{"id":760,"date":"2026-07-02T21:48:18","date_gmt":"2026-07-02T19:48:18","guid":{"rendered":"https:\/\/aiolia.eu\/?page_id=760"},"modified":"2026-07-02T22:47:04","modified_gmt":"2026-07-02T20:47:04","slug":"policy-brief-ai-ethics-from-principles-to-practice","status":"publish","type":"page","link":"https:\/\/aiolia.eu\/index.php\/policy-brief-ai-ethics-from-principles-to-practice\/","title":{"rendered":"Policy brief &#8220;AI Ethics from Principles to Practice&#8221;"},"content":{"rendered":"\n<style>\n  .aiolia-brief {\n    --aiolia-red: #df002b;\n    --aiolia-red-dark: #b80022;\n    --aiolia-black: #181818;\n    --aiolia-text: #2a2a2a;\n    --aiolia-muted: #666;\n    --aiolia-light: #f4f4f4;\n    --aiolia-pink: #fff4f6;\n    --aiolia-line: #e3e3e3;\n    --aiolia-green: #23463d;\n    max-width: 1120px;\n    margin: 0 auto;\n    padding: 8px 18px 56px;\n    color: var(--aiolia-text);\n    font-family: inherit;\n    line-height: 1.55;\n  }\n\n  .aiolia-brief * {\n    box-sizing: 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var(--aiolia-red);\n    color: #fff;\n    font-weight: 800;\n  }\n\n  .aiolia-rec-title {\n    color: var(--aiolia-black);\n    font-size: 1.08rem;\n    font-weight: 800;\n    line-height: 1.3;\n  }\n\n  .aiolia-rec-body {\n    padding: 0 24px 24px 90px;\n  }\n\n  .aiolia-timeline {\n    display: grid;\n    grid-template-columns: repeat(4, 1fr);\n    gap: 14px;\n    margin-top: 22px;\n  }\n\n  .aiolia-time {\n    padding: 20px;\n    background: var(--aiolia-light);\n    border-top: 4px solid var(--aiolia-red);\n  }\n\n  .aiolia-time h3 {\n    margin: 0 0 8px;\n    color: var(--aiolia-black);\n  }\n\n  .aiolia-implications {\n    display: grid;\n    grid-template-columns: 1fr 1fr;\n    gap: 22px;\n    margin-top: 22px;\n  }\n\n  .aiolia-barriers {\n    margin-top: 22px;\n  }\n\n  .aiolia-barriers ul {\n    margin: 10px 0 0 1.1em;\n    padding: 0;\n  }\n\n  .aiolia-barriers li {\n    margin-bottom: 10px;\n  }\n\n  .aiolia-conclusion {\n    margin-top: 44px;\n    padding: 30px;\n    background: var(--aiolia-green);\n    color: #fff;\n  }\n\n  .aiolia-conclusion h2 {\n    margin: 0 0 12px;\n    color: #fff;\n  }\n\n  .aiolia-conclusion p {\n    margin: 0;\n  }\n\n  .aiolia-footer {\n    margin-top: 34px;\n    padding-top: 24px;\n    border-top: 2px solid var(--aiolia-green);\n    display: grid;\n    grid-template-columns: 1fr 1.5fr;\n    gap: 28px;\n    color: var(--aiolia-text);\n  }\n\n  .aiolia-footer h2,\n  .aiolia-footer h3 {\n    margin-top: 0;\n    color: var(--aiolia-red);\n  }\n\n  .aiolia-disclaimer {\n    margin-top: 18px;\n    font-size: 0.92rem;\n    color: var(--aiolia-muted);\n  }\n\n  @media (max-width: 900px) {\n    .aiolia-two-col,\n    .aiolia-insights,\n    .aiolia-implications,\n    .aiolia-footer {\n      grid-template-columns: 1fr;\n    }\n\n    .aiolia-usecases {\n      grid-template-columns: repeat(2, 1fr);\n    }\n\n    .aiolia-timeline {\n      grid-template-columns: repeat(2, 1fr);\n    }\n  }\n\n  @media (max-width: 640px) {\n    .aiolia-brief {\n      padding-left: 12px;\n      padding-right: 12px;\n    }\n\n    .aiolia-stats,\n    .aiolia-usecases,\n    .aiolia-timeline {\n      grid-template-columns: 1fr;\n    }\n\n    .aiolia-stat {\n      border-right: none;\n      border-bottom: 1px solid rgba(255,255,255,0.25);\n    }\n\n    .aiolia-rec summary {\n      grid-template-columns: 42px 1fr 18px;\n      padding: 16px;\n    }\n\n    .aiolia-rec-number {\n      width: 38px;\n      height: 38px;\n    }\n\n    .aiolia-rec-body {\n      padding: 0 18px 20px 18px;\n    }\n  }\n<\/style>\n\n<article class=\"aiolia-brief\">\n\n  <section class=\"aiolia-key\" aria-labelledby=\"key-message\">\n    <h2 id=\"key-message\">Key message<\/h2>\n    <p>\n      Ethics operationalisation is essential, beneficial, and possible for organisations \u2014 but it requires structural support.\n      The AI Act creates obligations without providing tools for their implementation, while ALTAI is too abstract for industry\n      to implement AI ethics in real life. Organisations, particularly smaller ones, are left to develop processes from scratch,\n      often without adequate resources, guidance, or coordination infrastructure to support meaningful implementation of ethics\n      in their AI designs and deployments. This brief sets out six concrete recommendations to address these issues.