Cited by 875Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)AI and HR Technologies

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Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies

Open access

Ferrara, Emilio·2023·Sci

DOI 10.3390/sci6010003

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Abstract

The significant advancements in applying artificial intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems. This is particularly critical in areas like healthcare, employment, criminal justice, credit scoring, and increasingly, in generative AI models (GenAI) that produce synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including generative biases that affect the representation of individuals in synthetic data. This survey study offers a succinct, comprehensive overview of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies. We review sources of bias, such as data, algorithm, and human decision biases—highlighting the emergent issue of generative AI bias, where models may reproduce and amplify societal stereotypes. We assess the societal impact of biased AI systems, focusing on perpetuating inequalities and reinforcing harmful stereotypes, especially as generative AI becomes more prevalent in creating content that influences public perception. We explore various proposed mitigation strategies, discuss the ethical considerations of their implementation, and emphasize the need for interdisciplinary collaboration to ensure effectiveness. Through a systematic literature review spanning multiple academic disciplines, we present definitions of AI bias and its different types, including a detailed look at generative AI bias. We discuss the negative impacts of AI bias on individuals and society and provide an overview of current approaches to mitigate AI bias, including data pre-processing, model selection, and post-processing. We emphasize the unique challenges presented by generative AI models and the importance of strategies specifically tailored to address these. Addressing bias in AI requires a holistic approach involving diverse and representative datasets, enhanced transparency and accountability in AI systems, and the exploration of alternative AI paradigms that prioritize fairness and ethical considerations. This survey contributes to the ongoing discussion on developing fair and unbiased AI systems by providing an overview of the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the emerging field of generative AI.

AI snapshot

Written from the paper’s full text.

TL;DR

This survey reviews sources, impacts, and mitigation strategies for fairness and bias in AI, with particular attention to generative AI bias. It argues that bias arises from data, algorithms, and users, can perpetuate inequality, and requires holistic, interdisciplinary mitigation.

Key findings

Bias in AI is defined as systematic error in decision-making that leads to unfair outcomes; sources include data bias, algorithmic bias, and user bias, with generative bias as an emergent issue in GenAI outputs.
Real-world examples cited include COMPAS being biased against African-American defendants, a healthcare mortality algorithm biased against African-American patients, NIST finding facial recognition less accurate for darker skin tones, and text-to-image models (StableDiffusion, DALL-E, Midjourney) predominantly generating men for CEOs and people of color for criminals or terrorists.
Mitigation approaches reviewed include pre-processing data (oversampling, undersampling, synthetic data, adversarial debiasing), model selection (group fairness, individual fairness, regularization, ensemble methods), and post-processing (e.g., equalized odds); GenAI is said to require holistic strategies, audits, monitoring, and feedback loops.
Fairness types discussed include group fairness (demographic parity, disparate mistreatment, equal opportunity), individual fairness, counterfactual fairness, procedural fairness, and causal fairness; the paper distinguishes fairness as a deliberate goal from bias, which can be unintentional.

Methods

Design: a survey described as a systematic literature review spanning multiple academic disciplines, providing an overview of AI bias sources, impacts, and mitigation strategies.
Data or sample: no primary dataset or sample is analyzed; the paper draws on prior literature and illustrative examples from healthcare, criminal justice, hiring, credit scoring, facial recognition, and generative AI.
Main analysis: narrative synthesis, with tables characterizing types of AI bias, mitigation approaches and challenges, and definitions of fairness; no quantitative meta-analysis or formal search protocol is reported.

Limitations

Stated: lack of diverse and representative training data; difficulty identifying and measuring different types of bias; trade-offs between fairness and accuracy; ethical questions about prioritizing types of bias or groups; and limitations of mitigation methods, including pre-processing being time-consuming or ineffective if data are already biased, model selection lacking consensus on fairness, and post-processing being complex and data-intensive.
Stated: fairness definitions vary and may conflict; group and individual fairness approaches have trade-offs; fairness metrics may not capture intersectionality; and mitigation attempts can have unintended consequences, such as predictive policing efforts potentially increasing racial disparities in arrests.
Evident: as a brief survey, the paper does not report a systematic search strategy, inclusion/exclusion criteria, or quantitative synthesis; its examples are illustrative rather than exhaustive.

