{"id":4236,"date":"2026-08-13T05:30:00","date_gmt":"2026-08-13T05:30:00","guid":{"rendered":"https:\/\/naaia.ai\/?p=4236"},"modified":"2026-08-12T15:53:54","modified_gmt":"2026-08-12T15:53:54","slug":"dark-patterns-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/naaia.ai\/en\/dark-patterns-artificial-intelligence\/","title":{"rendered":"Dark Patterns and Artificial Intelligence: Understanding the Emerging Risks of Algorithmic Influence\u00a0"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Artificial intelligence is becoming a core&nbsp;component&nbsp;of digital services.&nbsp;Recommendation systems organise access to information, conversational agents&nbsp;assist&nbsp;users in everyday tasks, and AI systems are increasingly involved in purchasing,&nbsp;search&nbsp;and decision-making processes.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As these technologies evolve, growing attention has been devoted to&nbsp;<strong>dark patterns<\/strong>. Originally associated with deceptive interface design, dark patterns refer to design choices that steer users towards specific actions or decisions, often by taking advantage of behavioural biases, information&nbsp;asymmetries&nbsp;or cognitive limitations.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">While the concept&nbsp;emerged&nbsp;long before generative AI, the development of AI systems has broadened its scope. Influence is no longer limited to interface elements such as buttons,&nbsp;menus&nbsp;or website architecture. Recommendation algorithms, conversational agents, AI&nbsp;assistants&nbsp;and autonomous agents can shape behaviour through the generation,&nbsp;prioritisation&nbsp;and personalisation of information. As a result, dark patterns are increasingly discussed in the context of AI governance, consumer protection, data&nbsp;protection&nbsp;and digital regulation.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>I- The Origins of Dark Patterns<\/strong>\u00a0<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>A concept rooted in user experience design<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The term&nbsp;<em>dark pattern<\/em>&nbsp;was&nbsp;<a href=\"https:\/\/90percentofeverything.com\/2010\/07\/08\/dark-patterns-dirty-tricks-designers-use-to-make-people-do-stuff\/\" target=\"_blank\" rel=\"noreferrer noopener\">introduced in 2010<\/a>&nbsp;by user experience designer Harry Brignull to describe digital interfaces intentionally designed to influence user behaviour in ways that primarily&nbsp;benefit&nbsp;a service provider.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The concept&nbsp;emerged&nbsp;in response to recurring practices&nbsp;observed&nbsp;across websites, online&nbsp;platforms&nbsp;and subscription services. In many cases, users could easily subscribe to a service, accept extensive data&nbsp;collection&nbsp;or complete a purchase, while alternative choices were deliberately made less visible or more difficult to access.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Several categories of dark patterns became particularly widespread:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Hard to cancel<\/strong>, where subscribing requires only a few clicks while cancelling a subscription involves multiple steps or hidden settings;\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Preselection<\/strong>, where consent options or\u00a0additional\u00a0services are activated by default;\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Hidden costs<\/strong>, where\u00a0additional\u00a0fees only appear at the final stage of a transaction;\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Forced action<\/strong>, where users must create an account, share personal data or accept\u00a0additional\u00a0conditions to access a service;\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Fake urgency<\/strong>\u00a0and\u00a0<strong>fake scarcity<\/strong>, where users are pressured into acting quickly through countdown timers or claims of limited availability;\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Obstruction<\/strong>, where\u00a0important information, privacy settings or account deletion options are intentionally difficult to find.\u00a0<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">These practices&nbsp;demonstrated&nbsp;that digital design&nbsp;could&nbsp;influence decisions just as effectively as traditional advertising or marketing techniques, highlighting that&nbsp;the architecture of digital environments can shape user behaviour in ways that are not always visible to users.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>II- How AI Changes the Nature of Dark Patterns<\/strong>\u00a0<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Historically, dark patterns were primarily embedded within graphical interfaces, where influence was exerted through visual hierarchies, default&nbsp;settings&nbsp;or navigation paths.