\n    <\/p>\n  <\/section>\n\n  <section class=\"aiolia-section\" aria-labelledby=\"problem-context\">\n    <h2 id=\"problem-context\" class=\"aiolia-section-title\">1. Problem and policy context<\/h2>\n\n    <div class=\"aiolia-two-col\">\n      <div class=\"aiolia-card\">\n        <h3>Context<\/h3>\n        <p>\n          Across Europe, organisations are deploying AI systems that directly influence how people think, decide, and behave.\n          The EU AI Act sets requirements for ethical AI \u2014 but turning those requirements into practice is far harder than the\n          law acknowledges.\n        <\/p>\n        <p>\n          The Assessment List for Trustworthy AI (ALTAI), published by the EU\u2019s AI High-Level Expert Group in 2020, remains the\n          most widely used self-assessment tool for organisations deploying AI. Its seven requirements, covering human agency\n          and oversight, technical robustness, privacy, transparency, fairness, societal wellbeing, and accountability, provide\n          a broadly applicable framework.\n        <\/p>\n      <\/div>\n\n      <div class=\"aiolia-card soft\">\n        <h3>What AIOLIA adds<\/h3>\n        <p>\n          AIOLIA\u2019s research shows that ALTAI is too abstract for AI developers and has structural gaps in practice. The consequence\n          is that it has only limited value for organisations in implementing AI ethics when they develop or deploy AI.\n        <\/p>\n        <p>\n          AIOLIA created a unique, bottom-up ethics process for organisations to ensure ethics is deployed in practice. It was\n          created together with industry and translates ALTAI\u2019s high-level ethics principles into concrete, measurable technical\n          and organisational measures within a deployment context.\n        <\/p>\n        <p>\n          The work further resulted in a portfolio of 175 practical measures, 12 contextualised ethics principles, 37 components,\n          and 6 process recommendations \u2014 grounded in real-world experience, not theoretical ideals.\n        <\/p>\n      <\/div>\n    <\/div>\n\n    <div class=\"aiolia-stats\" aria-label=\"AIOLIA headline figures\">\n      <div class=\"aiolia-stat\">\n        <strong>175<\/strong>\n        <span>Practical measures<\/span>\n        <em>Technical and organisational, across ten use cases<\/em>\n      <\/div>\n      <div class=\"aiolia-stat\">\n        <strong>12<\/strong>\n        <span>Ethics principles<\/span>\n        <em>Bottom-up, contextualised from real deployments<\/em>\n      <\/div>\n      <div class=\"aiolia-stat\">\n        <strong>6<\/strong>\n        <span>Process recommendations<\/span>\n        <em>On how to operationalise ethics effectively<\/em>\n      <\/div>\n    <\/div>\n\n    <h3>Ten use cases, one shared challenge<\/h3>\n    <p>\n      AIOLIA created a framework for the operationalisation of AI ethics in cooperation with industry partners in ten use cases:\n      six European use cases and four non-European use cases spanning professional and personal AI deployment contexts.\n      Five use cases operate in professional settings and five in personal contexts.\n    <\/p>\n    <p>\n      The deployment context is significant: AIOLIA demonstrates that professional deployment contexts emphasise accountability,\n      transparency, and decision traceability, while personal deployment contexts foreground user autonomy, safety, and long-term\n      wellbeing. This distinction has direct implications for how ethics frameworks, including the AI Act and ALTAI, are applied.\n    <\/p>\n\n    <div class=\"aiolia-usecases\">\n      <div class=\"aiolia-usecase\">\n        <b>UC1<\/b>\n        <h3>Medical doctors using AI for diagnosis and treatment<\/h3>\n        <p>AI systems support doctors in interpreting images, predicting outcomes, and recommending personalised treatments.<\/p>\n      <\/div>\n\n      <div class=\"aiolia-usecase\">\n        <b>UC2<\/b>\n        <h3>Safety engineers using AI for software approval<\/h3>\n        <p>AI assists safety engineers in assessing and validating automotive software to ensure compliance and safety.<\/p>\n      <\/div>\n\n      <div class=\"aiolia-usecase\">\n        <b>UC3<\/b>\n        <h3>HR professionals using AI for people management<\/h3>\n        <p>AI supports recruitment, performance evaluation, and employee development decisions.<\/p>\n      <\/div>\n\n      <div class=\"aiolia-usecase\">\n        <b>UC4<\/b>\n        <h3>Security professionals using AI for surveillance and threat detection<\/h3>\n        <p>AI helps detect suspicious activities and improve situational awareness in real time.