🤖 AI Summary
This work addresses the challenge that multiple agents in a shared system may hold divergent structural causal models (SCMs), leading to inconsistent judgments of fairness due to disagreements over interventional and counterfactual distributions. The paper introduces the first causally aware framework that distinguishes between structural and parametric causal awareness, proposes algorithms to compute interventional and counterfactual distributions, and employs probabilistic distance measures—such as KL divergence—to quantify discrepancies among agents. Experiments on the German Credit dataset demonstrate that causal awareness significantly influences both accuracy and fairness evaluations, with results highly sensitive to the choice of distance metric and decision threshold. These findings reveal the context-dependent nature of bias, challenge the assumption of a single objective fairness standard, and underscore the necessity of multi-perspective fairness analysis.
📝 Abstract
Causal perception occurs when agents with competing Structural Causal Models (SCMs) of the same system infer different probability distributions, including the hypothetical distributions implied by each agent's SCM under the same set of interventions. It shapes how agents reason about the system and how they perceive its fairness. Causal perception is a promising probabilistic framework, but it has remained purely theoretical. This work provides the first implementation of the causal perception framework of Álvarez and Ruggieri (2025). We operationalize structural (agents disagree on the causal graph) and parametrical (agents agree on the causal graph but disagree on its weights) causal perception. We design algorithms for computing interventional and counterfactual distributions and propose suitable distance measures to quantify the disagreement. Using the German Credit dataset, we illustrate how causal perception affects accuracy and fairness in a multi-expert decision setting. We show that the perception verdict is sensitive to the choice of distance metric and threshold. We also show that causal perception changes fairness assessments and threshold-based decisions. Bias proves situated with respect to the agent's SCM, demonstrating that competing worldviews in fairness problems cannot be ignored.