fail to keep up with changing user habits, technological developments, or regulatory shifts, and you won’t be publishing for long. But this particular change has nonetheless proven to be a brutal complication.

Beyond traffic losses, it has also sparked concerns about the accuracy of AI-generated summaries. Outdated information, hallucinations, and questionable source credibility have highlighted shortcomings in these systems. A fast and professional-sounding answer is not necessarily a reliable one.

More crucially, the sustainability of this ecosystem has been questioned. If it becomes unprofitable to produce the original reporting and trusted journalism that these systems rely on, what exactly will they be drawing from in the future?

All of this can leave publishers feeling despondent as we head into 2026. But the good news is that many have already begun adjusting their strategies to weather the impact.

Here’s what they’re doing – and what you can think about adopting if you’re seeing the same effects.

The scale of the issue

Before we examine some practical approaches, it’s worth looking at the scale of the problem.

Some figures make for sobering reading. At the start of the year, a study from Bain & Company indicated that around 60% of searches now yielded no clicks through to a publisher’s site.

Separate research published in July showed that AI Overviews had led to drops of 47.5% in click-through rate on desktop and 37.7% on mobile, and that sites previously ranking first for a query could see a drop of around 79% if their result was placed beneath an AI Overview.

That same month, Daily Mail publisher DMG Media claimed that AI Overviews had led to a drop in click-through rates of up to 89%.

Engaging audiences elsewhere

Search engines may be a valuable source of traffic, but many publishers have already diversified their efforts to gain audiences elsewhere for good reason.

UK publisher Future plc, for example, whose titles include TechRadar, Marie Claire, and Go.Compare, claims that only 27% of its sessions originate from Google search results. It has even developed a strategy dubbed Google Zero to address these challenges by engaging audiences directly through other channels.

The constant threat of algorithm changes and the rise in zero-click searches have persuaded many publishers to place more attention on social media platforms.

While these platforms are also vulnerable to volatility, they offer certain advantages, such as the increased likelihood of shareability within trusted networks.

This isn’t simply a way to counter a drop from search engines; it also helps build authority and trust with audiences who encounter your brand repeatedly across channels, rather than only fleetingly through a search result.

It’s also proving to be a valuable revenue stream for some. Reach plc, for example, is reportedly making five-figure sums per day from Meta’s Content Monetization program, which rewards publishers for engagement with images and videos posted on Facebook.

This follows reports earlier in the year that Meta’s algorithm changes have made Facebook a more dominant source of social referral traffic.

Many publishers are also using messaging platforms to engage existing audiences more directly, building communities through channels such as WhatsApp, Messenger, and Instagram broadcasts.

These offer high open rates and immediate reach, with features like content links and polls providing a more conversational alternative to traditional page posts.

For some users, this is also a more convenient way to engage with these brands over other channels, such as newsletters or first-party apps.

Going beyond what AI summaries can deliver

An AI summary might extract relevant text from an article and present many users with a satisfactory answer. But it’s worth remembering that text isn’t the only thing on your site that people value.

This is where the benefit of making your content rich and diverse becomes clear.

Strong, engaging images – particularly those that tell a story and invite interaction – alongside informative video content and interactive features are the value-adds that encourage people to visit your site and build a relationship with your brand, rather than feel sated by a quick AI summary.

You can also offer readers the best of both worlds. Many publishers now include a short summary at the top of an article, followed by the full piece for those who want more detail.

This won’t be the right approach for every type of content, but if readers come to expect this format, it increases the likelihood of them clicking through.

Understanding what kind of content is likely to trigger AI Overviews

Some content lends itself well to AI-overview-style answers, such as definitions, informational queries, and straightforward questions. Other types do not – and therein lie opportunities for publishers.

Recent research from Ahrefs into 146 million search results found that searches less likely to trigger AI Overviews include those related to shopping or product comparison, real estate, local searches, time-sensitive content, and sports. While the data isn’t absolute, it’s a strong directional indicator.

Many publishers already specialize in the latter two, although much of the traffic to these stories comes from direct visits rather than organic search. Content adjacent to this – explainers or analysis pieces, for example – could therefore be a good way to capture additional search traffic.

Many of these same publishers also work heavily in shopping and product comparison, so it may be worth doubling down on those efforts, particularly as this carries the added benefit of affiliate revenue.

That said, the exact form this content should take is evolving. Traditional reviews and evergreen “best-of” buying guides haven’t escaped AI Overviews unscathed, so experimenting with more tangential buying guides and product-led features could be worthwhile.

Other content less likely to be surfaced in AI Overviews includes interviews, experience-based stories, and opinion pieces.

Remembering SEO principles when optimizing for AI Overviews

Some people want their answers to be picked up by the AI-powered components of search engines and LLMs – and this is a perfectly sensible goal if you’re looking to get noticed.

There is, however, a danger of this Generative Engine Optimization (GEO) creating a distraction from maintaining good SEO, which should still be the cornerstone of your site and content strategy.

It used to be that people wrote with Google in mind. Best practice then shifted to writing naturally for humans. Now, with AI-generated snippets playing a larger role in search results, the winning approach is to combine the two – writing for humans in a way that AI systems can easily parse and understand.

Exploring opportunities in image search

Text-based content has clearly been impacted by AI Overviews, and text remains the dominant way people search. But this isn’t the only way people look for information.

Image search has become a more popular method for certain types of discovery, particularly when shopping for products. It’s now estimated that Google Images processes over a billion requests per day.

Whether this works for you depends in part on whether your site is image-led and whether those visuals draw people in.

Publishers with original, high-quality images that stand out in image results – rather than the generic product photography found everywhere else – are likely to reap the greatest benefits.

And since engaging visuals can directly influence metrics such as dwell time, creating or sourcing strong images is well worth the effort.

Final thoughts

Staying agile, diversifying traffic and revenue sources, and engaging audiences in ways that AI Overviews cannot will help publishers remain in the strongest possible position as AI tools evolve and create new challenges.

But practical steps like these are only part of what’s likely to shape future success. Regulatory changes are also likely to play a significant role.

The UK-based Professional Publishers Association (PPA) recently submitted recommendations to the Competition and Markets Authority (CMA) that aim to create a fairer value exchange between publishers and search engines, giving publishers greater visibility and control.

Suggestions include clearer transparency over acquisition sources in GA4, more prominent links to publisher sites within AI Overviews, and requiring Google to disclose the data it scrapes for those overviews – remedies that would help address several of the structural issues publishers now face.

These are, of course, only recommendations. But with growing concern over the sustainability of the current system, and those concerns only getting louder, the likelihood of such remedies being adopted is increasing.

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SmartFrame Technologies named one of the UK’s most innovative media tech creators

Matt Golowczynski — Tue, 27 May 2025 08:12:45 +0000

SmartFrame Technologies makes the BusinessCloud MediaTech 50 ranking for the second year running

The BusinessCloud Media Tech 50 ranking celebrates the very best media tech creators in the UK – and we’re thrilled to have secured a spot on this year’s ranking.

Following an initial shortlist of 82 names, SmartFrame Technologies claimed 26th position. The final ranking was determined by a combination of a judging panel and a public vote held earlier this month.

This marks the second consecutive year that SmartFrame Technologies has earned a place on the list.

“SmartFrame has had an incredibly busy year. We’ve expanded our very talented team, attracted a wave of new content partners, publishers, and advertisers, and seen significant month-over-month growth in our network,” says Rob Sewell, CEO and Co-Founder of SmartFrame Technologies. “It’s wonderful to be recognized for our technology and the way we’re innovating within the industry, creating a whole new marketplace for the benefit of all participants. I’m thrilled to see our name alongside several other respected businesses, and I’m incredibly proud of the entire SmartFrame team.”

You can view the full list on the BusinessCloud site.

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SmartFrame Technologies appoints Mark Catlin as Global Sports and Entertainment Director

Matt Golowczynski — Tue, 06 May 2025 08:00:00 +0000

Former Topps International General Manager and Portsmouth FC CEO joins London-based company in new role

London, United Kingdom – 6 May 2025: SmartFrame Technologies, the company revolutionizing the way digital images are delivered, protected, and monetized online, is delighted to announce the appointment of Mark Catlin as its new Global Sports and Entertainment Director.

A seasoned executive in the international sports business, Catlin brings more than 20 years of leadership experience to SmartFrame Technologies, having driven commercial growth, strategic partnerships, and operational transformation across some of the biggest names in football, media, and entertainment.

