Fairness-aware machine learning: a perspective

08/02/2017
by   Indre Zliobaite, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/03/2020

FAE: A Fairness-Aware Ensemble Framework

Automated decision making based on big data and machine learning (ML) al...
research
05/17/2022

The Fairness of Machine Learning in Insurance: New Rags for an Old Man?

Since the beginning of their history, insurers have been known to use da...
research
10/01/2021

A survey on datasets for fairness-aware machine learning

As decision-making increasingly relies on machine learning and (big) dat...
research
05/04/2021

Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There?

The advent of powerful prediction algorithms led to increased automation...
research
02/13/2023

Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment

The presence of decision-making algorithms in society is rapidly increas...
research
07/30/2020

Visual Analysis of Discrimination in Machine Learning

The growing use of automated decision-making in critical applications, s...
research
10/18/2017

Themis-ml: A Fairness-aware Machine Learning Interface for End-to-end Discrimination Discovery and Mitigation

As more industries integrate machine learning into socially sensitive de...

Please sign up or login with your details

Forgot password? Click here to reset