SCALES: From Fairness Principles to Constrained Decision-Making

Fairness principles can be systematically translated into actionable rules: SCALES provides a structured method to convert abstract fairness concepts (like equal opportunity or demographic parity) into concrete constraints that AI systems can actually follow, bridging the gap between fairness theory and practice.

The framework addresses both how decisions are made and what outcomes result: Unlike approaches that focus on only one dimension, SCALES can enforce fairness in the decision-making process itself (procedural fairness) as well as in the final results (outcome fairness), offering more comprehensive protection against bias.

Real-world validation shows practical applicability: Testing on the COMPAS criminal risk assessment dataset—a widely-studied real-world system known for bias issues—demonstrates that SCALES can produce fairer policies in actual decision-making contexts, not just theoretical scenarios.

The framework handles complex, sequential decisions: Beyond one-time decisions, SCALES works for ongoing decision-making scenarios (like healthcare treatment over time), which is important since many high-stakes AI systems make multiple interconnected decisions affecting the same individuals.

Causal reasoning strengthens fairness implementation: By incorporating causal language, the framework can distinguish between correlation and causation when identifying sources of unfairness, potentially preventing superficial "fixes" that don't address root causes of bias.