Most recently, Catlin served as General Manager of International Sports and Entertainment at The Topps Company (part of the Fanatics group), where he transformed the brand’s international business strategy. During his tenure, he helped to secure and deliver on multi-million-pound partnerships with major rights holders and brands including F1, UEFA, Disney, and Bundesliga, as well as elite football clubs such as Manchester United FC, Paris Saint-Germain FC, FC Barcelona, and Liverpool FC, while also revolutionizing the Topps International business.

Prior to this, Catlin served as CEO and director of Portsmouth FC, where he helped lead the club out of administration, delivered sustained on-pitch success, eliminated over £7 million in debt, and achieved six consecutive years of EBITDA profitability. His community-focused leadership also earned the club multiple EFL Community Club of the Year awards.

In his new role, Catlin will spearhead the company’s global sports and entertainment strategy, focusing on building partnerships with clubs, leagues, federations, and media rights holders to help unlock new value and revenue from their images through SmartFrame’s unique image-streaming technology.

“Mark has a rare blend of commercial expertise, leadership, and credibility within global sport,” said Rob Sewell, CEO of SmartFrame Technologies. “His proven ability to drive growth while navigating complex stakeholder relationships makes him the ideal fit to lead our sports and entertainment strategy.”

“I’ve spent my entire career building businesses, forging partnerships, and developing new ways to create commercial value, most recently across the sports and entertainment ecosystem,” says Mark. “I took the decision to join SmartFrame as I truly believe its technology is a game-changer for sports and entertainment brands, content owners more broadly, and publishers, both with regard to IP protection and risk-free incremental revenue generation. I’m excited to help accelerate its adoption across the industry.”

— Ends —

About SmartFrame Technologies

Founded in 2015, SmartFrame Technologies is a London-based technology provider whose image-streaming platform redefines the standard for online image publishing.

It unites sports brands and other content owners with publishers, advertisers, and online audiences, and ensures that images are delivered in the highest quality with maximum security, clear provenance, and detailed analytics.

Furthermore, through its contextual ad tech component, brands can reach audiences with high-impact, contextually targeted, in-image advertising and sponsorship placements in a way that recognizes the interests of its viewers and simultaneously complies with global privacy regulations.

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How can publishers use AI responsibly?

Liam Machin — Mon, 31 Mar 2025 09:30:00 +0000

Can digital publishers use AI responsibly across content creation, personalization, editorial workflows, and user data protection – all while maintaining ethics and trust?

Digital publishing has become an expansive field, embracing a broad spectrum of content creators, from bloggers and online magazines to established news outlets with global reputations.

Many publishers are increasingly using artificial intelligence (AI) to streamline content production and distribution processes. These tools promise enhanced efficiency, increased engagement, and quality assurance.

However, responsible integration is important. The core mission of journalism – reporting the facts with impartiality, accountability and ethics – must not be jeopardized by new technology.

AI has already altered the way publishers identify trends, create content, and personalize user experiences. Platforms have begun to explore everything from AI-written news summaries to recommendation engines that tailor articles to individual reading habits.

Yet, as AI continues to reshape the publishing landscape, ethical considerations such as transparency, accuracy, privacy, and bias mitigation have become more important than ever.

Maintaining the public’s trust depends on how responsibly these systems are implemented and how proactive publishers are in addressing potential pitfalls.

Given the pace of innovation, now is the time for digital publishers to define clear standards for responsible AI usage.

This article will explore several applications of AI in digital publishing, highlight the ethical and operational challenges involved, and recommend ways publishers can strike the right balance between leveraging emerging technologies and adhering to journalistic principles.

How AI is transforming digital publishing

Content creation and curation

AI’s impact on content creation has grown significantly in recent years, offering automated solutions for everything from financial report summaries to sports updates and localized weather forecasts.

These tools aim to lighten the workload for editorial teams by handling repetitive writing tasks. The Associated Press, for example, uses automated systems to produce corporate earnings reports at a greater volume and speed, freeing human journalists to focus on more complex and investigative pieces.

Beyond writing briefs and summaries, AI can sift through vast amounts of data to identify emerging stories and trends. A publisher might use AI-driven tools to monitor social media or analyze online search patterns to pinpoint topics gaining traction in real time. This helps editors and writers quickly produce relevant content that keeps pace with fast-changing news cycles.

AI-enabled curation from companies such as Curata is also transforming how publishers compile articles and multimedia from various sources.

Automated systems can filter through large data sets, select relevant pieces, and categorize them based on topic or sentiment. By automating portions of the curation process, editors can save time and maintain consistent quality standards.

Improving reader engagement with AI personalization

The capacity for personalization is arguably one of AI’s most significant contributions to digital publishing.

By analyzing user behavior, reading history, and click-through rates, AI algorithms can deliver tailored article recommendations, boosting engagement and enhancing overall reader satisfaction.

Indeed, the BBC recently announced the creation of an entirely new department to focus on this area.

This study from McKinsey and Company outlines the benefits of personalization. Such results could likely have a direct impact on user retention because readers who consistently see articles aligned with their interests could be more likely to spend additional time on a platform.

Another potential benefit of personalization engines is that they can help readers discover archived or niche content that might otherwise remain underexposed. This allows publishers to maximize their entire content library.

AI-assisted editorial workflows and quality control

Editorial teams can benefit from AI tools that assist in proofreading, fact-checking, and content structuring. These systems can quickly detect typographical errors, grammatical inconsistencies, and possible factual inaccuracies, taking on much of the routine quality assurance process.

High-profile publishers such as The Times, Der Spiegel, and The New York Times are already making use of such technologies.

With AI handling simpler tasks, editors are freed to concentrate on arguably more important aspects of their role, such as strategic decision-making, investigative reporting, and opinion pieces.

In more advanced setups, machine learning algorithms can detect subtle semantic patterns and flag language that might appear biased or misleading, like the technology used in this research from herEthical AI. This technology could be a powerful tool in ensuring balance and impartiality in the newsroom.

AI tools can also help editors fact-check AI findings with trusted databases, reducing the risk of misinformation.

Proper integration of these applications could support a higher level of accuracy and consistency, and could also play a role in enforcing a publication’s style guide, house style, and tone across all content – although human input is still essential.

Transparency and trust in AI-generated content

As AI-generated or AI-enhanced content becomes more common, transparency about these processes is vital. Many readers value the integrity associated with traditional journalism and therefore may be cautious about automated content.

When adopting AI, a sure step publishers can take towards garnering ongoing trust is to clearly disclose when AI plays a role in generating or recommending articles.

Alongside this, developing explicit editorial guidelines is a key part of maintaining consistency. These guidelines could define the categories of content where AI might contribute, such as automated summaries or personalized recommendations, while also outlining how human editors review output to ensure quality and accuracy.

A well-crafted policy may also require disclosure statements where AI is involved, signalling to readers that certain content was generated or enhanced by automated systems.

TIME Magazine’s introduction of an AI chatbot for Person of the Year is an example of transparent use of the technology. By openly indicating that a bot, rather than a human editor, is responding to user queries, TIME maintains trust and integrity.

Addressing AI bias in publishing workflows

No algorithm is free of bias because AI models learn patterns from the data on which they are trained. If that data includes narrow perspectives or historical prejudices, the AI may perpetuate them in content recommendations or even in the tone of generated articles.

A notable example of this was when Amazon’s AI-based recruitment tool was allegedly found to be biased against female candidates, as reported by Reuters.

This bias resulted from the algorithm being trained on resumes submitted over a ten-year period, predominantly from male applicants, reflecting the tech industry’s gender imbalance. Consequently, the system favored male candidates, highlighting the risk of perpetuating existing biases through AI.

From an ethical standpoint, publishers need to make deliberate efforts to address this issue. Failing to do so can damage a publication’s reputation and potentially alienate readers, who expect balanced reporting.

Publishers should ensure their AI models use diverse training data that includes a wide range of sources, demographic groups, and cultural contexts.

Regular audits of AI-generated outputs are equally important to identify any biased language or skewed coverage.

Involving editorial staff from varied backgrounds can help, since human review of flagged content may catch subtle biases that automated systems fail to recognize.

Continuous evaluation and iterative improvements in the training process help maintain both credibility and fairness.

How publishers can protect user data while using AI

Personalization and audience analytics – core strengths of AI – rely on collecting and processing user data. This increases publishers’ obligations to protect that information under evolving privacy regulations.