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The growing use of AI systems extends these mechanisms beyond the interface itself. Recommendation algorithms&nbsp;determine&nbsp;which information users&nbsp;encounter&nbsp;first, search and ranking systems influence visibility, and conversational agents generate responses that adapt to the context and&nbsp;previous&nbsp;interactions.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A key difference is the ability of AI systems to personalise interactions at scale. Recommendations,&nbsp;explanations&nbsp;and prompts can vary according to behavioural patterns, inferred preferences or interaction history, meaning that different users may experience the same system in&nbsp;very different&nbsp;ways. Unlike traditional dark patterns, which are&nbsp;generally visible&nbsp;and identical for all users, AI-enabled influence can be dynamic,&nbsp;adaptive&nbsp;and personalised.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As a result, discussions surrounding dark patterns increasingly focus not only on interface design, but also on recommendation systems, conversational AI, personalisation&nbsp;mechanisms&nbsp;and autonomous agents.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>III- Main Categories of Dark Patterns in AI Systems<\/strong>\u00a0<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Data and Memory Exploitation<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">AI systems increasingly process information relating to users&#8217; habits, preferences, professional activities, health&nbsp;concerns&nbsp;and personal relationships. Because conversational systems rely on natural interactions, users may&nbsp;disclose&nbsp;significantly more information than they would in a traditional online form or search engine.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In this context,&nbsp;dark patterns may include extensive data retention by default, repeated requests for&nbsp;additional&nbsp;personal information to &#8220;improve&#8221; personalisation, limited visibility&nbsp;regarding&nbsp;how data will be used, or mechanisms that make deleting conversation histories and stored data more difficult. Unlike traditional websites, AI systems can also&nbsp;accumulate information over time through memory functions, creating increasingly detailed user profiles.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Informationally Misleading Design<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A second category concerns the way AI systems present information and&nbsp;represent&nbsp;their own capabilities.&nbsp;Conversational systems often communicate in a confident and authoritative manner,&nbsp;which&nbsp;can lead&nbsp;users&nbsp;to&nbsp;overestimate their&nbsp;reliability,&nbsp;expertise&nbsp;or understanding.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Misleading design may take several forms. A system may overstate its abilities, create unrealistic impressions\u00a0regarding\u00a0emotional understanding, present inaccurate information with confidence or selectively emphasise\u00a0particular perspectives\u00a0while omitting alternatives. These issues are particularly important in contexts where users rely on AI systems for information,\u00a0advice\u00a0or guidance.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>User Autonomy Compromised for Engagement<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Many digital services optimise for engagement and&nbsp;retention&nbsp;and similar dynamics can&nbsp;emerge&nbsp;in AI systems.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Conversational agents can encourage prolonged interactions through follow-up questions, suggestions for next steps, prompts to continue the discussion or features intended to increase user engagement. Some systems incorporate gamification mechanisms, usage streaks or reward structures designed to encourage repeated interactions.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">While these features may enhance user experience, they can also make it more difficult for users to disengage from a system when they intended to stop using it.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>False Social and Emotional Connection<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Many AI systems are designed to communicate in natural language, remember&nbsp;previous&nbsp;conversations&nbsp;and&nbsp;maintain&nbsp;continuity across interactions. Some simulate empathy, emotional&nbsp;support&nbsp;or companionship, while others are positioned as coaches,&nbsp;advisors&nbsp;or companions.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These characteristics can strengthen trust and engagement, but they may also encourage users to attribute human qualities to systems that&nbsp;remain&nbsp;statistical models. Examples include simulated emotions, role-playing&nbsp;behaviours&nbsp;or&nbsp;<em>sycophancy<\/em>, where a system systematically agrees with or&nbsp;validates&nbsp;the user&#8217;s opinions rather than providing balanced or&nbsp;accurate&nbsp;responses.