Revised Product Liability Directive (Directive (EU) 2024/2853)

Scope and obligations

EU strict-liability regime for defective products, modernised for the digital age and explicitly extended to software and AI systems. Repeals and replaces the 1985 Product Liability Directive (85/374/EEC). Art. 4(1) redefines "product" to include "software" (and digital manufacturing files, electricity); Recital 13 confirms a "developer or producer of software, including AI system providers within the meaning of Regulation (EU) 2024/1689" is treated as a manufacturer, irrespective of delivery model (on-device, cloud, SaaS). Free and open-source software developed/supplied outside a commercial activity is excluded (Recital 14). The load-bearing topic is REDRESS: Art. 6 sets compensable damage (death/personal injury incl. medically recognised psychological harm; property; destruction/corruption of non-professional data), Art. 8 names liable economic operators (manufacturers, component makers, importers, authorised reps, fulfilment-service providers, certain distributors and online platforms), Art. 9 creates a court-ordered evidence-disclosure mechanism, and Art. 10 establishes rebuttable presumptions of defectiveness and of the causal link — including a presumption available where a claimant faces "excessive difficulties, in particular due to technical or scientific complexity" (Art. 10(4)), the provision most relevant to opaque AI systems. Art. 7(2)(c) makes the product's "ability to continue to learn or acquire new features after it is placed on the market" relevant to defectiveness; Art. 11(2) keeps manufacturers liable for defects introduced by software updates/upgrades within their control. Adopted 23 Oct 2024, in force 18 Nov 2024, but substantive liability rules apply only to products on the market after 9 Dec 2026 (Art. 2(1)), so status = adopted_not_in_force. Designed to interlock with the EU AI Act (Reg. (EU) 2024/1689): breach of AI Act obligations can feed the Art. 10 presumptions. (The separate proposed AI Liability Directive was withdrawn by the Commission in 2025; the PLD now carries the principal EU AI-liability load.) An ex-post liability instrument, deliberately silent on most ex-ante AI-governance topics (transparency mandates, biometrics, deepfakes, compute, sector-specific rules) — those are governed by the AI Act and sectoral law, not by this directive.

Revised Product Liability Directive (Directive (EU) 2024/2853) addresses 1 contested AI-governance topics explicitly, 2 via general principles,.

Topics governed

• implicit
  Transparency Obligations— Art. 9 — court-ordered disclosure of relevant evidence in the defendant's control, reinforced by the Art. 10(2)(a) adverse presumption for non-disclosure
• governs
  Individual Redress— Arts. 6, 8, 9, 10 — strict-liability compensation for defective products incl. software/AI: compensable damage (Art. 6), liable economic operators (Art. 8), court-ordered evidence disclosure (Art. 9), and rebuttable presumptions of defect + causation (Art. 10)

  §Art. 10(4)paraphrase

  A national court shall presume defectiveness or the causal link where the claimant faces excessive difficulties, in particular due to technical or scientific complexity, in proving it.

  source ↗
• implicit
  Agentic AI Governance— Art. 7(2)(c) — defectiveness accounts for a product's ability to continue to learn or acquire new features after market placement; Art. 11(2) — post-placement software-update liability within the manufacturer's control

How to cite this article

Does this instrument’s approach work? — the social-science evidence

Aggregated over the 3 topics this instrument governs: whether each harm is empirically real, and whether the peer-reviewed evidence shows governance reduces it. The badge is the epistemic status of the evidence— “thin”/“absent” efficacy evidence is itself a finding (the “second silence”). Each epistemic-status label is Policy Window's editorial assessment of the cited evidence base (a structured classification), not a verdict any single source issues.

Of the 3 governed topics with a social-science evidence review, evidence that governance reduces the harm is established for 0, contested for 0, thin for 0, and absent for 3 — for most, no replicated study yet shows this instrument's approach works (the "second silence").