Europe’s General Data Protection Regulation (GDPR) and California’s Consumer Privacy Act (CCPA) are two prominent frameworks, with many other countries also implementing or contemplating their own data protection laws.

Complying with these regulations involves carefully managing data collection and usage. One best practice is data minimization, whereby publishers only gather the information essential for specific AI-driven tasks.

Another is obtaining transparent user consent, allowing readers to opt in or out of data collection and providing them with clear explanations of how their data will be used.

Additionally, publishers must safeguard the data they collect by implementing robust security measures, from encryption to anonymization, to reduce risks of breaches and misuse.

Respect for privacy is not only a legal requirement but also an important way to maintain reader trust, which can easily be lost if mishandling of data comes to light.

AI accountability in journalism

Even sophisticated AI requires human oversight. Issues arise when AI-generated outputs contain inaccuracies, perpetuate offensive stereotypes, or misinform readers.

To mitigate such risks, organizations should establish accountability frameworks, such as The Global Principles for Artificial Intelligence, developed by a group of 26 global publishing organizations.

Frameworks like this make it clear who is responsible for reviewing AI-driven content and addressing any inaccuracies or ethical concerns.

This oversight typically involves regular monitoring and clearly defined escalation protocols for disputed or erroneous outputs. When a correction or retraction is necessary, publishers should offer transparent updates – much like they would for human-generated copy.

A recent notable case was that of The Los Angeles Times, which faced criticism over AI-generated content discussing the Ku Klux Klan (KKK). This, according to the Guardian’s report, “appeared to downplay the KKK’s racist history”.

AI in the newsroom: Employment and legal considerations

The rise of AI in the newsroom and across editorial departments inevitably raises concerns about potential job displacement.

Although automation can handle repetitive tasks, editors and journalists can harness AI to amplify their productivity and delve deeper into creative and investigative work.

Rather than viewing AI as a competitor, many organizations benefit by training staff to collaborate effectively with these emerging tools.

Offering professional development programs that teach data analytics, AI collaboration, and digital storytelling can help employees adapt to new demands. Creating hybrid roles, where editorial expertise is blended with technical skills, can encourage a culture of innovation.

When staff members understand that AI can complement, rather than replace, their expertise, anxiety over job loss tends to subside. Maintaining open communication throughout these transitions also helps preserve morale and sets a forward-thinking tone within the organization.

What are the legal considerations when adopting AI in publishing?

AI-driven content presents evolving legal questions about copyright, intellectual property, and liability.

Who holds the rights to an article generated primarily by an algorithm? How should publishers handle potential plagiarism or unauthorized use of source materials by an AI engine? At the time of writing, debates are continuing.

These issues underscore the need for media organizations to remain proactive and stay informed about new regulations and case law.

Clear contracts with third-party AI vendors help provide clarity on licensing and ownership rights, reducing the risk of disputes down the line. Indeed, there have already been major partnerships formed between publishers and AI developers, such as AP signing a deal with Google and ProRata.ai partnering with various UK publishers.

From the point of view of a publisher using AI tools to create content, maintaining a level of human oversight and editorial review ensures that AI outputs align with the organization’s brand and ethical standards.

Periodic reviews of AI processes and outputs can also help detect inadvertent infringements or replicating of copyrighted material.

By actively engaging legal counsel who specialize in media and technology, publishers can better navigate the complexities posed by AI-generated or AI-assisted content.

Conclusion: Building a responsible AI strategy in publishing

AI is steadily reshaping the future of digital publishing, driving changes in the way content is produced, curated, and consumed.

For online magazine and news publishers, the potential to automate repetitive tasks, enhance personalization, and optimize editorial workflows is both exciting and challenging. These innovations promise efficiency gains and better engagement but also require well-designed strategies to address ethical, legal, and social implications.

Implementing AI responsibly is not just about embracing new technology. It’s about renewing commitment to transparency, accuracy, and reader trust. As the global regulatory landscape evolves, so too must editorial guidelines and data practices.

Publishers that adopt a collaborative approach, inviting insights from technologists, regulators, industry peers, and readers, are best positioned to thrive. Over time, a balanced integration of AI will likely become a hallmark of forward-looking, ethical publishing, ensuring that journalism remains grounded in integrity even as it evolves.

By taking measured steps now, publishers can harness AI as a tool for creativity, efficiency, and enhanced reader engagement, without compromising on the ideals that have long defined quality journalism.

As the technology matures, ongoing education, policy refinement, and public dialogue will help publishers refine their AI strategies. The future of publishing may indeed be powered by algorithms, but it will still rely on human integrity and judgment to guide the way.

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AI in Advertising: Friend or Foe?

Peter Townshend — Tue, 28 Jan 2025 10:49:00 +0000

AI is widely used in advertising, but like any new technology, it requires careful handling. Here, we explore its advantages and challenges

Artificial intelligence (AI) is rapidly transforming advertising, bringing new levels of precision, scalability, and creativity to an industry long driven by gut instincts and demographic segmentation.

Indeed, an article in the Wall Street Journal recently reported that 94.1% of advertising revenue will be informed by AI by 2029.

From machine learning algorithms that fine-tune targeting in real time to generative AI that spins out fresh ad copy, AI-enabled solutions promise deeper consumer insights and higher returns on ad spend.

Yet, as with many disruptive technologies, adoption is not without risks.

Whether leveraging AI for automated ad placement, optimization, or personalized messaging, marketers must tread carefully.

Ethical concerns – such as privacy, bias, and transparency – cast a growing shadow, and poorly implemented AI strategies can undermine consumer trust or even cause reputational damage.

So, is AI in advertising a friend, a foe, or perhaps both?

In this article, we will explore AI’s role in modern advertising, discussing its main benefits, key challenges, and how B2B marketers can integrate it responsibly.

We will also delve into regulatory considerations and actionable best practices for mitigating risks while capitalizing on AI’s transformative potential.

Can AI achieve accuracy and relevance in campaigns?

One of AI’s biggest draws is its capacity to analyze vast amounts of data and derive actionable insights.

By sorting through consumer behavior trends, social media interactions, and demographic variables, AI systems can predict purchasing intent, segment audiences, and personalize content.

This is reinforced by industry uptake. According to a Statista survey, 55% of marketers said they were using AI for advertising, with another 40% looking to do so within the next year. But how accurate is it?

AI in programmatic advertising

AI-driven programmatic platforms like Google Ads or The Trade Desk harness machine learning to automate bidding strategies, allocate budgets efficiently, and optimize campaign performance – and report strong success off the back of the technology.

For example, according to Google Ads, AI-powered search ads helped one of its clients enjoy a 182% increase in signups and a 258% increase in clicks.

This efficiency is especially attractive for B2B companies, where longer sales cycles and high-value deals demand precision at every stage of the funnel. However, this success does not come without risks.

Data bias and the risk of misrepresentation

One major issue is that AI trained on historical data can reflect and perpetuate societal biases.

A famous example involves Amazon allegedly scrapping a sexist AI recruitment tool that, according to a report by Reuters, discriminated against female candidates because it was trained on historical data that skewed towards male applicants.

While Amazon declined to comment specifically, the story provides food for thought when considering how AI can inadvertently reinforce stereotypes or underrepresent key audience segments.

In advertising, such biases can manifest in unbalanced targeting (for example, certain demographics receiving fewer or even exclusionary ads), leading to reputational risks and missed market opportunities.

As an example, a study of Google Ads by Carnegie Mellon University found that women were shown ads for high-paying jobs significantly fewer times than men.

Keeping data fresh and relevant

Another hurdle is data freshness. AI algorithms relying solely on historical datasets may not accurately reflect changing consumer tastes or market conditions.

This can lead to model drift (also known as concept drift), whereby AI models are unable to respond to industry trends, product releases, or shifts in buyer behavior.

A real-world example of model drift is the case of real estate company Zillow and its failed implementation of an automated property valuation algorithm.

This analysis from The AI Journal reports that changes in the market and increased activity between initial testing and expansion resulted in overvaluations of over $500m. The sustained losses forced Zillow to lay off 25% of its workforce back in 2021.

The most comprehensive protection against model drift is the integration of real-time data streams into your AI models using online machine learning. Where this is not possible, the issue can be mitigated by combining information with contextual data or regularly updating a training data set. However, the truth is that any model relying on batch learning is exposed to a level of risk.