&nbsp;&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Incentivised and Coercive Monetisation<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">As AI systems increasingly recommend products, services and subscriptions, concerns have&nbsp;emerged&nbsp;regarding&nbsp;monetisation practices embedded within AI interactions.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Examples include persistent upgrade prompts, recommendations influenced by commercial incentives, unclear distinctions between advertising and independent advice, or subscription mechanisms that make paid features appear necessary to continue an interaction. Unlike traditional online advertising, these commercial messages can be integrated directly into the conversation itself, making them less visible and more difficult to distinguish from neutral recommendations.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>IV- Risks Associated with AI Dark Patterns<\/strong>\u00a0<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Risks to privacy and data protection<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">AI systems often process large volumes of sensitive information. As a result, dark patterns affecting data collection, data retention or privacy controls may have significant implications for individual privacy.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The conversational nature of AI systems can encourage the disclosure of information relating to health, finances, relationships,&nbsp;emotions&nbsp;and behavioural preferences. Users may not always fully understand how this information is stored,&nbsp;reused&nbsp;or combined with other data sources.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Risks to user autonomy<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A recurring characteristic of dark patterns is their impact on users&#8217; ability to make independent and informed decisions.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Whether through interface design, personalised recommendations or conversational interactions, these mechanisms may influence choices in ways that are not&nbsp;immediately&nbsp;apparent. The issue is therefore not limited to deception&nbsp;but&nbsp;also concerns situations in which users are subtly steered towards outcomes that primarily serve the interests of a platform or service provider.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Emotional and psychological risks<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The ability of AI systems to&nbsp;maintain&nbsp;personalised and emotionally engaging interactions introduces&nbsp;additional&nbsp;considerations.&nbsp;Emotional attachment, social&nbsp;reinforcement&nbsp;and highly personalised conversations may influence how users interact with technology over time. In some situations, conversational systems may become a significant source of support,&nbsp;validation&nbsp;or companionship.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These dynamics can be particularly relevant when users experience loneliness, emotional&nbsp;distress&nbsp;or other forms of vulnerability. The growing popularity of AI&nbsp;companions has therefore intensified discussions concerning emotional dependency,&nbsp;trust&nbsp;and psychological influence.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Financial risks<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Dark patterns may also have direct economic consequences\u00a0as commercial recommendations, disguised advertising, pressure-driven\u00a0upgrades\u00a0and subscription mechanisms can influence purchasing behaviour. As AI systems increasingly\u00a0assist\u00a0users with shopping, service\u00a0selection\u00a0and transaction execution, the distinction between neutral\u00a0assistance\u00a0and commercial influence may become more difficult to\u00a0identify.\u00a0\u00a0<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>V- A Growing Regulatory Concern<\/strong>\u00a0<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Dark patterns are no longer viewed solely as a design issue. Over the past decade, regulators across multiple\u00a0jurisdictions\u00a0have progressively introduced measures addressing deceptive or manipulative digital practices. Regulatory frameworks exist in the European Union (AI Act, GDPR, Digital Services Act), the United States (FTC Act,\u00a0California Consumer Privacy Act), the United Kingdom (UK GDPR, Online Safety Act and ICO guidance on deceptive design), Australia\u00a0(Australian Consumer Law and Privacy Act reforms)\u00a0and several other jurisdictions, reflecting a broader recognition that digital systems can shape user behaviour and decision-making.