Advertising in AI Overviews: Ethical Concerns in the Age of Machine-Generated "Truth"

The digital information landscape is undergoing a profound transformation, with artificial intelligence (AI) playing an increasingly central role in shaping how people access and interpret knowledge. One of the most significant developments in this space is the rise of AI-generated content summaries, known as "AI Overviews," which are becoming a staple in modern search engines. These summaries promise users quick, concise, and seemingly authoritative answers, often eliminating the need to visit external websites. However, as the technology matures and commercial interests begin to intersect with AI-driven information delivery, new ethical questions are surfacing. The embedding of advertising within AI Overviews raises concerns about the objectivity, transparency, and integrity of machine-generated “truth,” and compels a thorough examination of how these systems operate and influence public trust.

What Are AI Overviews and How Do They Work?

Definition of AI Overviews

• AI Overviews are automated summaries generated in response to user queries, appearing in search engines or digital assistants.

• Google’s AI Overviews are a prominent example, using advanced language models to deliver instant, natural language summaries.

• The AI gathers and synthesises information from a wide range of web sources before presenting a unified answer.

• These summaries are designed to be concise, authoritative, and user-friendly, often appearing above traditional search results.

• The complexity and selectivity of the AI’s source aggregation is largely hidden from users.

AI Overviews are automatically generated content summaries that appear in response to user queries, usually within search engines or digital assistants. These overviews aim to provide concise, relevant answers by synthesising information from numerous online sources. Google’s AI Overviews, formerly known as the Search Generative Experience (SGE), represent a high-profile example of this technology, harnessing powerful machine learning models to deliver instant summaries at the top of search results. The process typically involves the AI scouring its indexed web sources, weighing factors such as relevance, recency, and authority, and then constructing a natural language response. This seamless delivery of information, while efficient, masks the complexity and selectivity of the underlying data aggregation and summarisation.

The Sources AI Uses to Generate These Summaries

• AI Overviews pull data from reputable news sites, academic publications, commercial websites, forums, and user-generated content.

• The selection criteria for these sources are not always transparent or consistent.

• Biases, inaccuracies, or commercially driven content can inadvertently be included in AI summaries.

• There is a risk that AI may blend factual reporting, opinions, and advertising into a single authoritative-sounding answer.

• Users may be unaware when summaries are influenced by lower-quality or self-promotional sources.

The sources feeding AI Overviews are vast and varied, encompassing everything from reputable news outlets and academic journals to forums, commercial websites, and user-generated content. While AI models strive to prioritise authoritative and reliable information, the criteria and algorithms governing this process are not always transparent. Consequently, summaries may inadvertently reflect the biases or inaccuracies present in their source material, particularly if low-quality or commercially motivated sites are heavily indexed. The aggregation process also increases the risk of conflating opinion, advertising, and factual reporting into a single, authoritative-sounding answer. As a result, users may receive responses that lack nuance or critical context, potentially distorting their understanding of complex topics.

How Users Typically Engage with AI Overviews vs Traditional Search Results

• Traditionally, users review a list of links and select sources to read, forming their own conclusions.

• With AI Overviews, users receive direct, consolidated answers and may not investigate the original sources.

• This shift encourages passive information consumption, with increased reliance on the platform’s judgement.

• Users are more likely to accept AI Overviews as complete and accurate, rather than critically evaluating the information.

• The opportunity for misinformation or subtle manipulation to go unnoticed is greatly increased.

The way users engage with AI Overviews differs markedly from traditional search interactions. In the past, users were presented with a list of links and had to sift through sources, compare perspectives, and form their own conclusions. With AI Overviews, users are more likely to accept the summary at face value, often without clicking through to examine the underlying sources. This shift encourages a more passive form of information consumption, placing greater trust in the AI's judgement and the platform's authority. While this can increase efficiency, it also heightens the potential for misinformation or manipulation to go unnoticed.

The Perception of Objectivity and Trust in AI-Generated Content

• AI Overviews are viewed as objective, authoritative, and neutral due to their machine-generated and platform-backed nature.

• Users often assume the content is fact-based and impartial, trusting the technology’s “judgement.”