The role of generative AI in advertising

Generative AI offers an effective way to expedite the content creation process, enabling marketers to develop ad copy, visuals, and even videos at scale. Popular generative AI tools include Jasper, Adobe Firefly, Midjourney, ChatGPT, and Google’s Veo 2.

According to Hubspot, 84% of marketers believe generative AI boosts efficiency in content creation and 85% believe it improves quality of content.

Using AI for ad creatives

While using generative AI for creativity is a contentious issue, there’s no doubt it can help expedite certain processes. And when used right, it can provide an innovative angle for ad campaigns.

One brand that’s embracing the technology is Coca-Cola, which has received mixed reactions that perfectly illustrate the controversy surrounding the topic.

On one hand, the brand was met with outrage when it recreated its famous Christmas ad using AI. On the other hand, its ‘Create Real Magic’ campaign – a collaboration with OpenAI and Bain & Company that invited digital artists to produce ad creative using DALL-E and GPT-4 – was a great all-round success.

The latter illustrates how generative AI can foster higher interactivity and a sense of ownership among audiences, whether in B2B or B2C contexts.

The former, while arguably more successful in terms of attention, illustrates how strongly your audience may feel about generative AI – a reminder to proceed with caution.

AI content localization

For marketing campaigns spanning multiple regions, localized content can significantly boost relevance and engagement.

This study by CSA Research found that 76% of online shoppers prefer to buy products with descriptions in their own language with 40% declaring they would never buy from websites in other languages.

By combining generative AI with multilingual capabilities – such as integrations with ChatGPT or other translation APIs – marketers can adapt visuals and messaging to suit local cultural norms and linguistic nuances.

Quality control challenges

In theory, this sounds like a no brainer, but in practice it can often be a different story. Generative AI can produce flawed or misleading outputs, ranging from nonsensical text to images that unintentionally violate brand guidelines.

A real-world example of how AI hallucinations can cause problems was the case of a New York lawyer referencing non-existent cases in court after using ChatGPT for legal research. While not specifically linked to advertising, the case paints a worrying picture.

The rapid scalability of such tools means mistakes can go viral before human oversight catches them. That’s why implementing structured review processes – like AI auditing tools, brand safety protocols, and human editorial checkpoints – is extremely important in minimizing the risk of reputational harm.

Risks of over-intrusiveness and brand damage

Personalisation

While AI excels at personalizing user experiences, hyper-personalisation does come with risks. Spotify Wrapped in 2024 was a high-profile example of a big brand missing the mark.

There is, of course, also the risk that personalisation can give insight into sensitive user information such as purchase or Browse histories.

Ad placement

The risk extends to where your ads appear. A Guardian investigation revealed that some programmatic ad placements have landed on extremist or highly controversial websites, tarnishing brand reputation.

For brands that rely heavily on trust and professionalism, appearing alongside inappropriate content can be especially damaging. Regular audits, negative keyword lists, and sophisticated content filters are essential in mitigating these risks.

AI audit committees

Implementing an AI policy that sets rules, boundaries and best practices is an important step in ensuring brand safety when utilizing AI in advertising, but it is surprising how few organizations have anything in place.

A 2023 survey from Deloitte showed that only 13% of respondents had a formalised AI oversight framework, but the importance of AI governance is growing at an alarming rate.

These internal councils can facilitate an ongoing dialogue among marketing teams, data scientists, and legal advisors to proactively spot potential issues and set clear boundaries for AI’s role in advertising.

Ethical concerns and regulatory challenges

Data privacy and consumer trust

Data privacy remains a top concern, especially when AI thrives on large data sets. Whether it’s collecting purchase histories, Browse patterns, or business interactions, brands need to handle data ethically and transparently.

A report by the UK Government shows that 57% of respondents see stolen information as the biggest risk surrounding the gathering and storage of digital data.

The fact that the same study found that “scary” was the most popular word people used to describe AI underscores the need for clear consent mechanisms and accessible privacy policies.

GDPR, FTC, and emerging regulations

In Europe, the General Data Protection Regulation (GDPR) sets stringent rules on data collection, consent, and user rights.

Companies that run afoul of GDPR face steep fines and reputational blowback. Meanwhile, the Federal Trade Commission (FTC) in the United States recently approved a final order against a tech company about deceptive AI practices.

Beyond these existing frameworks, the European Union has enacted the AI Act, which could impose further obligations on developers and users of AI systems. The legislation aims to classify AI applications based on their level of risk, potentially restricting certain uses in advertising.

While it may not have a huge effect on the day-to-day work of ad executives right now, savvy marketers should keep abreast of regulations to ensure compliance and foster consumer trust.

Responsible AI standards

Leading technology firms like Microsoft have introduced Responsible AI Principles, which include guidelines for fairness, reliability, safety, privacy, and inclusiveness.

These internal frameworks can serve as models for smaller organizations eager to establish robust governance around their AI activities. By adopting clear ethical guidelines – such as requiring sound reasoning behind AI decisions or offering user-friendly opt-out options – brands can pre-emptively address consumer concerns.

The future of AI in advertising

While there are some early concerns and bumps in the road, AI in advertising is undoubtedly here to stay. Here are a few of the most prominent emerging AI technologies that offer a glimpse into the next wave of advertising innovation:

Predictive analytics

Leveraging machine learning to anticipate behaviors like lead scoring, churn rates, and high-intent buying signals. By analyzing patterns in CRM data, website visits, and intent signals, marketers can deliver the right message at precisely the right moment in the buyer’s journey.

Virtual influencers

AI personas like Lil Miquela have amassed large social media followings and could provide brands with a controlled, AI-generated persona to interact with potential buyers.

Real-time dynamic creative optimization (DCO)

AI will automatically assemble ad elements (headlines, visuals, calls to action) based on user behavior and context, delivering hyper-relevant creative at scale.

AI-driven conversational ads

Brands can engage prospects through chat-like interfaces on LinkedIn or other platforms, guiding them toward product demos or case studies.

Advanced personalization

Future tools may analyze not just Browse history but also emotional cues like sentiment analysis to tailor messaging at an unprecedented level of detail.

Advanced fraud detection

As AI-driven advertising grows, so will AI-driven fraud. Innovations in fraud detection using machine learning could protect marketers from wasted spend and ensure ad impressions reach legitimate audiences.

By anticipating these developments, marketers can position themselves as early adopters, leveraging AI’s evolving capabilities to outmanoeuvre competitors and deliver higher value to clients.

Final thoughts

AI has already proven itself a formidable ally in advertising, unlocking insights, efficiency, and creative potential that were previously hard to imagine.

From predictive analytics that refine lead generation to generative AI tools that produce tailored, on-brand content in multiple languages, the opportunities for B2B marketers are vast.

Yet, AI’s integration comes with risks: algorithmic bias, data privacy issues, and the potential for reputational damage if poorly executed.

The path forward lies in balancing innovation with accountability. By incorporating human oversight, adopting ethical frameworks, and staying abreast of regulatory developments, brands can position AI as a trusted partner rather than a liability.

As AI continues to evolve – offering real-time dynamic creative, conversational ads, and advanced personalization – those who invest in responsible usage today will be best equipped to reap AI’s transformative rewards tomorrow.

In the end, whether AI is a friend or foe depends largely on how thoughtfully it is deployed. For marketers ready to embrace the future, the key is clear: pair cutting-edge AI with ethical boundaries and human judgment to ensure that, indeed, AI becomes your brand’s most valuable ally.

The post AI in Advertising: Friend or Foe? appeared first on SmartFrame.

How can publishers drive revenue growth in 2024?

Liam Machin — Mon, 11 Mar 2024 09:15:36 +0000

Publishers face a constant battle of staying profitable while maintaining journalistic integrity, so the need to develop several revenue streams is critical. We break down some of the key challenges and potential solutions.

As more independent publishers emerge each year, the fight for attention is becoming an increasingly important battle for established ones. Losing readers, after all, usually means losing revenue as well.

According to The Guardian, over the last decade, approximately 300 local newspapers in the UK have closed, with UK print publication ad revenue halving over a six year period.

The picture is much the same in the US; one-third of local news outlets have closed since 2005, mainly thanks to a lack of demand from consumers due to the emergence of other media sources.

Amid the ever-present volatility brought by the explosion of misinformation proliferation and the use of AI technology, the need for adaptability and innovation becomes critical for publishers.

