A Survey on Bias and Fairness in Machine Learning
Open accessAbstract
With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.
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One technical way to address bias is to use machine learning tools that are trained to detect and highlight potential biases.
“One technical way to address this issue is to use machine learning tools that are trained to detect and highlight potential biases (Mehrabi et al. 2019).”
From Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response (2022) · cites this paper as Mehrabi et al. 2019
Section 2.2, “Assessment Tools” (page unavailable), describes toolkits that test models for bias and fairness across population subgroups and produce reports, supporting the use of tools to identify potential bias. It does not say those assessment tools are machine-learning tools trained to detect bias, so that specific description is unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe effects of AI technologies on healthcare may vary significantly depending on patient demographics, underlying health conditions, and clinical environments.
“While AI technologies hold immense potential to transform healthcare by providing personalized and efficient care, their effects may vary significantly depending on patient demographics, underlying health conditions, and clinical environments.82–85”
From Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions (2026) · cites this paper as [83]
Section 3.2.2 says AI in healthcare has not affected all patients equally, and §3.1.1 describes how a diabetes model may be less suitable for some ethnic and gender groups. These passages support variation by patient demographics, but do not establish variation by underlying health conditions or clinical environments. Page numbers were not provided in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDifferent notions of fairness can be defined, including causal fairness which relies on causal relations and requires establishing causal graphs, or counterfactual fairness.
“Different notions of fairness can be also defined [78,90], including causal fairness which relies on causal relations and requires establishing causal graphs - or counterfactual fairness.”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [78]
Section 4.1 presents multiple definitions of fairness, and Section 5.2.6 describes causal approaches that use causal relationships and graphs; it also mentions counterfactual fairness. The survey does not say that every causal approach to fairness requires establishing a causal graph, so the categorical requirement is not supported. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAlgorithmic bias in hiring is real and largely traceable to data and design rather than malice.
“Taken together, this literature establishes two claims the remainder of the paper relies on: that algorithmic bias in hiring is real and largely traceable to data and design rather than malice, and that fixing it cannot be left to the technology alone.”
From Rejected by Code: Algorithmic Bias in AI-Driven Recruitment and the Case for HR-IT Governance Frameworks (2026) · cites this paper as [11]
Section 2.1 describes discriminatory delivery of STEM job ads despite a gender-neutral intention, and Section 3 explains how biased data and algorithm-design choices produce biased outcomes. These passages support bias in employment-related algorithms without malice, but they do not establish that hiring bias is largely attributable to data and design. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTraining data used to train an LLM may be drawn from a nonrepresentative sample of the population, which can cause the model to fail to generalize well to some social groups.
“Training Data: The data used to train an LLM may be drawn from a nonrepresentative sample of the population, which can cause the model to fail to generalize well to some social groups.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Mehrabi et al. 2021
Section 3.1.1 (page not provided) says nonrepresentative samples can omit subgroups and that sampling bias can prevent learned trends from generalizing to a new population. This supports the proposed mechanism, but the passages discuss machine-learning models generally, not LLMs specifically or their performance for particular social groups. (LLM, conf=0.84)
Experimental — accuracy not yet measured
AI models trained on historical data often exhibit inherent biases, resulting in discriminatory outcomes.
“AI models trained on historical data often exhibit inherent biases, resulting in discriminatory outcomes (Mehrabi et al., 2021).”
From Quantum technology for development framework as a tool for science diplomacy (2023) · cites this paper as Mehrabi et al., 2021
One of the main causes of undesirable learned patterns lies in biased training data.
“Yet, one of the main causes of undesirable learned patterns lies in biased training data55,56.”
From The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review (2024) · cites this paper as [56]
Biases leading to unfair decisions can be propagated from the data to the AI algorithm, from the algorithm to the user, or from the user to the data.
“biases leading to such unfair decisions can be propagated from the data to the AI algorithm (including measurement, omitted variable sampling, or representation biases, among others); from the algorithm to the user (as in algorithmic, popularity or evaluation biases); or from the user to the data (respectively, biases induced in the production of content, temporal, historical and/or social biases)”
From Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023) · cites this paper as [78]
Training data may omit important contexts, and proxies used as labels may incorrectly measure the actual outcome of interest.
“The data may omit important contexts, and proxies used as labels ( e.g. , sentiment) may incorrectly measure the actual outcome of interest ( e.g. , representational harms).”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Mehrabi et al. 2021
Bias emerges at points within the model development and deployment process, which may exacerbate preexisting historical biases.
“Another way of understanding social bias in LLMs is to examine at which points within the model development and deployment process the bias emerges, which may exacerbate preexisting historical biases. This has been thoroughly explored by Mehrabi et al. (2021), Shah, Schwartz, and Hovy (2020), and Suresh and Guttag (2021), and we summarize these pathways here:”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Mehrabi et al. 2021
The aggregation of data may obscure distinct social groups that should be treated differently.
“The aggregation of data may also obscure distinct social groups that should be treated differently, causing the model”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Mehrabi et al. 2021
Less has been done in the NLP space on fairness desiderata than in machine learning classification tasks, and NLP may contain more ambiguity than classification for tasks like language generation.