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Integrity signals

Registry

Crossref · 10.3390/sci6010003

Cites

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Statistics

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Claims the paper makes

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In the paper
“Biased algorithms can lead to the underrepresentation of certain groups, such as people of color or those from lower socioeconomic backgrounds, in credit scoring systems, making it harder for them to access loans or mortgages [25].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 3.1. Negative Impacts of Bias in AI on Individuals and Society, Including Discrimination and Perpetuation of Existing Inequalities
What the sources say
- Not checked yetcites [25] Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012, January 8–10). Fairness through awareness. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, Cambridge, MA, USA. source
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In the paper
“The same GenAI models that exhibit gender bias, perhaps unsurprisingly, also portray criminals or terrorists as people of color.”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 3.1. Negative Impacts of Bias in AI on Individuals and Society, Including Discrimination and Perpetuation of Existing Inequalities
What the sources say
- Not checked yetcites [23] Mittelstadt, 2016, The ethics of algorithms: Mapping the debate, Big Data Soc. source
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In the paper
“A study by the National Institute of Standards and Technology (NIST) found that facial recognition technology was significantly less accurate for people with darker skin tones, leading to higher rates of false positives [16].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 2.3. Real-World Examples of Bias in AI
What the sources say
- Not checked yetcites [16] Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., and Hall, P. (2022). Towards a Standard for Identifying and Managing Bias in Artificial Intelligence. source
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None of the sources cited for this claim have been checked yet.
In the paper
“Researchers and practitioners have proposed various mitigation strategies, such as improving data quality [8] and designing explicitly fair algorithms [9][10][11].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 1. Introduction
What the sources say
- Not checked yetcites [8] Asan, 2020, Artificial intelligence and human trust in healthcare: Focus on clinicians, J. Med. Internet Res. source
- Not checked yetcites [9][10][11]
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In the paper
“Furthermore, when prompted to generate images of criminals or terrorists, the models' output overwhelmingly more people of color.”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 2.3. Real-World Examples of Bias in AI
What the sources say
- Not checked yetcites [23] Mittelstadt, 2016, The ethics of algorithms: Mapping the debate, Big Data Soc. source
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In the paper
“A striking instance of GenAI bias was reported, where text-to-image models like StableDiffusion, OpenAI's DALL-E, and Midjourney exhibited racial and stereotypical biases in their outputs [23].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 2.3. Real-World Examples of Bias in AI
What the sources say
- Not checked yetcites [23] Mittelstadt, 2016, The ethics of algorithms: Mapping the debate, Big Data Soc. source
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None of the sources cited for this claim have been checked yet.
In the paper
“Multiple studies have identified biases against certain groups in AI systems, such as the facial recognition systems studied by Buolamwini and Gebru [1] and hiring algorithms examined by Dastin [2].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 1. Introduction
What the sources say
- Not checked yetcites [1] Buolamwini, J., and Gebru, T. (2018, January 23–24). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, New York, NY, USA.
- Not checked yetcites [2] Dastin, J. (2018). Ethics of Data and Analytics, Auerbach Publications.
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Show all 32 claims

It may state two separate claims as one. 26
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The quote only partly states this claim. 1