• The reality is that summaries reflect the data, algorithms, and biases present in their sources and in their training.

• When advertising is mixed into these answers, users may not distinguish between genuine information and paid content.

• The risk of misleading users and undermining long-term trust in digital platforms is significant.

One of the most significant shifts brought about by AI Overviews is the perception of objectivity and trustworthiness in machine-generated content. Because these summaries are produced and delivered by trusted platforms such as Google, users are inclined to view them as neutral and fact-based. The aura of technological impartiality, combined with the authoritative presentation, can obscure the fact that AI-generated answers are shaped by the quality and bias of their underlying data. This perception of "machine truth" makes the integration of advertising within these responses especially fraught, as users may struggle to distinguish between genuine information and paid promotion. The risk is not only that users are misled, but also that their confidence in the objectivity of search platforms is gradually undermined.

Advertising in AI Overviews: How It Works

How Ads Are or Could Be Embedded into AI-Generated Content

• AI Overviews can incorporate advertising directly within the generated summary, rather than as separate banners or links.

• This includes blending paid promotions with organic answers, making it harder for users to differentiate between the two.

• Ads may be integrated in real time, based on the specific query and context of the search.

• The merging of ads and information creates a seamless, but potentially misleading, experience for users.

• Commercial content is presented at the exact moment a user is seeking information, maximising advertiser impact.

The integration of advertising into AI Overviews represents a fundamental shift in how commercial content is delivered alongside—or within—informational responses. Instead of confining ads to clearly marked banners or sponsored links, AI-generated systems can weave promotional material directly into the flow of the summary itself. This approach allows for new formats such as promoted snippets, where a paid product or service appears as part of the AI’s answer, or inline sponsored content, where advertising is embedded seamlessly within the response. The effect is a blurring of boundaries between organic and commercial content, making it more difficult for users to discern where information ends and advertising begins. Such integration is attractive to advertisers for its potential to reach users at the exact moment of intent, but it also introduces significant ethical and practical challenges.

Possible Formats: Promoted Snippets, Inline Sponsored Content, Etc.

• Promoted snippets: Paid answers that appear as the top or main response to a user’s query.

• Inline sponsored content: Advertising embedded within the text of the AI summary itself.

• Contextual product or service recommendations: Suggestions placed within or alongside organic content.

• Sponsored solutions: AI may highlight a paid service or product as an “expert” answer.

• New ad formats increase relevance and visibility but risk eroding transparency.

Various formats for advertising within AI Overviews are already being considered and, in some cases, piloted. Promoted snippets may present a product or service as the top solution to a user's question, potentially bypassing organic recommendations entirely. Inline sponsored content might appear as a recommended resource or product within a broader summary, integrated so smoothly that users may not immediately recognise it as advertising. Additionally, contextual product recommendations can be tailored to the specific nature of the user’s query, providing highly targeted suggestions that feel natural within the AI’s response. These new ad formats offer unprecedented relevance and engagement but also risk eroding the transparency that has traditionally defined sponsored content online.

Targeting Mechanisms and How Advertisers Select Keywords or Intent

• Advertisers can target ads in AI Overviews by specifying keywords, search intent, or user demographics.

• AI’s ability to understand nuanced context enables hyper-targeted advertising matched to user queries.

• Commercial messages can be delivered at the moment of high intent or curiosity.

• Personalised ads leverage user data and browsing history for maximum impact.

• The advanced targeting capabilities raise concerns about manipulation and user autonomy.

Targeting mechanisms for these AI-driven ads build upon, and in many cases surpass, those used in traditional search advertising. Advertisers can specify keywords, user intents, demographics, and even behavioural patterns to ensure their messages reach the most receptive audience. Because AI systems are capable of parsing nuanced context and intent, they can present ads that are not only relevant to the query but also tailored to the user's presumed needs or preferences. This level of personalisation increases the effectiveness of advertising but also amplifies the potential for subtle manipulation, especially when users are unaware that their informational environment is being shaped by commercial interests. The ability to deliver ads at the precise intersection of curiosity and intent is both a marketing dream and an ethical minefield.

