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Algorithmic Fairness
Dana Pessach,Erez Shmueli +1 more
TL;DR: An overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms is presented and the most commonly used fairness-related datasets in this field are described.
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Abstract: An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. This paper presents an overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, towards a better understanding of which mechanisms should be used in different scenarios. The paper then describes the most commonly used fairness-related datasets in this field. Finally, the paper ends by reviewing several emerging research sub-fields of algorithmic fairness.
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Figures
Table 1. Measures and Definitions for Algorithmic Fairness 
Table 4. Additional Measures and Definitions for Algorithmic Fairness 
Fig. 1. If the SAT scores were used for hiring, then unprivileged candidates with high potential would be excluded, whereas lower potential candidates from the privileged group would be hired instead 
Table 3. Common Benchmark Datasets for Algorithmic Fairness 
Table 2. Pre-Process, In-Process and Post-Process Mechanisms for Algorithmic Fairness
Citations
Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learning
TL;DR: In this paper , a multi-objective evolutionary learning framework is used to simultaneously optimise several metrics (including accuracy and multiple fairness measures) of machine learning models, and ensembles are constructed based on the learning models in order to automatically balance different metrics.
AI’s fairness problem: understanding wrongful discrimination in the context of automated decision-making
TL;DR: In this paper , the authors identify and propose three main guidelines to properly constrain the deployment of machine learning algorithms in society: algorithms should be vetted to ensure that they do not unduly affect historically marginalized groups; they should not systematically override or replace human decision-making processes; and the decision reached using an algorithm should always be explainable and justifiable.
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Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups
Amy A. Winecoff,Elizabeth Anne Watkins +1 more
- 02 Mar 2022
TL;DR: This paper conducted interviews with 23 entrepreneurs working at early-stage AI startups and found that actors within startups both conform to and resist institutional pressures, and that influential external stakeholders either lacked the technical knowledge to appreciate entrepreneurs' emphasis on rigor or focused on business priorities.
Applying Fairness Constraints on Graph Node Ranks Under Personalization Bias
Emmanouil Krasanakis,Symeon Papadopoulos,Ioannis Kompatsiaris +2 more
- 01 Dec 2020
TL;DR: In this article, the authors address algorithmic fairness concerns that arise when graph nodes are ranked based on their structural relatedness to a personalized set of query nodes, and introduce a personalization editing mechanism that helps ranking algorithms achieve different trade-offs between fairness constraints and rank changes.
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On the Advantages of Distinguishing Between Predictive and Allocative Fairness in Algorithmic Decision-Making
TL;DR: In this article , the authors argue that the problem of algorithmic fairness is typically framed as finding a unique formal criterion that guarantees that a given algorithmic decision-making procedure is morally permissible.
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