Summary of How Should Ai Decisions Be Explained? Requirements For Explanations From the Perspective Of European Law, by Benjamin Fresz et al.
How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law
by Benjamin Fresz, Elena Dubovitskaya, Danilo Brajovic, Marco Huber, Christian Horz
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the connection between law and Explainable Artificial Intelligence (XAI), highlighting the need for XAI methods that meet specific legal requirements. The authors focus on European and German laws, as well as international regulations like the General Data Protection Regulation (GDPR) and product safety and liability standards. By deriving XAI requirements from legal bases using taxonomies, they conclude that each legal basis necessitates distinct XAI properties. Currently, XAI methods fall short of fulfilling these needs, particularly in terms of correctness and confidence estimates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at the link between law and Explainable Artificial Intelligence (XAI). It’s like trying to make sure AI can explain its decisions in a way that follows laws and regulations. The researchers focus on European and German laws, as well as some international rules. They want to know what kinds of XAI methods are needed to meet these legal requirements. Their main finding is that different laws need different types of XAI properties. Right now, AI explanations aren’t good enough at showing why they made certain decisions. |
