Preventing Disparities: Bayesian and Frequentist Methods for Assessing Fairness in Machine-Learning Decision-Support Models

McNair, Douglas S.

doi:10.5772/intechopen.73176

Cited by 3 publications

(1 citation statement)

References 87 publications

(80 reference statements)

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“…Recent research pointed to the need to pay attention to bias and fairness in machine learning [101][102][103][104][105]. Some research has studied different forms of biases, some are due to the algorithms while others are due to inherent biases in the input data or in the interaction between data and algorithms [85,97,[106][107][108][109][110][111][112][113].…”

Section: Plos Onementioning

confidence: 99%

Evolution and impact of bias in human and machine learning algorithm interaction

2020

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Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions. More recently however, algorithms have been receiving data from the general population in the form of labeling, annotations, etc. The result is that algorithms are subject to bias that is born from ingesting unchecked information, such as biased samples and biased labels. Furthermore, people and algorithms are increasingly engaged in interactive processes wherein neither the human nor the algorithms receive unbiased data. Algorithms can also make biased predictions, leading to what is now known as algorithmic bias. On the other hand, human's reaction to the output of machine learning methods with algorithmic bias worsen the situations by making decision based on biased information, which will probably be consumed by algorithms later. Some recent research has focused on the ethical and moral implication of machine learning algorithmic bias on society. However, most research has so far treated algorithmic bias as a static factor, which fails to capture the dynamic and iterative properties of bias. We argue that algorithmic bias interacts with humans in an iterative manner, which has a long-term effect on algorithms' performance. For this purpose, we present an iterated-learning framework that is inspired from human language evolution to study the interaction between machine learning algorithms and humans. Our goal is to study two sources of bias that interact: the process by which people select information to label (human action); and the process by which an algorithm selects the subset of information to present to people (iterated algorithmic bias mode). We investigate three forms of iterated algorithmic bias (personalization filter, active learning, and random) and how they affect the performance of machine learning algorithms by formulating research questions about the impact of each type of bias. Based on statistical analyses of the results of several controlled experiments, we found that the three different iterated bias modes, as well as initial training data class imbalance and human action, do affect the models learned by machine learning algorithms. We also found that iterated filter bias, which is prominent in personalized user interfaces, can lead to more inequality in estimated relevance and to a limited human ability to discover relevant data. Our findings indicate that the relevance blind spot (items from the testing set whose predicted relevance probability is less than 0.5 and who thus risk being hidden from humans) amounted to 4% of all relevant items when using a content-based filter

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Section: Plos Onementioning

confidence: 99%

Evolution and impact of bias in human and machine learning algorithm interaction

2020

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Evaluation Gaps in Machine Learning Practice

Hutchinson¹,

Rostamzadeh

Greer

et al. 2022

2022 ACM Conference on Fairness, Accountability, and Transparency

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Fairness in Machine Learning: A Survey

Caton,

Haas

2024

ACM Comput. Surv.

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When Machine Learning technologies are used in contexts that affect citizens, companies as well as researchers need to be confident that there will not be any unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on approaches to mitigate bias and promote fairness, yet the area is complex and hard to penetrate for newcomers to the domain. This article seeks to provide an overview of the different schools of thought and approaches that aim to increase the fairness of Machine Learning. It organises approaches into the widely accepted framework of pre-processing, in-processing, and post-processing methods, subcategorizing into a further 11 method areas. Although much of the literature emphasizes binary classification, a discussion of fairness in regression, recommender systems, and unsupervised learning is also provided along with a selection of currently available open source libraries. The article concludes by summarising open challenges articulated as five dilemmas for fairness research.

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Preventing Disparities: Bayesian and Frequentist Methods for Assessing Fairness in Machine-Learning Decision-Support Models

Cited by 3 publications

References 87 publications

Evolution and impact of bias in human and machine learning algorithm interaction

Evolution and impact of bias in human and machine learning algorithm interaction

Evaluation Gaps in Machine Learning Practice

Fairness in Machine Learning: A Survey

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