However, the end of third-party cookie support in Google Chrome presents a new opportunity for both news publishers and media companies to take stock of their revenue streams and better understand the advertisements they’re putting in front of their audiences, optimizing accordingly.

What are the biggest challenges facing news publishers today?

In today’s world, trust is everything. Publishers are working hard to make sure their reports are accurate and to fight false information.

In fact, a World Economic Forum report labeled misinformation and disinformation generated by artificial intelligence as the single biggest threat to the world over the next two years, one that poses a greater risk than even extreme weather events and armed conflict.

A significant story from this year saw a series of deepfake images of Taylor Swift go viral with over 27 million views in 19 hours before it was taken down.

Not only does this type of manipulation harm the person in question, but if the fake images were incorrectly reported to be genuine by a prominent news outlet, reputational and financial harm could easily follow.

Fortunately, this particular issue was resolved swiftly. Nevertheless, it highlights the importance of having clear and robust fact-checking procedures in place to foster a more transparent ecosystem, one that prioritizes truth, integrity, and audience trust.

While cost-cutting measures and organizational changes are becoming more frequent for many online publishers, they should not compromise the quality of content.

Exploring ways to train and upskill existing employees, or to leverage new technologies, can be an easy way to minimize risk without ramping up costs.

The decline of print media

As we have seen, print advertising – once the cornerstone of revenue for many publications – has experienced a strong decline in recent years.

Online media, and the shift of younger audiences away from physical to digital media sources, have taken away much of its demand.

A telling sign can be seen in the total number of actively purchased print publications in the UK, which have fallen by 70% between 2010 and 2022.

The same can be seen in the US where. According to Pew Research, the daily newspaper circulation (both print and digital) was 20.9 million, falling around 8-10% compared to 2021.

This lack of demand for print news has forced more publications to emphasize online media and the advertising revenue that comes from that, as print faces a dire need to innovate.

Online advertising: A necessity for survival

With online publishing, display advertising has become increasingly predominant in their online reading experience – often leading to complaints.

On top of that, the digital space is undeniably saturated with competition, which makes it challenging for publishers to secure a significant share of online ad revenue as their space is valued against what the advertisers are willing to pay.

A Digiday and Permutive research report found that 60% of publishers said advertising would account for “one-fifth or less” of their annual revenue in 2023.

Programmatic advertising, although efficient in terms of speed and ad placement, often yields relatively low returns when compared to alternative agreements and carries the risk of displaying irrelevant or even harmful content.

Despite the possible reach of digital advertising, publishers often struggle to maximize returns, especially with the dominance of programmatic advertising.

The report found that open marketplace deals produced less than 21% of their annual ad revenue, showing the demand for more direct relationships and agreements with brands to generate the best deals for everyone involved – something we’ll go into more detail later.

What can publishers do to improve engagement and increase revenue?

There are several different ways in which publishers can increase engagement with their readers and simultaneously boost revenue – which is most effective is up for debate.

The continued evolution of privacy measures, such as the dropping of support for third-party cookies on Chrome, highlights the value of strategic partnerships.

Publishers can increase their revenue and attract more readers by partnering with relevant tech companies, brands, or agencies that can help them promote products and services that their audience already enjoys.

There are numerous success stories, in particular in Norway, whose news outlets are judged to be leaders in digital media subscriptions. But these partnerships can go beyond just brand deals and include other news sources.

The New York Times, for example, has developed a unique subscription bundle that combines other areas of its business to offer potential customers a more rounded package.

It includes news, games, recipes, audio & podcasts, reviews via Wirecutter, and sports coverage via The Athletic.

This bundle has helped the organization achieve an annual digital subscription revenue of more than $1bn for the first time in its history, with 9.7 million of them digital-only.

These models aren’t new but by creating more holistic packages it offers additional value to customers and gives ample opportunity to promote affiliated businesses.

Understanding audience preferences

Already, most publishers have embraced a multi-platform content strategy to engage their audience segments effectively.

Yet, this type of audience segmentation can also dictate decisions about what brands and advertisements should feature onsite.

Research from the Digiday and Permutive paper found that 65% of those interviewed said data and analytics have the greatest impact on driving positive ad revenue outcomes.

As well as all this, direct ad sales, curated marketplaces, and sponsored content partnerships all provide more tailored opportunities for publishers to have greater control over ad placement suited for their audience.

Adopting other forms of advertising, such as in-image advertising and augmented reality ads, also presents new options to promote any advertisements effectively.

News outlets can’t control what brands make, but stronger relationships with brands and agencies promote more free sharing of ideas to achieve the best results for both parties.

How does SmartFrame help news publishers generate more revenue?

At SmartFrame, we are committed to creating the most sustainable ecosystem for all those involved with photography and publishing.

We’ve designed an ecosystem where all parties receive due recognition for their contributions.

Our publisher partners enjoy unrestricted access to a vast library of exclusive and historical imagery through an ad-funded model.

Here’s an example of such historic images that are only accessible in this format through our New Zealand Rugby library.

This grants them license-free access to streamed image embeds for online usage, together with JPEG access for use in print and where an embed cannot be used.

Moreover, publishers maintain full control over the ad campaigns, with each campaign undergoing approval before it’s put into place. This ensures that publishers retain control over the content featured on their platforms.

By embedding our images into their content, publishers benefit from the advertising that’s layered over the image in a non-intrusive manner.

Think of it as a new advertising billboard seamlessly integrated into an article that enriches the content with exclusive imagery and provides contextually relevant ads without disrupting the reader’s attention or load times.

The post How can publishers drive revenue growth in 2024? appeared first on SmartFrame.

Tech predictions 2024: What we expect

Liam Machin — Tue, 09 Jan 2024 09:26:47 +0000

The next 12 months should usher in several significant changes across the tech world. So what should we expect?

As we move into 2024, the tech world seems to be at something of a crossroads.

With an expected 60+ national elections on the horizon, including major contests in some of the world’s most powerful nations, the tech industry will play a crucial role in shaping responsible public discourse.

From Google’s Privacy Sandbox to the still-much-anticipated explosion of metaverse platforms and experiences, there’s plenty that could end up shaping 2024.

Furthermore, McKinsey estimates that AI will contribute $13 trillion to the global economy by 2030 as it spreads across different industries.

Here are just a few areas that we think you should keep tabs on this year.

Regulatory crackdown on Big Tech

Regulators around the world are battling to regain control over industry giants Alphabet (Google), Amazon, Meta, Microsoft, and Apple.

There is a growing belief that increased regulation of the biggest tech players will create a more competitive, innovative, and ethical environment, one that prioritizes individual rights and consumer protection.

As these debates continue, it’s important to strike a balance between effective regulation and stifling innovation.

The ultimate goal should be to create a regulatory framework that promotes a fairer tech ecosystem that benefits consumers and businesses alike. However, this is easier said than done.

With several global elections and major lawsuits popping up around the world, it may well be a challenging year for the most prominent names in the tech world.

Artificial Intelligence (AI) deepening its integration into Western society

The last few years have seen AI dramatically streamline workflows, and we’re now at the stage where the technology is starting to free up our time at work.

Although this incredible growth does come with some potential risks, AI’s ability to remove arduous and boring tasks from our lists is surely something to look forward to, allowing people to focus on more creative and strategic endeavors.

ChatGPT is perhaps the most prominent example. Launched in November 2022, it reached 100 million users within just 60 days of its launch and is now said to be used by 92% of Fortune 500 companies.

In the creative world, the boundary between human and machine creativity continues to blur, from the advanced editing features of Google Pixel 8 – dubbed Magic Editor – to professional-grade software that leverages AI to transform images and videos and other media.

But while AI-powered tools have put artistic power into everyone’s hands – regardless of skill level – copyright issues mean that regulations will continue to evolve to ensure the responsible use and application of these tools.

Demand for true human authorship

While many people accept AI’s potential to improve our lives, there’s an inevitable downside to contend with too.

2023 saw a dramatic increase in “dud” websites, also known as unreliable artificial intelligence-generated news and information websites (UAINs), filled with generic text with no value or depth.

In response, a trend emerged with a renewed appreciation for human connection and authenticity. Results from one recent survey that reflected this shift showed that 72% of people stated they preferred to read content written by a human.

The recent growth of micro-influencers and the power of word-of-mouth marketing reflect a genuine desire for real-world recommendations and relatable voices.

