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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 5·5 min read
Implementing Causal Perception: Competing SCMs and Situated Fairness
TL;DR
Causal perception, a framework for understanding how agents with differing Structural Causal Models (SCMs) perceive fairness, has been implemented for the first time. This implementation reveals that fairness assessments are sensitive to the choice of distance metrics and the agents' competing worldviews.
✦ Why It Matters
Evaluate your fairness metrics in decision-making systems to account for differing agent perspectives.
Key Takeaways
- Implement algorithms for interventional and counterfactual distributions.
- Assess fairness metrics based on agent-specific SCMs.
- Consider the impact of distance metrics on decision outcomes.
- Utilize the German Credit dataset for practical applications.
How It Works
The implementation of causal perception involves creating algorithms that can compute interventional and counterfactual distributions based on differing SCMs. By operationalizing both structural and parametric disagreements, the framework allows for a nuanced understanding of how agents perceive fairness in decision-making contexts.