<\/p>\n      <\/div>\n\n      <div class=\"aiolia-usecase\">\n        <b>UC5<\/b>\n        <h3>Personal AI assistants in daily life<\/h3>\n        <p>AI assistants help users manage tasks, provide information, and support decision-making.<\/p>\n      <\/div>\n\n      <div class=\"aiolia-usecase\">\n        <b>UC6<\/b>\n        <h3>Deepfake-based therapy<\/h3>\n        <p>AI-generated deepfakes are used in therapeutic settings to support mental health treatments.<\/p>\n      <\/div>\n\n      <div class=\"aiolia-usecase\">\n        <b>UC7<\/b>\n        <h3>Workplace equipped with emotion recognition<\/h3>\n        <p>AI analyses employees\u2019 emotions to improve wellbeing, optimise team dynamics, and support workplace decisions.<\/p>\n      <\/div>\n\n      <div class=\"aiolia-usecase\">\n        <b>UC8<\/b>\n        <h3>AI helpers in elderly care facilities<\/h3>\n        <p>AI assists caregivers in monitoring residents, managing tasks, and improving quality of care.<\/p>\n      <\/div>\n\n      <div class=\"aiolia-usecase\">\n        <b>UC9<\/b>\n        <h3>Personal companion for elderly<\/h3>\n        <p>AI companions provide conversation, reminders, and emotional support to reduce loneliness and enhance wellbeing.<\/p>\n      <\/div>\n\n      <div class=\"aiolia-usecase\">\n        <b>UC10<\/b>\n        <h3>Grief bots<\/h3>\n        <p>AI chatbots offer comfort and support to people experiencing grief by providing a safe space for expression and guidance.<\/p>\n      <\/div>\n    <\/div>\n\n    <div class=\"aiolia-insights\">\n      <div class=\"aiolia-insight\">\n        <h3>Ethics operationalisation is effective risk management<\/h3>\n        <p>\n          A consistent insight across all ten use cases is that organisations approach AI ethics primarily through the perspective\n          of risk management and benefits \u2014 not abstract moral values. Organisations that embed ethics can gain concrete advances:\n          reduced legal exposure, stronger stakeholder trust, enhanced reputational legitimacy, and new business opportunities as\n          ethics-aware providers.\n        <\/p>\n      <\/div>\n\n      <div class=\"aiolia-insight\">\n        <h3>High-level principles are necessary but insufficient<\/h3>\n        <p>\n          AIOLIA\u2019s bottom-up co-creation process confirmed that well-established ethics frameworks, including the ALTAI Assessment\n          List and OECD AI Principles, resonate with practitioners. However, abstract principles alone are unable to guide concrete\n          implementation. Practitioners need the translation of these principles into context-specific actions.\n        <\/p>\n      <\/div>\n\n      <div class=\"aiolia-insight\">\n        <h3>Ethics principles interact, create tensions, and require managing trade-offs<\/h3>\n        <p>\n          Transparency requirements can conflict with privacy protections, while safety monitoring requires data collection that\n          clashes with user anonymity. These tensions cannot be resolved by addressing abstract ethics principles in isolation;\n          they require ongoing institutional mechanisms to balance competing ethics expectations.\n        <\/p>\n      <\/div>\n    <\/div>\n\n    <div class=\"aiolia-card\" style=\"margin-top:22px;\">\n      <h3>AI-driven change in professional behaviour vs individual cognition<\/h3>\n      <p>\n        AIOLIA reveals a structural difference in how ethics principles play out depending on deployment context. In professional\n        settings such as healthcare, safety engineering, HR, and security, the core concerns are accountability, traceability, and\n        preventing over-reliance on AI in high-stakes decisions. In personal settings such as AI companions, personal assistants,\n        and deepfake therapy, the main focus is on user autonomy, psychological safety, and long-term wellbeing.\n      <\/p>\n      <p>\n        Current AI governance frameworks, including ALTAI, do not adequately reflect this distinction \u2014 and the AI Act\u2019s high-risk\n        classification system does not fully capture the risks in personal AI use.