Will this cause a shift in demand for keywords and SEO? Opinions are divided, but platforms such as LinkedIn have seen explosive growth in recent years, fueled by a hunger for genuine expertise instead of algorithmic curation.

Staying informed about misinformation

Discussions around image manipulation, privacy, and AI bias within photography are poised to take center stage in 2024.

Last year saw multiple stories fabricated from fake content, including a reported explosion at the Pentagon, Such stories can easily – and quickly – lead to devastating consequences.

With AI-powered editing tools, such as Photoshop’s Generative Fill, widely available, the line between reality and fabrication continues to blur.

The most obvious answer to this is in initiatives such as the Adobe-led Content Credentials. Several of these continue to grow in stature, and this should help improve overall transparency about how an image has been changed. And by increasing awareness of online manipulation and misinformation, greater demand for more ethical practices should follow.

More metaverse momentum

Excitement for virtual worlds and experiences continues to build in certain circles but the core challenges for the metaverse to expand are still in place. Will 2024 be the year it finally makes a real breakthrough?

Key hurdles, such as accessibility, user behavior, and privacy concerns, are undoubtedly the main reasons why widespread adoption has not yet properly taken place – much like the internet in its early years.

And yet, despite the arguments against it, and some already claiming Meta’s iteration has died a quiet death, many see huge potential in metaverse’s market potential.

Bloomberg predicts its value could reach $615bn by 2030, while McKinsey shoots higher by suggesting it could reach a lofty $5tn the same time frame.

With investments pouring in from the likes of Meta, Microsoft, and Epic Games, it does feel like we’re at a potential tipping point.

Even the World Economic Forum (WEF) is introducing “metaverse sessions” in a bid to democratize access to its events – especially for young adults and entrepreneurs.

Changes in online search

Following on from the metaverse, many people have already pointed out that the rise of alternative sources of product information, such as TikTok and the Internet of Things (IoT), could be a sign of the end of the default “I’ll Google it” mentality.

Already, almost 40% of Gen Z is using TikTok and Instagram, and with the ongoing antitrust scrutiny against Google, this shift could lead to a more diverse search landscape.

However, one of the most notable developments in this space is Google’s supercharged Search Generative Experience (SGE). This AI-powered feature holds the potential to deliver answers directly on the search page, potentially bypassing traditional websites.

This may pose some challenges for publishers and content creators, but it also has the potential to democratize access to information, making it easier for users to find what they need.

No cookies, no party

Google’s Privacy Sandbox hopes to create a better balance of ad targeting with user privacy, replacing the use of third-party cookies with new tools such as CHIPS and Topics API.

It’s been on the cards for a while and organizations should see it as a necessary shift towards less intrusive measures such as contextual advertising and better first-party-data strategies.

The effectiveness of this change is still up for debate and will no doubt continue across the year, with continuing concerns about Google’s potential power grab and user data collection.

However, this shift could also ignite an era of more creative advertising campaigns that are built on quality content and authentic engagement.

The post Tech predictions 2024: What we expect appeared first on SmartFrame.

Why is ethical advertising important?

Liam Machin — Tue, 19 Dec 2023 16:04:01 +0000

More than ever, ethical practices are a vital aspect of a brand’s reputation. But how does this apply to advertising?

People are increasingly becoming more aware of the implications of their lifestyle choices and the effects of their purchases. Therefore, brands need to be honest in their advertising and marketing efforts.

Effective advertising is hard to achieve:

How do you reach the right audience?
Which platforms are they on?
What type of message should you be promoting
Could this message be seen as misleading?

To maintain consumer trust, it is important to communicate honestly and ethically without exaggerating or misleading users about your product.

What is the most fundamental ethical issue in advertising?

The main ethical problem in advertising is dishonesty. Providing accurate information about a product or service is crucial as it helps consumers make informed choices based on facts rather than oversensationalization.

Misleading claims, false advertising, and deceptive tactics all harm consumer trust and can have long-lasting negative consequences for a brand.

Why are ethics important when advertising?

Ethical advertising is important because it builds trust with consumers and, assuming all organizations follow them, creates a fairer marketplace.

When brands are truthful and open, they are more likely to gain and retain customer loyalty. This also helps protect consumers from misleading or harmful advertising, benefiting society.

If all companies have the same ethics, over-the-top ads will only occur for specific reasons, such as satire.

Businesses that lie outright and claim their product can do something it cannot – without obvious signals of a joke – might gain an unfair advantage.

Even Red Bull was fined because its drink didn’t actually give you wings!

When businesses follow the same rules, it creates fair competition, which in turn promotes innovation and rewards businesses that satisfy consumer needs.

Ethical advertising is important as it allows for informed decision-making, protects against deception, and enables people to support businesses that align with their values.

What is an advertising code of ethics?

An advertising code of ethics is a set of rules created by a company or industry group. It ensures that businesses are honest and fair when promoting their product(s).

These ethics are often enforced through self-regulation, government oversight, independent agencies, and legal action.

The potential level of offense an ad creates will dictate the consequences. Advertisers usually have a process for complaints so consumers can report ads they consider to be problematic.

According to the Institute for Advertising Ethics (IAE), there are nine key ‘Principles for Ethical Advertising’.

These principles promote honesty and ethical standards in advertising, public relations, and marketing for the public’s benefit.

They stress the importance of personal ethics, transparency, fair treatment of consumers, and adherence to laws in creating commercial advertisements.

Additionally, the principles highlight the need for open discussions about ethical concerns within advertising teams and establishing trust through transparency in business relationships.

Other organizations, like the International Chamber of Commerce, have established codes of ethics, and offer online courses to promote responsible advertising practices globally.

What is greenwashing? And why is it a problem?

Greenwashing involves making false claims about the environmental friendliness of products in advertising. This makes it one form of unethical advertising.

These advertisements often mislead individuals striving to make more environmentally responsible choices and capitalize on the overall push for a greener world.

This issue with greenwashing is that it not only tricks customers, but also takes attention away from real efforts to help the environment.

Companies can easily make unsubstantiated claims, or withhold certain information completely, in a bid to make them appear more environmentally conscious.

The emotional appeal of eco-friendly messaging can cloud judgment, leading people to unknowingly support companies that may be contributing to environmental harm.

What challenges are faced when adopting ethical advertising principles?

Many think it’s important for companies to advertise transparently and ethically. So what challenges are there in doing so?

One major issue is the urgency to earn money quickly, especially with outside investment. This can cause businesses to prioritize profit over ethical decision-making.

Ethical marketing can be costly for companies because they have to bear expenses such as research, training, and advice, and such costs can be particularly challenging for companies with financial limitations.

What does ethical advertising look like?

A great example of ethical advertising is Patagonia’s “Don’t Buy This Jacket” campaign.

Widely known as a sustainability-first brand, instead of pushing people to rush into buying new clothes for Christmas, Patagonia encouraged its loyal customers to think about the environmental effects of their purchases.

This campaign matched the company’s dedication to sustainability and openness, connecting with its consumers who appreciate ethical practices.

It encouraged mindful buying habits, reducing unnecessary consumption and mitigating the environmental impact of the fashion industry.

Furthermore, the company’s willingness to challenge the idea of endless consumption is the exact opposite of “fast fashion” and many consumers appreciate the impact of such practices, as it prioritizes quality over quantity and mindful consumption over rash buying.

What does unethical advertising look like?

SmileDirectClub might not have gone out of business if it had been truthful about its product.

Over the years, the US firm had faced numerous allegations of making false and misleading claims about the effectiveness, safety, and affordability of its services.

In Australia, the Australian Competition and Consumer Commission (ACCC) fined SmileDirectClub AU $3.5 million for making false or misleading statements about its products and services between 2019 and 2020.

Similarly, in the United Kingdom, the BBC ran an investigation into SmileDirectClub that revealed the firm had been making false claims about the qualifications of its dentists and the compatibility of its aligners with various dental conditions.

Earlier in 2023, Align Technology sued SmileDirectClub for false advertising, alleging that the company was misrepresenting its products and services as the same as those offered by licensed orthodontists.

The many SmileDirectClub cases highlight the need for more oversight in online advertising, whether from a regulatory body or government.

Our interconnected online world has made it easier than ever to distribute advertisements and has created an environment in which false or misleading claims can reach a large audience quickly.

Without effective safeguards in place, consumers will always be at risk of being misled or even harmed by companies.

SmileDirectClub folded in December 2023, with reports estimating the firm held almost $900 million in debt.

