# An Algorithmic Framework for Fairness Elicitation

## TL;DR

This paper introduces a flexible algorithmic framework for eliciting complex fairness constraints from stakeholders and integrating them into model training, with theoretical guarantees and preliminary behavioral study results.

## Contribution

It presents a provably convergent algorithm for learning models under elicited fairness constraints, accommodating nuanced fairness notions beyond traditional definitions.

## Key findings

- Algorithm is provably convergent and oracle efficient.
- Framework can incorporate traditional and elicited fairness constraints.
- Preliminary behavioral study on COMPAS dataset supports feasibility.

## Abstract

We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders. We introduce a framework in which pairs of individuals can be identified as requiring (approximately) equal treatment under a learned model, or requiring ordered treatment such as "applicant Alice should be at least as likely to receive a loan as applicant Bob". We provide a provably convergent and oracle efficient algorithm for learning the most accurate model subject to the elicited fairness constraints, and prove generalization bounds for both accuracy and fairness. This algorithm can also combine the elicited constraints with traditional statistical fairness notions, thus "correcting" or modifying the latter by the former. We report preliminary findings of a behavioral study of our framework using human-subject fairness constraints elicited on the COMPAS criminal recidivism dataset.