\n      <\/p>\n    <\/div>\n  <\/section>\n\n  <section class=\"aiolia-section\" aria-labelledby=\"recommendations\">\n    <h2 id=\"recommendations\" class=\"aiolia-section-title\">2. Policy recommendations<\/h2>\n\n    <div class=\"aiolia-recommendations\">\n\n      <details class=\"aiolia-rec\" open=\"\">\n        <summary>\n          <span class=\"aiolia-rec-number\">1<\/span>\n          <span class=\"aiolia-rec-title\">Develop implementation-driven guidance to complement the AI Act\u2019s principles<\/span>\n        <\/summary>\n        <div class=\"aiolia-rec-body\">\n          <p>\n            The AI Act establishes what organisations must achieve in AI ethics but not how. AIOLIA demonstrates that the way\n            obligations like \u201censure human oversight\u201d or \u201cprovide explainability\u201d translate into practice varies enormously by\n            context, sector, and AI system type.\n          <\/p>\n          <p>\n            The European AI Office and national supervisory authorities should develop sector-specific, implementation-driven\n            guidance, drawing on practitioner co-creation processes like AIOLIA\u2019s, that shows organisations concretely how to\n            operationalise each requirement, not merely what the requirement says.\n          <\/p>\n        <\/div>\n      <\/details>\n\n      <details class=\"aiolia-rec\">\n        <summary>\n          <span class=\"aiolia-rec-number\">2<\/span>\n          <span class=\"aiolia-rec-title\">Update ALTAI to reflect the professional\/personal context distinction, temporal risks, and practical handling of competing ethics requirements<\/span>\n        <\/summary>\n        <div class=\"aiolia-rec-body\">\n          <p>\n            AIOLIA\u2019s bottom-up process reveals important gaps in ALTAI that limit its usefulness for practitioners. ALTAI does not\n            adequately distinguish between professional and personal AI use contexts or account for the long-term and cumulative\n            effects of AI on human cognition and behaviour, such as deskilling, dependency, and gradual manipulation.\n          <\/p>\n          <p>\n            It further lacks guidance on the context-specificity of ethics implementations across the AI lifecycle and does not\n            adequately account for tensions when implementing competing ethics principles. The European Commission should provide\n            accompanying guidance to ALTAI, treating AIOLIA\u2019s D3.1 and D3.2 as direct inputs, to address these gaps.\n          <\/p>\n          <p>\n            The AI Office should consider developing ALTAI variants for specific high-risk AI systems under Annex III of the AI Act.\n          <\/p>\n        <\/div>\n      <\/details>\n\n      <details class=\"aiolia-rec\">\n        <summary>\n          <span class=\"aiolia-rec-number\">3<\/span>\n          <span class=\"aiolia-rec-title\">Resource and expand regulatory sandboxes to support ethics operationalisation for SMEs<\/span>\n        <\/summary>\n        <div class=\"aiolia-rec-body\">\n          <p>\n            Small and medium-sized organisations often lack the capacity to build comprehensive ethics governance processes from\n            scratch. While Article 57 of the AI Act requires member states to establish regulatory sandboxes, these are primarily\n            framed around legal compliance testing.\n          <\/p>\n          <p>\n            Policymakers should expand the scope and resourcing of sandboxes, and leverage Digital Innovation Hubs, to offer\n            structured support for ethics operationalisation, including access to multidisciplinary expertise, co-creation\n            facilitation, and independent review mechanisms that are affordable to smaller actors.\n          <\/p>\n        <\/div>\n      <\/details>\n\n      <details class=\"aiolia-rec\">\n        <summary>\n          <span class=\"aiolia-rec-number\">4<\/span>\n          <span class=\"aiolia-rec-title\">Mandate and standardise ethics process requirements, not only outcome requirements<\/span>\n        <\/summary>\n        <div class=\"aiolia-rec-body\">\n          <p>\n            The AI Act focuses primarily on the outputs and properties of AI systems, such as accuracy, explainability, and\n            non-discrimination. AIOLIA demonstrates that the effective implementation of ethics depends strongly on the process\n            through which ethics is operationalised: who is involved, how trade-offs are managed, how feedback is collected, and\n            how findings are used to update systems and procedures.