The Ethical Dilemma

User Trust vs. Advertiser Influence

• Users trust AI Overviews to be factual and unbiased, but ad integration undermines this trust.

• When commercial interests dictate what appears in authoritative summaries, users may be misled.

• Decisions may be made based on paid promotions rather than objective merit.

• The blending of facts and ads erodes the credibility of both AI and the platforms delivering it.

• Maintaining user trust is essential for the long-term viability of AI-generated information.

The core ethical dilemma at the heart of advertising in AI Overviews is the tension between user trust and advertiser influence. When users encounter an AI-generated summary, they are predisposed to view it as factual, unbiased, and free from external influence. However, if advertisers can pay to insert their content into these trusted spaces, the objectivity of the information is immediately called into question. Users may unwittingly accept commercial claims as part of the "truth" presented by the AI, making decisions based on marketing rather than merit. This blending of fact and promotion undermines the foundational trust that makes AI Overviews so powerful and potentially useful.

Transparency: How Clearly Are Ads Labeled in AI Overviews?

• Effective transparency requires that ads in AI Overviews are clearly and consistently labelled.

• Poor labelling can cause users to mistake paid content for unbiased information.

• The language and presentation of AI can make ads appear as part of objective responses.

• Without obvious disclosure, users may only realise content is promotional after acting on it.

• Lack of transparency can permanently damage trust in both the technology and the brand.

Transparency is a further point of concern, particularly regarding how ads are disclosed within AI Overviews. If advertising is not clearly and conspicuously labelled, users may not realise that the information they are reading is paid content. This lack of transparency can be especially problematic when AI systems produce language that mimics the tone and style of unbiased information, blurring distinctions between editorial content and marketing. Users may only discover the promotional nature of a snippet after making a purchase or following a recommendation, by which time their trust in both the platform and the information may be irreparably damaged. Ensuring that users can easily differentiate between organic and sponsored content is therefore not just a technical challenge, but a moral imperative.

Bias Amplification: Can Advertisers Manipulate AI Responses?

• Advertisers can saturate the web with optimised content to influence both organic and sponsored results in AI Overviews.

• Well-funded interests are more likely to dominate AI summaries, overshadowing independent or minority voices.

• The ecosystem becomes skewed toward those who can pay for visibility, narrowing perspectives.

• AI may reinforce commercial or ideological biases by favouring the most prevalent or well-promoted content.

• The risk of echo chambers and reduced information diversity increases.

Another pressing issue is the potential for bias amplification, as advertisers attempt to influence AI Overviews by saturating the web with content optimised for both organic inclusion and paid promotion. Well-resourced companies can invest heavily in SEO and content marketing, increasing the likelihood that their viewpoints are represented in AI summaries regardless of their intrinsic value. This dynamic risks crowding out minority perspectives, independent voices, or more accurate but less profitable information. Over time, the information ecosystem may become increasingly skewed towards those able to pay for visibility, distorting public discourse and narrowing the range of available knowledge. The result is not just biased advertising, but a more homogenous and commercially-driven understanding of the world.

Content Integrity: The Risks of Harmful or Misleading Promotions

• Harmful or low-quality products may be promoted through authoritative AI summaries.

• In sensitive areas (health, finance, politics), misleading ads can have serious consequences.

• AI-generated content is often presented definitively, increasing user trust in bad advice or products.

• Inadequate vetting allows questionable promotions to appear as “trusted” recommendations.

• Platforms have an increased ethical responsibility to prevent harm from poorly vetted ads.

Content integrity is also at stake when misleading, harmful, or low-quality products are promoted through AI-generated summaries. Unlike traditional advertising, where disclaimers or user reviews may be more apparent, AI Overviews often present information in a definitive, authoritative manner. If a questionable product is recommended by the AI—whether due to inadequate vetting or intentional advertiser manipulation—users may be more likely to trust and act upon that recommendation. This is particularly concerning in sensitive areas such as health, finance, or politics, where the consequences of misinformation can be severe. The ethical responsibility of platforms to vet and monitor advertised content in AI Overviews is therefore significantly greater than in conventional ad placements.