\u00a0<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>The AI Act<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The AI Act is the first comprehensive AI regulation to explicitly address certain forms of harmful manipulation. Article 5 prohibits AI systems that deploy manipulative or deceptive techniques, or exploit vulnerabilities linked to age,&nbsp;disability&nbsp;or socio-economic circumstances, where those practices materially distort behaviour and are likely to cause harm.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The Regulation also relies on transparency as a safeguard against deception. Article 50 requires individuals to be informed when they are interacting with certain AI systems and introduces transparency obligations for AI-generated or AI-manipulated content, including specific requirements relating to deepfakes. These measures aim to increase awareness, support informed&nbsp;decision-making&nbsp;and reduce risks associated with deception,&nbsp;misinformation&nbsp;and hidden forms of influence.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>The GDPR<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Dark patterns became\u00a0an important issue\u00a0in data protection long before the emergence of generative AI. Under the GDPR, consent must be freely given, informed, specific and\u00a0unambiguous.\u00a0Design choices that steer individuals towards accepting data processing activities or make refusal more difficult may call into question whether consent was freely given.\u00a0<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>The Digital Services Act<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The Digital Services Act (DSA) explicitly addresses certain online interface designs that distort or impair users&#8217; ability to make free and informed decisions. The Regulation recognises that digital architecture itself can influence behaviour and extends regulatory scrutiny beyond the content presented to users.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The DSA is one of the first EU regulations to explicitly address certain forms of dark patterns in online platforms.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>The FTC Act<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In the United States, dark patterns are primarily addressed through consumer protection rules enforced by the Federal Trade Commission (FTC). The FTC considers certain deceptive interface designs and manipulative online practices capable of constituting unfair or deceptive acts or practices under the FTC Act.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The FTC&#8217;s report&nbsp;<em>Bringing Dark Patterns to Light<\/em>&nbsp;helped bring greater attention to deceptive digital design practices.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>The Online Safety Act<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In the United Kingdom, the Online Safety Act forms part of a broader framework aimed at strengthening accountability for digital services and reducing online harms. While it does not specifically regulate dark patterns, it reflects increasing&nbsp;regulatory discussion&nbsp;on&nbsp;platform design, user protection and the risks associated with digital services.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This framework is complemented by guidance from the Information Commissioner&#8217;s Office (ICO), which has addressed harmful and deceptive design practices in areas such as privacy,&nbsp;consent&nbsp;and children&#8217;s online services.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>VI- Governance Challenges for AI Systems<\/strong>\u00a0<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Assessing\u00a0behavioural\u00a0impacts<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Dark patterns highlight the importance of looking beyond the technical performance of AI systems.\u00a0Alongside traditional considerations such as\u00a0accuracy, robustness,\u00a0safety\u00a0and\u00a0bias, increasing attention\u00a0should be\u00a0devoted to the way AI systems influence behaviour and decision-making. This includes examining how recommendations are generated, how choices are presented and whether certain design features may affect user autonomy.\u00a0<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Transparency and user awareness<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Many dark patterns rely on users having limited visibility into how a system\u00a0operates.\u00a0Transparency therefore plays\u00a0an important role\u00a0in helping individuals understand the nature of their interaction with an AI system. Clear disclosures, understandable\u00a0explanations\u00a0and visibility over the operation of key features can make it easier to\u00a0identify\u00a0potential influence mechanisms and support informed decision-making.\u00a0<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>The emergence of AI agents<\/strong>\u00a0<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">AI agents introduce an&nbsp;additional&nbsp;dimension to these discussions. As these systems become capable of searching for information, comparing&nbsp;alternatives&nbsp;and taking actions on behalf of users, influence may no longer occur solely between a platform and a human user.