The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Additionally, there may be privacy concerns when collecting certain types of data, such as medical records or financial information.”
- Also stated in: § 2. Unintended consequences on the distribution of outcomes · plain
  “These challenges can limit the effectiveness of dataset augmentation as a mitigation approach.”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 2. Unintended consequences on the distribution of outcomes
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In the paper
“These biases can perpetuate systemic discrimination and inequality, with detrimental effects on individuals and communities in areas like hiring, lending, and criminal justice [3][4][5][6][7].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 1. Introduction
What the sources say
- Not checked yetcites [3][4][5][6][7]
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None of the sources cited for this claim have been checked yet.
In the paper
“In the realm of generative AI, addressing bias is even more challenging as it requires a holistic strategy [14,21,22].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 4.1. Overview of Current Approaches to Mitigate Bias in AI, Including Pre-Processing Data, Model Selection, and Post-Processing Decisions
What the sources say
- Not checked yetcites [14] Ferrara, E. (2023). Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models. First Monday, 28. source
- Not checked yetcites [21] Ferrara, E. (2023). GenAI against humanity: Nefarious applications of generative artificial intelligence and large language models. arXiv. source
- Not checked yetcites [22] Ferrara, E. (2023). The butterfly effect in artificial intelligence systems: Implications for AI bias and fairness. arXiv. source
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“The importance of fairness and bias in AI is widely recognized by researchers, policymakers, and the academic community [1,[12][13][14][15][16].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 1. Introduction
What the sources say
- Not checked yetcites [1,[12][13][14][15][16]
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In the paper
“For instance, facial recognition algorithms trained on data primarily consisting of men can struggle to recognize female faces accurately, perpetuating gender bias in security systems [1].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 3.1. Negative Impacts of Bias in AI on Individuals and Society, Including Discrimination and Perpetuation of Existing Inequalities
What the sources say
- Not checked yetcites [1] Buolamwini, J., and Gebru, T. (2018, January 23–24). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, New York, NY, USA.
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In the paper
“Additionally, model selection methods may be limited by the lack of consensus on what constitutes fairness, and post-processing methods can be complex and require large amounts of additional data [32].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 4.1. Overview of Current Approaches to Mitigate Bias in AI, Including Pre-Processing Data, Model Selection, and Post-Processing Decisions
What the sources say
- Not checked yetcites [32] Barocas, 2016, Big data’s disparate impact, Calif. Law Rev.
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In the paper
“A study by ProPublica found that the system was biased against African-American defendants, as they were more likely to be labeled as high-risk even if they had no prior convictions.”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 2.3. Real-World Examples of Bias in AI
What the sources say
- Not checked yetcites ProPublica
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It may state two separate claims as one.
In the paper
“For example, biased algorithms used in the criminal justice system can lead to unfair treatment of certain groups, particularly people of color, who are more likely to be wrongly convicted or receive harsher sentences [1].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 3.1. Negative Impacts of Bias in AI on Individuals and Society, Including Discrimination and Perpetuation of Existing Inequalities
What the sources say
- Not checked yetcites [1] Buolamwini, J., and Gebru, T. (2018, January 23–24). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, New York, NY, USA.
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Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
In the paper
“Different people and groups may have different definitions of fairness, and these definitions may change over time [39].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 6.2. Discussion of the Limitations and Challenges of These Approaches
What the sources say
- Not checked yetcites [39] Ezzeldin, Y.H., Yan, S., He, C., Ferrara, E., and Avestimehr, S. (2023, January 7–14). Fairfed: Enabling group fairness in federated learning. Proceedings of the AAAI 2023—37th AAAI Conference on Artificial Intelligence, Washington, DC, USA.
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In the paper
“This can be performed through techniques such as regularization, which penalizes models for making discriminatory predictions, or through ensemble methods, which combine multiple models to reduce bias [25].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 4.1. Overview of Current Approaches to Mitigate Bias in AI, Including Pre-Processing Data, Model Selection, and Post-Processing Decisions
What the sources say
- Not checked yetcites [25] Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012, January 8–10). Fairness through awareness. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, Cambridge, MA, USA. source
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None of the sources cited for this claim have been checked yet.
In the paper
“Other approaches include transparency, accountability, and explainability.”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 6.1. Overview of Current Approaches to Ensure Fairness in AI, including Group Fairness and Individual Fairness
What the sources say
- Not checked yetcites [26] Ananny, 2018, Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability, New Media Soc. source
