Effort-aware Fairness: Incorporating a Philosophy-informed, Human-centered Notion of Effort into Algorithmic Fairness Metrics
Journal:
arXiv
Published Date:
May 25, 2025
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 way to conceptualize and
evaluate Effort-aware Fairness (EaF) based on the concept of Force, or temporal
trajectory of predictive features coupled with inertia. In addition to our
theoretical formulation of EaF metrics, our empirical contributions include: 1/
a pre-registered human subjects experiment, which demonstrates 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 spent
significant efforts to improve but are still stuck with systemic/early-life
disadvantages outside their control.
