# Fairness-aware machine learning: a perspective

arXiv: https://arxiv.org/abs/1708.00754 · HTML: https://mlsanity.com/paper/1708.00754/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 3 of 3)

- [Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems](https://mlsanity.com/paper/2201.06952/) (2022)
- [Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models](https://mlsanity.com/paper/2109.12422/) (2021)
- [Using Pareto Simulated Annealing to Address Algorithmic Bias in Machine Learning](https://mlsanity.com/paper/2105.15064/) (2021)

## Abstract (arXiv)

Algorithms learned from data are increasingly used for deciding many aspects in our life: from movies we see, to prices we pay, or medicine we get. Yet there is growing evidence that decision making by inappropriately trained algorithms may unintentionally discriminate people. For example, in automated matching of candidate CVs with job descriptions, algorithms may capture and propagate ethnicity related biases. Several repairs for selected algorithms have already been proposed, but the underlying mechanisms how such discrimination happens from the computational perspective are not yet scientifically understood. We need to develop theoretical understanding how algorithms may become discriminatory, and establish fundamental machine learning principles for prevention. We need to analyze machine learning process as a whole to systematically explain the roots of discrimination occurrence, which will allow to devise global machine learning optimization criteria for guaranteed prevention, as opposed to pushing empirical constraints into existing algorithms case-by-case. As a result, the state-of-the-art will advance from heuristic repairing, to proactive and theoretically supported prevention. This is needed not only because law requires to protect vulnerable people. Penetration of big data initiatives will only increase, and computer science needs to provide solid explanations and accountability to the public, before public concerns lead to unnecessarily restrictive regulations against machine learning.