\n          <\/p>\n          <p>\n            Policymakers should introduce process-level requirements for high-risk AI \u2014 mandating co-creation with affected\n            communities, multidisciplinary governance committees, feedback loops, and documented ethics decision-making \u2014 alongside\n            existing output requirements.\n          <\/p>\n        <\/div>\n      <\/details>\n\n      <details class=\"aiolia-rec\">\n        <summary>\n          <span class=\"aiolia-rec-number\">5<\/span>\n          <span class=\"aiolia-rec-title\">Recognise and fund the co-creation model as a standard practice for ethics operationalisation<\/span>\n        <\/summary>\n        <div class=\"aiolia-rec-body\">\n          <p>\n            AIOLIA\u2019s co-creation process, pairing academic ethics and AI expertise with industrial partners through structured\n            operationalisation cycles, produced 175 concrete measures that neither party could have developed alone. AIOLIA has\n            condensed them into a 6-page Measures Portfolio. This Portfolio generates ethics guidance that is simultaneously\n            principled and practically grounded.\n          <\/p>\n          <p>\n            Policymakers should recognise co-creation partnerships as a legitimate and fundable approach to AI ethics\n            implementation, and create EU-level infrastructure, including funding, coordination, and methodology toolkits, to make\n            this model accessible beyond research projects.\n          <\/p>\n        <\/div>\n      <\/details>\n\n      <details class=\"aiolia-rec\">\n        <summary>\n          <span class=\"aiolia-rec-number\">6<\/span>\n          <span class=\"aiolia-rec-title\">Invest in longitudinal research on AI\u2019s impacts on human cognition and behaviour<\/span>\n        <\/summary>\n        <div class=\"aiolia-rec-body\">\n          <p>\n            AIOLIA identifies a critical knowledge gap: long-term effects of AI systems on human cognitive capacities,\n            decision-making patterns, social skills, and wellbeing are poorly understood. Without this evidence base, organisations\n            and policymakers are making precautionary judgements without solid empirical basis.\n          <\/p>\n          <p>\n            The EU should fund structured longitudinal research programmes, enabling independent researchers to access operational\n            AI systems and usage data, with robust data protection safeguards, to build the evidence base needed for informed,\n            proportionate regulation.\n          <\/p>\n        <\/div>\n      <\/details>\n\n    <\/div>\n  <\/section>\n\n  <section class=\"aiolia-section\" aria-labelledby=\"implications\">\n    <h2 id=\"implications\" class=\"aiolia-section-title\">3. Policy implications<\/h2>\n\n    <h3>Implementation timeline<\/h3>\n\n    <div class=\"aiolia-timeline\">\n      <div class=\"aiolia-time\">\n        <h3>Immediate<\/h3>\n        <p>\n          Recommendations 1 and 2, implementation guidance and ALTAI update, are immediately actionable. They can be initiated by\n          the European Commission in 2026.\n        <\/p>\n      <\/div>\n\n      <div class=\"aiolia-time\">\n        <h3>Midterm<\/h3>\n        <p>\n          Recommendations 3 and 5, SME support and co-creation model, require medium-term investment from 2026 to 2028.\n        <\/p>\n      <\/div>\n\n      <div class=\"aiolia-time\">\n        <h3>Legislative<\/h3>\n        <p>\n          Recommendation 4, process requirements, involves legislative and regulatory development and is a medium-term priority.\n        <\/p>\n      <\/div>\n\n      <div class=\"aiolia-time\">\n        <h3>Continuous<\/h3>\n        <p>\n          Recommendation 6, longitudinal research, should begin immediately and be funded continuously across the legislative cycle.\n        <\/p>\n      <\/div>\n    <\/div>\n\n    <div class=\"aiolia-implications\">\n      <div class=\"aiolia-card\">\n        <h3>The compliance framing is both a risk and an opportunity<\/h3>\n        <p>\n          AIOLIA shows that industrial partners are supportive of AI ethics, especially when it is framed as an approach to risk\n          management and business benefit rather than moral obligation. Policymakers can harness this orientation by clearly\n          communicating the legal, reputational, and commercial risks of non-compliance, while simultaneously emphasising the\n          business benefits of implementing ethics compliance. If the AI Act is primarily framed as a compliance burden, this\n          motivation will be lost.