Regulation and Responsibility

What Current Ad Guidelines Exist for Traditional Search vs. AI-Generated Summaries?

• Traditional search advertising requires disclosure of sponsorship, accuracy checks, and avoidance of harmful products.

• AI-generated summaries blur the line between editorial and commercial content.

• Existing regulations may not sufficiently address the complexity of AI content aggregation.

• New guidelines specific to AI Overviews are needed to ensure transparency and user protection.

• The unique risks of AI-driven advertising require updated oversight mechanisms.

Current advertising guidelines for search engines typically require clear disclosure of sponsored content, adherence to truth-in-advertising laws, and basic checks for harmful or illegal products. However, the blending of advertising into AI-generated summaries presents new regulatory challenges, as the line between editorial and commercial content becomes less distinct. Traditional frameworks may not account for the complexity and opacity of algorithmically curated and presented information. As a result, there is a growing recognition that AI Overviews demand their own set of rules and oversight mechanisms. This includes not only advertising standards but also broader considerations around data usage, user consent, and algorithmic accountability.

The Role of Google (or Other Platforms) in Ensuring Ethical AI Ad Practices

• Platforms like Google bear primary responsibility for setting ethical standards for AI Overview ads.

• They must establish robust labelling, content vetting, and monitoring systems.

• Ongoing risk assessment is required to prevent exploitation or unintended harm.

• User education about how AI Overviews and advertising operate is crucial.

• Proactive communication and enforcement of standards build and preserve public trust.

The responsibility for ensuring ethical AI advertising practices rests primarily with the platforms that develop and deploy these systems. Companies like Google must set robust internal standards for ad labelling, content vetting, and the prevention of harmful or misleading promotions within AI Overviews. They must also be proactive in monitoring for emerging risks, including the unintended consequences of bias or the exploitation of vulnerabilities by bad actors. At the same time, platforms have an obligation to educate users about how AI Overviews are generated and how advertising is incorporated. Only with clear communication and consistent enforcement can trust in these new technologies be maintained.

Should AI Overviews Be Held to Higher Standards Than Traditional Ad Placements?

• AI Overviews carry greater influence and perceived authority than traditional ads.

• The merging of advertising and information magnifies the consequences of bias or error.

• Stricter disclosure, vetting, and third-party auditing should be mandatory.

• Platforms should provide documentation on how both ads and organic content are chosen.

• Higher ethical standards help maintain integrity and user confidence.

Many experts argue that AI Overviews should be held to higher standards than traditional ad placements, given their heightened influence and perceived authority. The seamless presentation of information and advertising in a single summary magnifies the impact of any errors or biases, making robust safeguards essential. Higher standards might include more rigorous requirements for disclosure, greater scrutiny of advertised content, and ongoing auditing by independent third parties. Platforms should also be required to provide clear documentation of how ads and organic responses are selected and prioritised. In this way, the integrity of both the informational and commercial aspects of AI Overviews can be preserved.

The Role of Regulation and Policymakers

• Regulators should update laws and standards to address AI Overview advertising.

• Collaboration between platforms, consumer advocates, and researchers is essential.

• Comprehensive frameworks should emphasise transparency, accountability, and user protection.

• Regulation must adapt to new risks and evolving technology.

• Ongoing dialogue ensures the ethical future of AI-generated information and ads.

Regulators and policymakers also have a crucial role to play in establishing clear guidelines and enforcement mechanisms for AI-generated advertising. Existing laws and industry standards should be updated to address the unique challenges posed by AI Overviews, including the need for transparency, accountability, and user protection. Regulatory bodies should work in partnership with technology companies, consumer advocates, and independent researchers to develop comprehensive frameworks. The goal must be to balance innovation with responsibility, ensuring that the benefits of AI-driven search are not outweighed by the risks of manipulation or harm. Ultimately, the ethical future of AI Overviews will depend on the willingness of all stakeholders to engage in ongoing dialogue and reform.

Recommendations for Ethical AI Advertising

Clear Ad Labelling and Separation from Organic AI Responses

• Ads in AI Overviews must be clearly, unmistakably labelled and visually distinct.

• Labelling standards should be consistent across devices and platforms.

