Summary of Attributing Responsibility in Ai-induced Incidents: a Computational Reflective Equilibrium Framework For Accountability, by Yunfei Ge and Quanyan Zhu
Attributing Responsibility in AI-Induced Incidents: A Computational Reflective Equilibrium Framework for Accountability
by Yunfei Ge, Quanyan Zhu
First submitted to arxiv on: 25 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 proposed Computational Reflective Equilibrium (CRE) approach aims to establish a coherent and ethically acceptable responsibility attribution framework for stakeholders in AI-enabled systems. This is crucial due to the complex challenges in attributing responsibility when incidents occur. The CRE method provides a structured analysis, overcoming limitations of conceptual approaches in handling dynamic scenarios. The framework showcases explainability, coherence, and adaptivity properties during the responsibility attribution process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI is changing how we make decisions, but it also raises questions about who’s responsible when things go wrong. Researchers have created a new way to figure out where blame lies using Artificial Intelligence. This system helps doctors make better choices, but what happens if something goes wrong? The new approach uses math and computer science to decide who’s at fault. It shows that different starting points can lead to different answers. |