What role will AI play in ethical advertising?

Artificial intelligence (AI) has revolutionized the advertising landscape in recent years, with more automation and more opportunities to offer targeted personalized ad experiences.

By analyzing consumer data, AI algorithms can identify patterns and preferences, allowing advertisers to tailor their messaging to specific individuals or groups.

This targeted approach increases the relevance of advertisements and enhances the overall consumer experience.

However, the further embedding of AI in advertising does raise concerns about privacy and data ethics, especially with general internet users; one report claims that 72% of American consumers worry about their data being used by an AI system in the future.

Striking a balance between innovation and ethical considerations is paramount in the age of AI-driven advertising.

Advertisers can harness the power of AI to create effective advertising campaigns while maintaining consumer trust and remaining ethical in their online campaigns by adopting responsible data practices, respecting user privacy, and prioritizing transparency.

Final thoughts

Ethical advertising is not just about being honest; it’s also about being fair and responsible.

Advertising messages and campaigns must be carefully crafted to avoid misinterpretation, as different individuals may perceive them in different ways.

Brands must diligently communicate to ensure they convey the intended message and avoid any potential misunderstanding or offense.

Working collaboratively and trusting your team to find the best balance between creativity and responsibility is paramount.

Ethical advertising is not just a moral obligation but a strategic necessity for long-term success in building a strong and sustainable brand reputation in the modern marketplace.

The post Why is ethical advertising important? appeared first on SmartFrame.

Copyright and AI images: What does the law say?

SmartFrame — Mon, 27 Mar 2023 13:26:41 +0000

We are used to thinking that copyright only applies to creations of the human mind. But images created with the use of AI aren’t wholly exempt from its protection. So what exactly does the law say?

Art created by artificial intelligence (AI) has exploded into the mainstream, thanks to a range of different platforms and apps such as OpenAI’s DALL·E 2, Stability AI’s Stable Diffusion, and Prisma’s Lensa AI.

Through a combination of machine learning, written prompts, and user-uploaded images, anyone can now quickly generate countless pictures and imitate particular artistic styles, from photorealism to illustrations and cartoons.

The question of how this flurry of AI-enabled digital art impacts image owners has not gone unanswered, with both individual creators and companies taking the matter to the courts.

One group of artists and illustrators has already filed a class-action complaint against Midjourney, Deviantart, and Stability AI. Media giant Getty Images, meanwhile, has sued the latter for copyright violations and unfair competition.

How does artificial intelligence generate artwork?

Most generative AI art models scrape existing images and text-to-image pairs off the internet, using machine learning to build associations between its data and the prompt to create new content.

For example, OpenAI’s DALL·E 2 was trained on “hundreds of millions of captioned images from the internet” while Stability AI’s Stable Diffusion was trained on 2.3 billion images.

But the technology has accelerated faster than the protections for the art they require to function, causing a copyright frenzy.

Some datasets, for example, have been found to include copyrighted images. And while the exact mechanisms behind how individual images are processed and weighted are unclear to most, research has revealed that image-generating models have been shown to copy the data on which it was trained.

Although OpenAI has sought to mitigate against what it calls image regurgitation by removing large quantities of visually similar images, this does not protect copyrighted images.

Moreover, this does not stop users from reproducing a certain style – as artists such as Hollie Mengert found out – ultimately putting creators at risk of devaluation of work or potentially a loss in commissions.

Copyright implications of AI-generated artwork: What you need to know

Each country will establish its own viewpoint as time goes on. But, at present, the UK is one of a handful of countries that protects computer-generated and AI-assisted works.

The law states that every work that expresses “original human creativity” benefits from copyright protection if it requires a relative amount of skill, labor, and creative judgment to create.

When considering AI-assisted work, these parameters could apply to AI-based options found inside a camera. Provided the photographer’s creativity is still evident, the photo will still be protected as an artistic work.

Similarly, the US protects the fruits of intellectual labor as long as they are both original and fixed in tangible form; ideas cannot be copyrighted until they have taken some kind of shape or form.

However, recent legal cases demonstrate the US Copyright Office only deems works of human authorship worthy of protection – although what constitutes “human authorship” is not always clear cut.

For example, computer scientist Stephen Thaler made multiple requests for AI-generated artworks and patents to receive copyright protection, and all were unsuccessful.

On the other hand, the copyright request for Kristina Kashtanova’s comic book Zarya of The Dawn, which featured artwork generated through Midjourney, was initially granted, before being rescinded and then reissued for the storyline and characters – but not for the images.

In Kashtanova’s case, the contention lay in defining what counts as “substantial human involvement.” Although the case has been brought to a close, the blurry line between human creation and authorship remains a site of conflict.

What constitutes fair use for AI-generated art?

Copyright exceptions are permitted in both the US and the UK. In many cases, a person or party is allowed to use a copyrighted work without the owner’s permission, as long as this use is limited to specific purposes such as news reporting, comment, criticism, research, scholarship, or teaching.

Still, there are no hard and fast rules when it comes to fair use, with situations usually considered on a case-by-case basis.

This depends on the nature of use, the nature of the copyrighted work, how much of the copyrighted work is used, and the effect on the market or value of the original work.

The UK government held a public consultation on copyright related to text and data mining (TDM) for AI between October 2021 and January 2022. It concluded that TDM constitutes a copyright infringement unless a legal copyright exception or permission is granted for its use. This exception already exists: TDM is permitted if it is limited to use for non-commercial research or educational purposes.

However, the UK government has indicated it might extend this exception to include TDM for any purpose, including for commercial use. In this event, rights holders’ content would still be protected by measures such as requirements for lawful access, which means they will be able to choose the platform on which their works are available and will be able to charge for access.

The US Copyright Office and the US Patent and Trademark Office also hosted a consultation on copyright law and machine learning in the age of AI in October 2021.

At present, human authorship is currently an essential requirement for copyright protection, but Director Shira Perlmutter sees cases becoming more complex as time goes on. The US Copyright Office recently announced addressing these legal gray areas would be a priority going into 2023.

Cases arguing the extent of “transformative” use are not new. In the case of Graham vs. Prince, where photographer Donald Graham sued artists Richard Prince for using an Instagram screenshot of one of his photos, Prince’s motion to dismiss the complaint was denied as the reproduced art did not fundamentally change the underlying “composition, presentation, scale, color palette, and media” of the photo.

With this new unprecedented scale of current technological abilities, data scraping, and the opacity of AI algorithms, such disputes are likely only to become more common.

Ethical considerations when AI tools meet the world of art

As with any new technology that has the potential to become embedded in our daily lives, there are ethical points to consider. Within the art world, these considerations range from the previously mentioned issues of authorship and ownership to questions of bias and discrimination.

According to an independent analysis of 12 million images from Stable Diffusion’s dataset (LAION-5B), 47% were sourced from only 100 domains, with the largest number of these taken from Pinterest.

While the sample analyzed only accounts for 0.5% of the 2.3 billion images that the model was first trained on, and 2% of the 600 million images used to train the most recent three checkpoints, the analysis revealed a handful of interesting things.

In general, user-generated content platforms, such as WordPress-hosted blogs, Smugmug, Blogspot, and Flickr, made up a huge proportion of the image data, as did shopping sites and stock image sites.

Many artists use online platforms like these to promote their work and connect with others. Visibility is a fundamental part of exposure, business, and sales, so artists hoping to protect their intellectual property by keeping their work off social media or behind a paywall may limit reach and networking opportunities.

The study also revealed that some of the images used are copyright protected – and it’s for this reason Getty Images is suing Stability AI. (Incidentally, the stock image company had previously banned the use of AI-generated images from its platform due to copyright concerns.)

On top of that, Sydney-based artist Kim Leutwyler said she saw “almost every portrait” she had ever shared on the internet used to train popular AI models through the search engine Have I Been Trained, which allows people to discover whether their work has been used in datasets.

Another artist used the site to discover their private medical records in LAION-5B, begging the question of how much other personal data might have been included in these large-scale datasets.

Exploring the intersection of copyright and AI-generated art

The absence of a clearly defined legal framework, coupled with opaque data collection and processing methods, leaves many questions open to ethical interpretation.

An artistic style cannot be granted copyright protection. But style sets individual creators apart – especially considering that it takes years to develop a craft and hone a skill.

It’s easy for everyday users to experiment with digital images due to the open-source nature and affordability of different models, which complicates things further.