• Agentic AI Governance

  • Is the harm real?evidence: thin

    The capability that agentic governance targets — autonomous multi-step action — is real and rapidly, measurably advancing: METR finds the task length AI agents complete at 50% reliability has doubled roughly every seven months for the past six years (about 50 minutes for frontier 2025 models), and the UK AI Security Institute's first Frontier AI Trends Report (Dec 2025, >30 systems) reports models now finish hour-long software tasks >40% of the time versus <5% in late 2023. The distinct realized HARM from agency (as opposed to the underlying model) is, however, thinly documented: on consequential real-world tasks agents still fail the majority — Gemini 2.5 Pro completed only 30.3% of TheAgentCompany's 175 professional tasks (OpenHands scaffold, project leaderboard) — so the agency-specific harm magnitude is early and context-dependent rather than established at scale.

    Sources: Kwa, West, Becker et al. 2025 (METR; arXiv:2503.14499, 'Measuring AI Ability to Complete Long Tasks'); UK AI Security Institute 2025 (Frontier AI Trends Report, Dec 2025); Xu, Song, Zhou et al. 2024 (TheAgentCompany, arXiv:2412.14161); 30.3% figure per TheAgentCompany leaderboard (OpenHands)

  • Does governance work?evidence: absent

    There is no impact-evaluation evidence that agent-specific governance reduces agentic harm: the operative regimes — the EU GPAI Code of Practice (published July 2025, voluntary/non-binding), the Seoul Frontier AI Safety Commitments (2024, voluntary), and AISI agent evaluations — are 2024-25 vintage and have never been measured against an outcome. The scholarship itself has not settled the contested unit of regulation: Kolt (2025) argues for governing the agentic relationship via principal-agent and agency-law tools, while Chan, Ezell, Kaufmann et al. (2024) propose agent-specific visibility mechanisms (identifiers, real-time monitoring, activity logging) that remain proposal-stage and unevaluated — meaning the field has design proposals but, as with most frontier-AI rules, the evidence that any of them works is absent rather than merely thin.

    Sources: Kolt 2025 ('Governing AI Agents', 101 Notre Dame L. Rev., forthcoming; arXiv:2501.07913); Chan, Ezell, Kaufmann et al. 2024 ('Visibility into AI Agents', ACM FAccT 2024, pp. 958-973; DOI 10.1145/3630106.3658948); EU AI Office 2025 (GPAI Code of Practice, July 2025); Seoul Frontier AI Safety Commitments 2024

• Individual Redress

  • Is the harm real?evidence: established

    The premise behind redress — that affected people lack meaningful recourse against automated decisions — is real, but the flagship instrument is weaker than commonly assumed. Wachter, Mittelstadt & Floridi (2017) show GDPR creates only a limited 'right to be informed,' not a binding 'right to explanation' of specific decisions; and controlled work finds the explanations actually delivered do not measurably improve lay decision accuracy over showing the bare AI prediction (Alufaisan et al. 2021; and a 2022 meta-analysis by Schemmer et al. — screening 393 articles down to 9 in the final analysis — reports 'no effect of explanations on users' performance compared to sole AI predictions,' even though XAI overall had a positive effect). Honest caveat: the legitimacy/dignity value of being heard is empirically well established in the procedural-justice tradition even where outcome accuracy is unchanged, so 'redress fails' depends on which aim is measured.

    Sources: Wachter, Mittelstadt & Floridi 2017 (International Data Privacy Law 7(2):76); Alufaisan, Marusich, Bakdash, Zhou & Kantarcioglu 2021 (Proceedings of the AAAI Conference on AI 35(8):6618); Schemmer, Hemmer, Nitsche, Kühl & Vössing 2022 (AAAI/ACM AIES '22, meta-analysis)

  • Does governance work?evidence: absent

    There is no rigorous impact evaluation showing that mandated redress mechanisms (right-to-explanation, appeal, human-in-the-loop review) actually reduce erroneous or unfair automated decisions — the evidence that the rule works is itself missing. The closest experimental analogues are discouraging: explanations increase humans' acceptance of AI recommendations regardless of correctness (Bansal et al. 2021), and algorithm-in-the-loop oversight can introduce racial disparities and exhibit automation bias rather than reliably catching model errors (Green & Chen 2019). The procedural-justice literature (Tyler 1990; Lind & Tyler 1988) robustly supports a legitimacy and compliance benefit of fair process, but it measures perceived fairness, not reduction of the substantive decision harm redress is meant to cure.

