{"id":2481,"date":"2025-04-01T21:07:02","date_gmt":"2025-04-02T05:07:02","guid":{"rendered":"https:\/\/blog.mozilla.org\/netpolicy\/?p=2481"},"modified":"2025-04-01T22:10:02","modified_gmt":"2025-04-02T06:10:02","slug":"new-mozilla-research-civil-liability-along-the-ai-value-chain","status":"publish","type":"post","link":"https:\/\/blog.mozilla.org\/netpolicy\/2025\/04\/01\/new-mozilla-research-civil-liability-along-the-ai-value-chain\/","title":{"rendered":"New Mozilla Research: Civil Liability Along the AI Value Chain"},"content":{"rendered":"

What happens when AI systems fail? Who should be held responsible when they cause harm? And how can we ensure that people harmed by AI can seek redress?<\/em><\/strong><\/p>\n

READ THE REPORT HERE<\/a><\/strong><\/p>\n

As AI is increasingly integrated in products and services across sectors, these questions will only become more pertinent. In the EU, a proposal for an AI Liability Directive (AILD) in 2022 catalyzed debates around this issue.\u00a0 Its recent withdrawal by the European Commission leaves a wide range of open questions to linger as businesses and consumers will need to navigate fragmented liability rules across the EU\u2019s 27 member states.<\/p>\n

To answer these questions, policymakers will need to ask themselves: what does an effective approach to AI and liability look like?<\/p>\n

New research published by Mozilla tackles these thorny issues and explores how liability could and should be assigned across AI\u2019s complex and heterogeneous value chain.<\/p>\n

Solving AI\u2019s \u201cproblem of many hands\u201d\u00a0<\/b><\/p>\n

The report<\/a><\/strong>, commissioned from Beatriz Botero Arcila \u2014 a professor at Sciences Po Law School and a Faculty Associate at Harvard\u2019s Berkman Klein Center for Internet and Society \u2014 explores how liability law can help solve the \u201cproblem of many hands\u201d in AI: that is, determining who is responsible for harm that has been dealt in a value chain in which a variety of different companies and actors might be contributing to the development of any given AI system. This is aggravated by the fact that AI systems are both opaque and technically complex, making their behavior hard to predict.<\/p>\n

Why AI Liability Matters<\/b><\/p>\n

To find meaningful solutions to this problem, different kinds of experts have to come together. This resource is designed for a wide audience, but we indicate how specific audiences can best make use of different sections, overviews, and case studies.<\/p>\n

Specifically, the report:<\/p>\n