Open AccessPosted Content
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
Adaptive Priority Reweighing for Generalizing Fairness Improvement
Zhihao Hu,Yiran Xu,Xinmei Tian +2 more
- 18 Jun 2023
TL;DR: Adaptive priority reweighing method improves the generalizability of fair classifiers by assigning higher weights to samples closer to the decision boundary in each group.
What to expect from opening up ‘black boxes’? Comparing perceptions of justice between human and automated agents
Nadine Schlicker,Nadine Schlicker,Markus Langer,Sonja Kristine Ötting,Kevin Baum,Cornelius J. König,Dieter Wallach +6 more
TL;DR: This study compares effects of decision agents and explanations for decisions on decision-recipients’ perceptions of justice in a healthcare-scheduling context and finds that perceptions of informational justice were impaired only for the human decision agent.
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Machine learning in the service of policy targeting: the case of public credit guarantees
Monica Andini,Michela Boldrini,Emanuele Ciani,Guido de Blasio,Alessio D'Ignazio,Andrea Paladini +5 more
TL;DR: This work uses Machine Learning (ML) predictive tools to propose a policy-assignment rule designed to increase the effectiveness of public guarantee programs, and suggests a benchmark ML-based assignment rule, trained and tested on credit register data.
Understanding Fairness Surrogate Functions in Algorithmic Fairness
Wei Yao,Zhanke Zhou,Zhicong Li,Bo Han,Yong Liu +4 more
TL;DR: This work shows that there is a surrogate-fairness gap between the fairness definition and the fairness surrogate function, and proposes the general sigmoid surrogate to simultaneously reduce both the surrogate- Fairness gap and the variance, and offers a rigorous fairness and stability upper bound.
References
ImageNet: A large-scale hierarchical image database
Jia Deng,Wei Dong,Richard Socher,Li-Jia Li,Kai Li,Li Fei-Fei +5 more
- 20 Jun 2009
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
- 08 Dec 2014
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Glove: Global Vectors for Word Representation
Jeffrey Pennington,Richard Socher,Christopher D. Manning +2 more
- 01 Oct 2014
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
A kernel two-sample test
TL;DR: This work proposes a framework for analyzing and comparing distributions, which is used to construct statistical tests to determine if two samples are drawn from different distributions, and presents two distribution free tests based on large deviation bounds for the maximum mean discrepancy (MMD).