# Fairness without Regret

## TL;DR

This paper proposes a flexible optimization framework that generates a set of solutions by varying parameters, enabling the balancing of fairness and primary objectives without sacrificing solution quality.

## Contribution

It introduces a parametrized objective approach that allows for fairness considerations without regret, addressing limitations of traditional constrained fairness methods.

## Key findings

- Provides a method to generate multiple optimal solutions with varied fairness levels.
- Enables secondary optimization for fairness without degrading primary objectives.
- Offers a practical approach to balancing fairness and efficiency in optimization problems.

## Abstract

A popular approach of achieving fairness in optimization problems is by constraining the solution space to "fair" solutions, which unfortunately typically reduces solution quality. In practice, the ultimate goal is often an aggregate of sub-goals without a unique or best way of combining them or which is otherwise only partially known. I turn this problem into a feature and suggest to use a parametrized objective and vary the parameters within reasonable ranges to get a "set" of optimal solutions, which can then be optimized using secondary criteria such as fairness without compromising the primary objective, i.e. without regret (societal cost).