\n        <\/p>\n      <\/div>\n\n      <div class=\"aiolia-card\">\n        <h3>Voluntary measures cannot substitute for structural support<\/h3>\n        <p>\n          Across all ten use cases, partners implemented ethics measures that exceeded legal requirements. But they did so as\n          individual organisations, without shared infrastructure, standardised tools, or access to common expertise. The result is\n          often a patchwork of siloed innovation and knowledge. Structural policy support, including guidance, sandboxes,\n          co-creation infrastructure, and funded research, is necessary to bring ethics operationalisation into standard practice.\n        <\/p>\n      <\/div>\n    <\/div>\n\n    <div class=\"aiolia-card aiolia-barriers\">\n      <h3>Potential barriers<\/h3>\n      <ul>\n        <li><strong>Industry heterogeneity:<\/strong> a single implementation approach cannot serve all sectors; guidance must be context-sensitive, requiring sustained co-development with diverse industry actors.<\/li>\n        <li><strong>Regulatory capacity and bandwidth:<\/strong> developing sector-specific implementation guidance at scale requires significant expertise and resources for stakeholder consultation.<\/li>\n        <li><strong>Research access:<\/strong> independent longitudinal research on the impact of AI on users requires AI providers to grant data access, which raises liability and IP concerns that must be addressed through carefully designed legal frameworks.<\/li>\n        <li><strong>Fast-moving developments:<\/strong> as AI systems evolve rapidly, implementation guidance and ALTAI updates risk becoming outdated. A regular review cycle must be built in from the outset.<\/li>\n      <\/ul>\n    <\/div>\n  <\/section>\n\n  <section class=\"aiolia-conclusion\" aria-labelledby=\"conclusion\">\n    <h2 id=\"conclusion\">Conclusion<\/h2>\n    <p>\n      Ethics is a continuous reflective and anticipatory practice, embedded in organisational culture, governance structures, and\n      industrial, professional contexts. The EU AI Act and ALTAI have created important foundations for AI ethics. What is needed is\n      an operational translation of the regulatory framework to help organisations meet their obligations: practical guidance,\n      shared tools, co-creation support, skills infrastructure, and a sustained evidence base on AI\u2019s impacts. AIOLIA provides a\n      tested model and a ready-to-use portfolio of measures. The six recommendations in this policy brief show how policymakers can\n      make them work in practice.\n    <\/p>\n  <\/section>\n\n\n\n<\/article>\n\n\n\n<p class=\"wp-block-paragraph\">This policy brief (<a href=\"https:\/\/aiolia.eu\/wp-content\/uploads\/2026\/07\/AIOLIA_Policy_Brief_From-Principles-to-Practice-v3.pdf\" data-type=\"attachment\" data-id=\"768\">PDF<\/a>) was published on 01 July 2026.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key message Ethics operationalisation is essential, beneficial, and possible for organisations \u2014 but it requires structural support. The AI Act creates obligations without providing tools for their implementation, while ALTAI is too abstract for industry to implement AI ethics in real life. Organisations, particularly smaller ones, are left to develop processes from scratch, often without&hellip;&nbsp;<a href=\"https:\/\/aiolia.eu\/index.php\/policy-brief-ai-ethics-from-principles-to-practice\/\" rel=\"bookmark\">Read More &raquo;<span class=\"screen-reader-text\">Policy brief &#8220;AI Ethics from Principles to Practice&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"class_list":["post-760","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/aiolia.eu\/index.php\/wp-json\/wp\/v2\/pages\/760","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aiolia.eu\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/aiolia.eu\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/aiolia.eu\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aiolia.eu\/index.php\/wp-json\/wp\/v2\/comments?post=760"}],"version-history":[{"count":3,"href":"https:\/\/aiolia.eu\/index.php\/wp-json\/wp\/v2\/pages\/760\/revisions"}],"predecessor-version":[{"id":782,"href":"https:\/\/aiolia.eu\/index.php\/wp-json\/wp\/v2\/pages\/760\/revisions\/782"}],"wp:attachment":[{"href":"https:\/\/aiolia.eu\/index.php\/wp-json\/wp\/v2\/media?parent=760"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}