“While similar concepts have been operationalized for machine learning classification tasks (Mehrabi et al. 2021; Verma and Rubin 2018), less has been done in the NLP space, which may contain more ambiguity than classification for tasks like language generation.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Mehrabi et al. 2021
LLMs and other AI tools may have a number of biases that could arise from various factors, such as unbalanced training datasets.
“LLMs and other AI tools may have a number of biases that could arise from various factors, such as unbalanced training datasets74–77.”
From On opportunities and challenges of large multimodal foundation models in education (2025) · cites this paper as [74]
Bias may be derived from data, algorithms, and user interactions.
“Notably, the bias may be derived from data, algorithms, and user interactions [24].”
From Survey of Explainable AI Techniques in Healthcare (2023) · cites this paper as [24]
The UK Biobank exhibits a "healthy volunteer" selection bias, where participants are generally healthy and therefore do not represent patients typically encountered by healthcare systems.
“An example of this is the “healthy volunteer” selection bias observed in the UK Biobank, where participants are generally healthy and therefore do not represent patients typically encountered by healthcare systems39.”
From Bias recognition and mitigation strategies in artificial intelligence healthcare applications (2025) · cites this paper as [39]
Authentically engaging and partnering with patients and communities is essential to understand both a problem affecting them and its solutions.
“Authentically engaging and partnering with patients and communities is essential to understand both a problem affecting them and its solutions.22”
From Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care (2023) · cites this paper as [22]
In the medical field, bias and discrimination in AI have been studied in various domains.
“In the medical field, bias and discrimination in AI have been studied in various domains [11, 12].”
From Fairness of artificial intelligence in healthcare: review and recommendations (2024) · cites this paper as [12]
Data scientists and programmers treat AI biases as bugs, i.e., technical issues like security to be fixed.
“On the other hand, it is the approach by data scientists and programmers that characterise AI biases as bugs implying that it is just a technical issue like security that needs to be fixed [Tramer et al. 2016, 53, 54].”
Existing reviews differ in scope, depth, and focus, leaving a gap in understanding how bias permeates the development of AI for clinical decisions.
“While these reviews have made valuable contributions to our understanding of bias in medical AI, they differ in scope, depth, and focus, leaving a gap in understanding how bias permeates the development of AI for clinical decisions.”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [14]
Bias in data is one of the major sources of biased algorithmic outcome.
“Bias in data is one of the major source of biased algorithmic outcome. 18,19”
From AI pitfalls and what not to do: mitigating bias in AI (2023) · cites this paper as [18]
Strategies at the data processing stage may include data augmentation or data filtering to create more representative training datasets.
“Apart from collecting more data, strategies at the data processing stage may include the creation of more representative training datasets by data augmentation (e.g., by specifically adding underrepresented examples to the data through additional sampling or data generation) or data filtering (e.g., actively undersampling or filtering out undesirable or non-representative samples).68”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [68]
AI systems may transport various types of bias stemming from their underlying training data.
“AI systems may transport various types of bias stemming from their underlying training data.68, 69”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [68]
The way data are presented to the model during training and how model parameters are updated can introduce bias into the model.
“The way data are presented to the model during training (affected by the problem formulation and the labeling methodology) and how model parameters are updated (defined through training setup including the objective function) can introduce bias into the model.13, 68”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [68]
AI systems may transport various types of bias stemming from their underlying training data.
“AI systems may transport various types of bias stemming from their underlying training data.68, 69”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [69]
Strategies such as robust data augmentation, counterfactual fairness, and diverse representative datasets alongside unbiased data collection methods can combat bias.
“To combat bias, this paper has highlighted strategies such as robust data augmentation, the application of counterfactual fairness, and the imperative for diverse, representative datasets alongside unbiased data collection methods [49][50][51].”
From Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (2023) · cites this paper as [50]
At least some of the considered "correct answers" may correspond to robustly learned biases that may influence the model's answers to a wide variety of realistic prompts, even when the model is asked to respond in demographically varying roles.
“We argue that at least some of these “correct answers” may be more likely to correspond to robustly learned biases (Mehrabi et al., 2021): biases that may conceptually influence the model’s answers to a wide variety of realistic prompts, even if the model is asked to respond in a myriad of demographically varying roles.”
From Diminished diversity-of-thought in a standard large language model (2024) · cites this paper as Mehrabi et al., 2021
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Trustworthy artificial intelligence
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- Authors
- Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A.
- Affiliations
- USC-ISI
- Venue
- ACM Computing Surveys
- Published
- 2021-07-13
- Volume
- 54
- Issue
- 6
- Pages
- 1-35
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2969896603
- Cited by
- 5,812
- References
- 152
- Citations by year
- 2026: 1254 · 2025: 1532 · 2024: 1035 · 2023: 892 · 2022: 602 · 2021: 367 · 2020: 121 · 2019: 7 · 2018: 1
- FWCI
- 905.17
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Computing Surveys (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Defense Advanced Research Projects Agency (Agreement No. HR0011890019.)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Discrimination and Equality Law
- Keywords
- algorithmic fairness, machine learning bias, discrimination, fairness definitions, bias mitigation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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