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Papers on “algorithmic bias fairness machine learning”

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1. Greedy function approximation: A gradient boosting machine.

   Jerome H. Friedman · 2001 · The Annals of Statistics · 30,508 cites

   Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent minimization. A general gradient descent “boosting” paradigm is developed for additive expansions based on any fitting criterion.Specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification. Special enhancements are derived for the particular case where the individual additive components are regression trees, and tools for interpreting such “TreeBoost” models are

2. Deep Reinforcement Learning with Double Q-Learning

   Hado van Hasselt, Arthur Guez, David Silver · 2016 · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · 6,318 cites

   The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q-learning with a deep neural network, suffers from substantial overestimations in some games in the Atari 2600 domain. We then show that the idea behind the Double Q-learning algorithm, which was introduced in a tabular setting, can be generalized to work with large-scale function approximation. We propose

3. Advances and Open Problems in Federated Learning

   Peter Kairouz, H. Brendan McMahan, Brendan Avent, et al. · 2020 · Foundations and Trends® in Machine Learning · 5,495 cites

   Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this monograph discusses recent advances and presents an extensive collection of open problems and challenges.

4. Towards A Rigorous Science of Interpretable Machine Learning

   Finale Doshi‐Velez, Been Kim · 2017 · arXiv (Cornell University) · 3,197 cites

   As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpretability, there is very little consensus on what interpretable machine learning is and how it should be measured. In this position paper, we first define interpretability and describe when interpretability is needed (and when it is not). Next, we suggest a taxonomy for rigorous evaluation and expose open questions towards a more rigorous science of interpretable machine learni

5. A guide to machine learning for biologists

   Joe G. Greener, Shaun M. Kandathil, Lewis Moffat, et al. · 2021 · Nature Reviews Molecular Cell Biology · 2,184 cites

6. AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

   Rachel Bellamy, Kuntal Dey, Michael Hind, et al. · 2019 · IBM Journal of Research and Development · 870 cites

   Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and prison sentencing. This article introduces a new open-source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license (https://github.com/ibm/aif360). The main objectives of this toolkit are to help facilitate the transition of fairness research algorithms for use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithms. The package includes a comprehensive set of fairness metrics for datasets and models, explanat

7. Algorithmic Bias in Education

   Ryan S. Baker, Aaron Hawn · 2021 · International Journal of Artificial Intelligence in Education · 813 cites

   In this paper, we review algorithmic bias in education, discussing the causes of that bias and reviewing the empirical literature on the specific ways that algorithmic bias is known to have manifested in education. While other recent work has reviewed mathematical definitions of fairness and expanded algorithmic approaches to reducing bias, our review focuses instead on solidifying the current understanding of the concrete impacts of algorithmic bias in education—which groups are known to be impacted and which stages and agents in the development and deployment of educational algorithms are implicated. We discuss theoretical and formal perspectives on algorithmic bias, connect those perspect

8. Algorithmic fairness in artificial intelligence for medicine and healthcare

   Richard J. Chen, Judy Jiaqi Wang, Drew F. K. Williamson, et al. · 2023 · Nature Biomedical Engineering · 711 cites