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Organisations will increasingly need to consider how AI agents evaluate information, how recommendations are prioritised and how external digital environments may shape their behaviour. This extends governance discussions beyond traditional human-computer interactions and introduces new questions&nbsp;regarding&nbsp;transparency,&nbsp;oversight&nbsp;and accountability in agentic systems.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion: From User Experience to AI Governance<\/strong>\u00a0<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Dark patterns have long been associated with deceptive interface design. The growing adoption of AI systems has expanded the discussion beyond websites and mobile applications to recommendation systems, conversational agents, AI&nbsp;companions&nbsp;and autonomous agents.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As AI becomes increasingly involved in how information is generated,&nbsp;prioritised&nbsp;and presented, questions of influence, transparency and user autonomy are becoming more prominent. Practices that once relied on visual design choices can now&nbsp;emerge&nbsp;through personalisation, conversational interactions, memory&nbsp;functions&nbsp;or AI-generated recommendations.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Addressing these challenges requires more than&nbsp;identifying&nbsp;individual dark patterns. Organisations increasingly need to understand how AI systems interact with users, how behavioural influence may arise throughout the AI lifecycle, and which regulatory requirements may apply under frameworks such as the AI Act, the GDPR and the Digital Services Act.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Preparing for Emerging AI Governance Challenges<\/strong>\u00a0<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">With&nbsp;Naaia, organisations can strengthen their approach to AI governance by&nbsp;identifying&nbsp;AI systems, documenting use cases, assessing potential risks, and understanding the regulatory obligations associated with their deployment.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Discover how&nbsp;Naaia&nbsp;enables operational AI governance by&nbsp;helping&nbsp;organisations map AI systems, classify use cases, manage governance&nbsp;processes&nbsp;and&nbsp;maintain&nbsp;the documentation needed to support compliance,&nbsp;transparency&nbsp;and accountability across the AI lifecycle.&nbsp;<\/p>\n\n\n<div class=\"naaia-button-wrapper wp-block-naaia-button\">\n\t<a href=\"https:\/\/naaia.ai\/en\/get-a-demo\" class=\"naaia-btn--primary\">\n\n\t\t\t\t\t<img\n\t\t\t\tclass=\"naaia-btn__icon\"\n\t\t\t\tsrc=\"https:\/\/naaia.ai\/wp-content\/themes\/naaia\/assets\/img\/icon-stars.svg\"\n\t\t\t\talt=\"\"\n\t\t\t\taria-hidden=\"true\"\n\t\t\t\twidth=\"16\"\n\t\t\t\theight=\"16\"\n\t\t\t>\n\t\t\n\t\t<span class=\"naaia-btn__label\">\n\t\t\t<span class=\"naaia-btn__label-text\">Get a demo<\/span>\n\t\t\t<span class=\"naaia-btn__label-text\" aria-hidden=\"true\">Get a demo<\/span>\n\t\t<\/span>\n\n\t<\/a>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Artificial intelligence is becoming a core&nbsp;component&nbsp;of digital services.&nbsp;Recommendation systems organise access to information, conversational agents&nbsp;assist&nbsp;users in everyday tasks, and AI systems are increasingly involved in purchasing,&nbsp;search&nbsp;and decision-making processes.&nbsp; As these&hellip; <a href=\"https:\/\/naaia.ai\/en\/dark-patterns-artificial-intelligence\/\">Lire la suite<\/a><\/p>\n","protected":false},"author":9,"featured_media":4237,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"naaia_last_modified":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-4236","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-governance-blog"],"_links":{"self":[{"href":"https:\/\/naaia.ai\/en\/wp-json\/wp\/v2\/posts\/4236","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/naaia.ai\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/naaia.ai\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/naaia.ai\/en\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/naaia.ai\/en\/wp-json\/wp\/v2\/comments?post=4236"}],"version-history":[{"count":1,"href":"https:\/\/naaia.ai\/en\/wp-json\/wp\/v2\/posts\/4236\/revisions"}],"predecessor-version":[{"id":4238,"href":"https:\/\/naaia.ai\/en\/wp-json\/wp\/v2\/posts\/4236\/revisions\/4238"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/naaia.ai\/en\/wp-json\/wp\/v2\/media\/4237"}],"wp:attachment":[{"href":"https:\/\/naaia.ai\/en\/wp-json\/wp\/v2\/media?parent=4236"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/naaia.ai\/en\/wp-json\/wp\/v2\/categories?post=4236"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/naaia.ai\/en\/wp-json\/wp\/v2\/tags?post=4236"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}