Summary of Causal Responsibility Attribution For Human-ai Collaboration, by Yahang Qi et al.
Causal Responsibility Attribution for Human-AI Collaboration
by Yahang Qi, Bernhard Schölkopf, Zhijing Jin
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Applications (stat.AP)
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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 paper presents a novel approach to attributing responsibility in human-AI systems, which is crucial as AI increasingly influences decision-making across various fields. The existing attribution methods based on actual causality and Shapley values have limitations, as they tend to disproportionately blame agents who contribute more to an outcome. This paper develops a causal framework using Structural Causal Models (SCMs) to systematically attribute responsibility in human-AI systems, considering the overall blameworthiness while employing counterfactual reasoning to account for agents’ expected epistemic levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out who’s responsible when AI and humans work together. Right now, it’s hard to say because existing methods blame the person or AI system that had the most impact on the outcome. But this isn’t always fair. The researchers created a new way to look at how AIs and humans make decisions together using something called Structural Causal Models (SCMs). This framework helps us understand who’s really responsible by considering what each agent knew and didn’t know. |
