Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges

by   Songül Tolan, et al.

Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human lives, such as credit lending or criminal justice. This is driven by the idea that `objective' machines base their decisions solely on facts and remain unaffected by human cognitive biases, discriminatory tendencies or emotions. Yet, there is overwhelming evidence showing that algorithms can inherit or even perpetuate human biases in their decision making when they are based on data that contains biased human decisions. This has led to a call for fairness-aware machine learning. However, fairness is a complex concept which is also reflected in the attempts to formalize fairness for algorithmic decision making. Statistical formalizations of fairness lead to a long list of criteria that are each flawed (or harmful even) in different contexts. Moreover, inherent tradeoffs in these criteria make it impossible to unify them in one general framework. Thus, fairness constraints in algorithms have to be specific to the domains to which the algorithms are applied. In the future, research in algorithmic decision making systems should be aware of data and developer biases and add a focus on transparency to facilitate regular fairness audits.



page 1

page 25


The FairCeptron: A Framework for Measuring Human Perceptions of Algorithmic Fairness

Measures of algorithmic fairness often do not account for human percepti...

Towards a Fairness-Aware Scoring System for Algorithmic Decision-Making

Scoring systems, as simple classification models, have significant advan...

Social Bias Meets Data Bias: The Impacts of Labeling and Measurement Errors on Fairness Criteria

Although many fairness criteria have been proposed to ensure that machin...

Algorithmic Fairness Datasets: the Story so Far

Data-driven algorithms are being studied and deployed in diverse domains...

Statistical Equity: A Fairness Classification Objective

Machine learning systems have been shown to propagate the societal error...

On the Fairness of Machine-Assisted Human Decisions

When machine-learning algorithms are deployed in high-stakes decisions, ...

Fairness in Credit Scoring: Assessment, Implementation and Profit Implications

The rise of algorithmic decision-making has spawned much research on fai...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.