Effort-aware Fairness: Incorporating a Philosophy-informed, Human-centered Notion of Effort into Algorithmic Fairness Metrics

TL;DR

This paper introduces a new fairness metric for AI that considers the effort individuals put into their features, grounded in philosophical concepts, supported by human experiments and practical fairness pipelines.

Contribution

It proposes a philosophy-informed Effort-aware Fairness metric, integrating temporal feature trajectories and inertia, with empirical validation and application pipelines.

Findings

People prioritize feature trajectories over aggregate values in fairness judgments.

Effort-aware fairness can reveal systemic disadvantages not captured by traditional metrics.

The approach enables more equitable AI decisions considering individual effort.

Abstract

Although popularized AI fairness metrics, e.g., demographic parity, have uncovered bias in AI-assisted decision-making outcomes, they do not consider how much effort one has spent to get to where one is today in the input feature space. However, the notion of effort is important in how Philosophy and humans understand fairness. We propose a philosophy-informed approach to conceptualize and evaluate Effort-aware Fairness (EaF), grounded in the concept of Force, which represents the temporal trajectory of predictive features coupled with inertia. Besides theoretical formulation, our empirical contributions include: (1) a pre-registered human subjects experiment, which shows that for both stages of the (individual) fairness evaluation process, people consider the temporal trajectory of a predictive feature more than its aggregate value; (2) pipelines to compute Effort-aware Individual/Group Fairness in the criminal justice and personal finance contexts. Our work may enable AI model auditors to uncover and potentially correct unfair decisions against individuals who have spent significant efforts to improve but are still stuck with systemic disadvantages outside their control.