When a specific style is used as a prompt, it can easily cause financial, professional, and reputational damage. Search results might be distorted by AI illustrations, images that may go against that artist’s brand or values. Such automated generation also invalidates the practice, passion, and purpose of artists who have cultivated their abilities for years.

There is a level of creativity and originality in the selection of images, the formulation of textual prompts, post-production, and editing, that lead to the generation of an AI image. Certainly, the creation of new art depends on existing art; as creators develop, they draw on inspiration, reformulating and transforming it into something new.

However, feeding images into an AI model blurs the line between inspiration and appropriation. The same process might be happening in principle, but without the time, learned skill, and unique voice of the artist.

In the future, cases may be decided on the level of contribution of each individual component (original art, user, training data, AI) but it’s easy to see how scraping someone else’s work to generate something “new” before claiming authorship (and copyright) over it can constitute an ethical violation.

Pros and cons: Do the benefits of AI-generated art outweigh the pitfalls?

Andrey Usoltsev, founder of Prisma Labs, the company behind Lensa AI, believes this “democratization of access” is a breakthrough, promising the company would focus on steering “the use of such technology in a safe and ethical way.”

While it’s true that the tool may be helpful for visualizing screenplay or novel scenes, or for generating reference images, the lack of privacy or compensation inherent to such models must be considered.

As we have seen, the safety and ethics of these tools are already contested. There is also the privacy angle to consider; where activists have long argued against the non-consensual use of personal data by large social tech platforms, people are currently training AI models for free, using both their own and other people’s data.

Artists and users currently have little legal recourse or the means to enforce data restriction measures on the images and information used for AI models. The outcome of a number of ongoing lawsuits, however, may provide more insight into how these cases will proceed.

Where exactly this all goes from here is unclear. But one thing is certain: as technology, AI, intellectual property, and copyright laws continue to intersect in increasingly complex ways, individuals, tech companies, and publishers alike will have to pay closer attention to the data and images they use.

The post Copyright and AI images: What does the law say? appeared first on SmartFrame.

Are AI tools a threat to creativity?

SmartFrame — Thu, 09 Feb 2023 11:12:30 +0000

When used for automation, AI tools can bring myriad benefits. But between ChatGPT and generative AI image-creation tools, creatives may now be fearing for their livelihoods. Should they be?

It’s been 25 years since a computer first beat the world chess champion at his own game. Today, news of the abilities of AI-powered tools floods our feeds.

Some businesses already plan to use the tech to enhance their offerings. Others, meanwhile, lament its destabilizing potential.

The excitement for, and trust in, its revolutionary capabilities have truly taken root, epitomized by Microsoft’s recent extension of a multiyear, multi-billion-dollar investment in OpenAI.

With chatbot capabilities extending to essay writing and more, many in the creative world consider themselves to be under threat. But can AI truly replace creativity? Are these tools actually generating something new? Or are they just spitting out formulas?

The inner workings of AI technology

It can be difficult to keep track of the ever-growing list of AI models available. To keep things simple, we’ll be focusing on ChatGPT and GPT-3.5.

First, it’s important to differentiate between AI models, chatbots, and specific applications. GPT-3.5 (Generative Pre-trained Transformer) is a large language model, also called an autoregressive language processing model. ‘Autoregressive’ refers to the ability to predict future behavior based on past behavior, working with probability to generate one element, or word, at a time.

With 175 billion parameters, GPT-3.5 is the most recent and powerful AI model out there. ChatGPT is a chatbot, an artificially intelligent conversation simulator that uses this same deep-learning model to respond to requests and follow prompts.

How machine learning can transform business practices

In business, AI tools can collect and process massive amounts of data. They can then use this information to help with problem-solving, pattern recognition, recommendations, or predictions – at a faster rate than any human could – making data-driven, informed decisions. This is called decision intelligence.

AI could improve healthcare and agriculture, as well as business and services. One practical example of this could see AI recording every single interaction on a popular website page to catch any user experience glitches or issues.

Other use cases could involve personalizing and refining online customer service and chatbot applications, condensing long and complex texts, or improving search queries – perhaps even rivaling Google, which has responded to the competitive pressure by launching its own chatbot, Bard.

ChatGPT’s competency has led some to believe that it might oust creatives from their professions. The existence of Jasper.ai, an AI content platform specifically designed for copywriting, seems to add weight to this argument.

The limits of AI solutions

Like all AI deep-learning models, these machines require data to learn and generate something new. One obvious area of scrutiny, therefore, is the nature of the data that is input.

We have discussed in another post how AI models can reproduce, and therefore entrench, systemic and societal bias. We can see examples of this happening with previous AI chatbot iterations, such as Microsoft’s bot Tay and Meta’s Blenderbot 3, which have fallen victim to users purposefully teaching them sexist, racist, and antisemitic rhetoric and false information.

In the case of OpenAI’s ChatGPT, the company has tried to mitigate against such developments with a content moderation tool, one that’s designed to flag and block unsafe and illegal information.

However, the paths to circumventing its filters are varied – and, perhaps more importantly, frequently successful. Users can, for example, easily trick the bot into explaining how to hotwire a car.

Another issue arises with the age of data. At present, most AI models are trained on a specific, finite data set, which means that new, real-time developments are not included. For example, ChatGPT’s current training data ends in 2021 – and incomplete data sets lead to incomplete and skewed results.

AI can also not discern right from wrong. While it may be trained on a wide variety of data, there is no distinguishing between accurate or inaccurate information. The risk here is that such a machine could very easily and very quickly spread misinformation that sounds completely plausible and convincing.

Ideally, AI output should be independently verified and cross-checked with reliable sources – but there is a chance those using it may not do so.

An artificial threat to real-world creativity?

In 2021, an AI model was trained to finish Beethoven’s last symphony, albeit with human expert support. More recently, the mainstream availability of ChatGPT has shown the world that it is capable of language translation, understanding complex topics such as quantum mechanics, and even more creative endeavors such as writing poems, scripts, generating code, and more.

Unsurprisingly, questions abound about whether or not these AI models will ultimately subvert the creative realm.

Let’s consider an op-ed written by GPT-3 for the Guardian back in 2020. Upon reading, it is clear that AI can competently put together ideas and sentences that make sense when considered as single paragraphs.

However, the piece lacks a coherent narrative overall. The ideas are erratic and disjointed, and while they could be written by a person, it isn’t good writing. If individual sentences sound familiar, it’s because they are – they are patterns that have been rehashed, reiterated, and repeated countless times across the internet.

Of course, this technology has evolved since 2020. More recently, a number of writers and journalists used ChatGPT to write convincing introductory paragraphs for their articles. It was also used to fool a recruitment team.

In an experiment, John Warner – writer, educator, and author on writing – through repeated prompts, guides ChatGPT to write a moderately good piece of creative writing. As with Beethoven’s symphony, it is through human guidance, tailored prompts, and emulating particular creative styles that the AI succeeds in sounding more human.

When it comes to formulaic texts, ChatGPT thrives. That is what it has been taught to do: to recognize patterns, word associations, and syntax structures. In that respect, AI can easily replicate an essay – a phenomenon that is making waves in academic circles.

This does pose some questions about the ways we teach, what we might expect from, and how we value creative writing and creativity.

The fact that ChatGPT can complete such tasks could be more of a condemnation of standardization than a risk to creativity. Warner points out that, more often than not, students (and perhaps, others working in writing professions) are “rewarded for… regurgitating existing information” in a system that “privilege[s] surface-level correctness” rather than “develop[ing] their writing and critical thinking skills” – and, perhaps, this is the issue.

The future of AI in the creative world

AI models like ChatGPT can, through human expert direction, produce acceptable, and even quality work. At present, they cannot convey meaning the way a human can, because they do not understand meaning; these are machines that understand symbols and patterns.

There is, of course, the opportunity to use such AI models as tools, or toys, to aid the teaching and endeavor of writing. As the integration of AI in general business and mainstream practices edges closer, this does raise challenges and concerns relating to copyright and plagiarism. This process is already occurring in the art world, where the widespread adoption of Lensa AI to generate images has triggered wider conversations about stricter copyright laws.

How will this pan out? It depends – at least in part – on how our societies evolve in response to this new technology. Progress cannot be undone, but creative and legal forces may come together to restrict the use of these tools. Some workers end up being supplanted by AI models, of course. But in the end, ChatGPT is only truly a threat to creativity if the value of quantity supersedes that of quality and originality.

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