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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
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
Vertical Federated Learning: Taxonomies, Threats, and Prospects
Qun Li,Chandra Thapa,Lawrence Ong,Yefeng Zheng,Hua Ma,Seyit Camtepe,Anmin Fu,Yan Gao +7 more
TL;DR: Federated learning (FL) is the most popular distributed machine learning technique as mentioned in this paper , which allows machine-learning models to be trained without acquiring raw data to a single point for processing.
Applications and Challenges of Fairness APIs in Machine Learning Software
Ajoy Das,Gias Uddin,Shaiful Chowdhury,Mostafijur Rahman Akhond,Hadi Hemmati +4 more
TL;DR: This qualitative study examines the use and challenges of 13 open-source fairness APIs in machine learning software, analyzing 204 GitHub repositories and identifying 17 unique use-cases, highlighting developers' knowledge gaps and troubleshooting issues.
Fair MP-BOOST: Fair and Interpretable Minipatch Boosting
Camille Olivia Little,Genevera I. Allen +1 more
- 01 Apr 2024
TL;DR: Fair MP-Boost is a fair and interpretable boosting scheme that enhances fairness and accuracy by adaptively learning features and observations.
Feasibility of continuous fever monitoring using wearable devices.
Benjamin L. Smarr,Kirstin Aschbacher,Sarah M. Fisher,Anoushka Chowdhary,Stephan Dilchert,Karena Puldon,Adam Rao,Frederick Hecht,Ashley E. Mason +8 more
TL;DR: Findings from the first 50 subjects who reported COVID-19 infections provide the first evidence that illness-associated elevations in peripheral temperature are observable using wearable devices and correlate with self-reported fever, and support the hypothesis that prediction of illness onset is possible using continuously generated physiological data collected by wearable sensors.
Framework for developing algorithmic fairness
Dedy Prasetya Kristiadi,Po Abas Sunarya,Melvin Ismanto,Joshua Dylan,Ignasius Raffael Santoso,Harco Leslie Hendric Spits Warnars +5 more
TL;DR: A framework for defining a fair algorithm metric is proposed by compiling information and propositions from various papers into a single summarized list of fairness requirements (guideline alike) so that the researcher can adopt it as a foundation or reference to aid them in developing their interpretation of algorithmic fairness.
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).