    Sources: Bansal, Wu, Zhou, Fok, Nushi, Kamar, Ribeiro & Weld 2021 (CHI '21); Green & Chen 2019 (Disparate Interactions, ACM FAT* '19); Tyler 1990 (Why People Obey the Law, Yale Univ. Press); Lind & Tyler 1988 (The Social Psychology of Procedural Justice, Plenum Press)

• Transparency Obligations

  • Is the harm real?evidence: contested

    Documentation artifacts (model cards, datasheets) are well-specified as proposals and are genuinely adopted, but the empirical premise that mandated disclosure produces meaningful transparency is contested. Selbst & Barocas (2018) argue inscrutability and non-intuitiveness are distinct problems and that disclosing rules does not resolve the latter, and large-scale audits find documentation is sparsely and unevenly completed: a systematic analysis of 32,111 Hugging Face model cards (Liang et al. 2024) found environmental-impact, limitations and evaluation sections least often filled, and Bhat et al. (2023, 45 practitioners) found a substantial gap between the documentation proposal and actual practice. Honest caveat: the documentation frameworks themselves are real and adopted, so the dispute is about whether disclosure conveys decision-relevant information, not whether the artifacts exist.

    Sources: Selbst & Barocas 2018 (Fordham Law Review 87:1085-1139); Liang et al. 2024 (Nature Machine Intelligence, s42256-024-00857-z, 'Systematic analysis of 32,111 AI model cards'); Bhat et al. 2023 (CHI '23, 'Aspirations and Practice of ML Model Documentation', DOI 10.1145/3544548.3581518); Mitchell et al. 2019 (FAccT, Model Cards for Model Reporting); Gebru et al. 2021 (CACM 64(12):86-92, Datasheets for Datasets)

  • Does governance work?evidence: absent

    There is no rigorous impact evaluation showing that AI transparency mandates (model cards, training-data summaries) measurably reduce bias, misuse or accidents — the central regulatory assumption is empirically untested, partly because flagship mandates like EU AI Act Art. 53(1)(d) GPAI training-data summaries are only subject to AI Office enforcement/verification from 2 August 2026 (the obligation itself began 2 August 2025 for new models). The closest analogue, mandated consumer disclosure, shows small and context-dependent effects: Bollinger, Leslie & Sorensen (2011) found mandatory calorie posting cut average calories per transaction by about 6%, while Loewenstein, Sunstein & Golman (2014) review evidence that disclosure effects are frequently diminished or even reversed by limited attention and often change provider rather than recipient behavior. These are analogues, not AI studies; no study demonstrates that AI transparency disclosure achieves its stated downstream safety aims.

    Sources: Bollinger, Leslie & Sorensen 2011 (AEJ: Economic Policy 3(1):91-128); Loewenstein, Sunstein & Golman 2014 (Annual Review of Economics 6:391-419, 'Disclosure: Psychology Changes Everything'); EU AI Act Art. 53(1)(d) GPAI training-data summary (obligation from 2 Aug 2025; AI Office enforcement from 2 Aug 2026)

References

1. Directive (EU) 2024/2853 of the European Parliament and of the Council of 23 October 2024 on liability for defective products and repealing Council Directive 85/374/EEC, OJ L, 2024/2853, 18.11.2024 (CELEX:32024L2853; ELI:http://data.europa.eu/eli/dir/2024/2853/oj). Entered into force 18 November 2024; applies to products placed on the market or put into service after 9 December 2026 (Art. 2(1)).
2. Art. 9 — court-ordered disclosure of relevant evidence in the defendant's control, reinforced by the Art. 10(2)(a) adverse presumption for non-disclosure
3. Arts. 6, 8, 9, 10 — strict-liability compensation for defective products incl. software/AI: compensable damage (Art. 6), liable economic operators (Art. 8), court-ordered evidence disclosure (Art. 9), and rebuttable presumptions of defect + causation (Art. 10)
4. Art. 7(2)(c) — defectiveness accounts for a product's ability to continue to learn or acquire new features after market placement; Art. 11(2) — post-placement software-update liability within the manufacturer's control