• Users must always be able to tell the difference between ads and organic content.

• Platforms should explain why and how certain ads are included in Overviews.

• Clear separation preserves user trust and helps prevent manipulation.

To address the complex challenges outlined above, a multi-pronged approach to ethical AI advertising is necessary. Firstly, all ads included in AI Overviews should be unmistakably labelled and visually separated from organic responses, leaving no room for confusion. Platforms should implement clear and consistent labelling standards that persist across devices and formats, ensuring that users can always identify paid content. This transparency must extend beyond mere disclosure, encompassing detailed explanations of how and why certain ads were selected for inclusion. Users should be able to access information about the sources, algorithms, and criteria involved in the creation of both ads and AI-generated summaries.

Transparency on How Content Was Chosen and Which Sources Were Used

• Platforms should provide transparency about source selection and algorithmic criteria.

• Users deserve access to information about the origins of both paid and organic content.

• Transparency helps ensure that the informational environment is not unduly shaped by commercial interests.

• Regular reporting on content selection processes builds public confidence.

• Source disclosure encourages accountability from both advertisers and AI platforms.

Stronger vetting processes for advertised content are also essential, especially in sensitive fields such as healthcare, finance, and politics. Platforms should require advertisers to provide evidence of product claims, adhere to strict standards for accuracy, and refrain from promoting misleading or harmful material. Independent audits and third-party oversight can provide an additional layer of accountability, ensuring that AI Overview ad systems are regularly evaluated for compliance and effectiveness. Consumer feedback mechanisms should also be implemented, allowing users to report problematic ads and flag potential risks. Transparency regarding the source and selection process helps ensure that both AI and paid content maintain a standard of quality and trustworthiness.

Stronger Vetting of Advertised Content in Sensitive Areas

• Strict vetting is necessary for ads relating to health, finance, or politics.

• Platforms should require documentation and evidence for all claims in sensitive domains.

• Harmful, misleading, or controversial promotions should be proactively excluded.

• Independent audits should evaluate compliance and flag emerging risks.

• Subject matter experts may be needed to review high-impact or sensitive advertising.

Another key recommendation is the adoption of independent and transparent auditing practices. By opening up AI Overview ad systems to regular inspection by qualified external bodies, platforms can demonstrate their commitment to ethical standards and identify areas for improvement. Audits should assess not only the accuracy of ad labelling and content selection but also the broader impacts on user trust, diversity of viewpoints, and the prevention of bias amplification. These findings should be published and accessible, fostering greater public awareness and informed debate. In particularly sensitive domains, advertising vetting should go beyond the basics, involving subject matter experts where appropriate.

Third-Party Oversight or Independent Auditing of AI Overview Ad Systems

• Independent bodies should regularly audit AI Overview ad systems for ethics and compliance.

• Audits should assess transparency, diversity of viewpoints, and prevention of bias.

• Findings should be published and accessible to the public.

• Oversight ensures that ethical standards adapt to new technological developments.

• Collaborative governance fosters greater accountability and trust.

Finally, platforms and policymakers should work together to establish clear, enforceable guidelines that reflect the unique risks and opportunities of AI-generated advertising. Ongoing research, stakeholder engagement, and adaptive regulation will be necessary as the technology evolves. The priority must always be to protect users from harm while preserving the informational and commercial value that AI Overviews can provide. Only through a concerted, transparent, and collaborative effort can ethical standards keep pace with technological innovation. Third-party oversight and independent auditing will help ensure that standards remain rigorous and responsive to emerging challenges.

Final verdict

The advent of advertising in AI Overviews signals a profound shift in how information and commercial messages are delivered to users. As these systems become more prevalent and influential, the ethical stakes grow ever higher, demanding new approaches to transparency, accountability, and user protection. The blending of machine-generated "truth" with paid content has the potential to undermine trust, distort public understanding, and expose users to subtle forms of manipulation. To navigate this new landscape responsibly, platforms, regulators, and advertisers must work together to uphold the highest ethical standards, ensuring that the promise of AI-powered information is realised without sacrificing the public good. The future of search—and, by extension, the integrity of the digital public sphere—depends on getting this balance right.