- Not checked yetcites [38] Donovan, J., Caplan, R., Matthews, J., and Hanson, L. (2018). Algorithmic Accountability: A Primer, Data & Society.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Additionally, different types of fairness may conflict with each other, and trade-offs may need to be made to achieve fairness in specific contexts [26].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 5.1. Definition of Fairness in AI and Its Different Types
What the sources say
- Not checked yetcites [26] Ananny, 2018, Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability, New Media Soc. source
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None of the sources cited for this claim have been checked yet.
In the paper
“Additionally, it may be difficult to determine which types of fairness are most appropriate for a given context and how to balance them appropriately [4][5][6].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 6.2. Discussion of the Limitations and Challenges of These Approaches
What the sources say
- Not checked yetcites [4] Kleinberg, 2018, Human decisions and machine predictions, Q. J. Econ.
- Not checked yetcites [5] Kleinberg, 2018, Discrimination in the Age of Algorithms, J. Leg. Anal. source
- Not checked yetcites [6] Kleinberg, 2018, Algorithmic fairness, AEA Pap. Proc. source
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In the paper
“To address this issue, the Northpointe COMPAS was modified to include a “race-neutral” version of the algorithm that achieved similar accuracy while reducing racial bias [19].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 5.3. Real-World Examples of Fairness in AI
What the sources say
- Not checked yetcites [19] Angwin, J., Larson, J., Mattu, S., and Kirchner, L. (2016). Ethics of Data and Analytics, Auerbach Publications.
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In the paper
“To address this issue, researchers have proposed using techniques such as subgroup analysis to identify and address bias in the data used to train AI models [11].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 5.3. Real-World Examples of Fairness in AI
What the sources say
- Not checked yetcites [11] Yan, S., Kao, H.T., and Ferrara, E. (July, January 26). Fair class balancing: Enhancing model fairness without observing sensitive attributes. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, Online.
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In the paper
“In other words, bias can be viewed as a technical issue, while fairness is a social and ethical issue [26].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 5.2. Comparison of Fairness and Bias in AI
What the sources say
- Not checked yetcites [26] Ananny, 2018, Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability, New Media Soc. source
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Bias can be unintentional, while fairness is inherently a deliberate and intentional goal.
In the paper
“There are several different types of fairness that have been proposed in the literature, including group fairness, individual fairness, and counterfactual fairness [30].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 5.1. Definition of Fairness in AI and Its Different Types
What the sources say
- Not checked yetcites [30] Zafar, M.B., Valera, I., Gomez Rodriguez, M., and Gummadi, K.P. (2017, January 3–7). Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. Proceedings of the 26th International Conference on World Wide Web, Perth, Australia. source
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In the paper
“One example is the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) system, which is used to predict the likelihood of recidivism in criminal defendants.”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 5.3. Real-World Examples of Fairness in AI
What the sources say
- Not checked yetcites [19] Angwin, J., Larson, J., Mattu, S., and Kirchner, L. (2016). Ethics of Data and Analytics, Auerbach Publications.
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None of the sources cited for this claim have been checked yet.
In the paper
“Diversifying the teams involved in AI development and evaluation is equally crucial, as it brings a multiplicity of perspectives that can better identify and correct for biases [55][56][57].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 7. Conclusions
What the sources say
- Not checked yetcites [55] Chauhan, 2022, The Role of Data and Artificial Intelligence in Driving Diversity, Equity, and Inclusion, Computer source
- Not checked yetcites [56] Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., and Wallach, H. (2019, January 4–9). Improving fairness in machine learning systems: What do industry practitioners need?. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK. source
- Not checked yetcites [57] Stathoulopoulos, K., Mateos-Garcia, J.C., and Owen, H. (2023, December 15). Gender Diversity in AI Research. Available online: https://www.nesta.org.uk/report/gender-diversity-ai/.
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In the paper
“Another difference is that bias can be either positive or negative, whereas fairness is only concerned with negative bias or discrimination [36].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 5.2. Comparison of Fairness and Bias in AI
What the sources say
- Not checked yetcites [36] 2017, Measuring discrimination in algorithmic decision making, Data Min. Knowl. Discov. source
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None of the sources cited for this claim have been checked yet.
In the paper
“We also considered the ethical implications of AI in preserving privacy and the necessity for transparency, oversight, and continuous evaluation of AI systems [52][53][54].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 7. Conclusions
What the sources say
- Not checked yetcites [52] Lipton, 2018, The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery, Queue source
- Not checked yetcites [53] Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I.D., and Gebru, T. (2019, January 29–31). Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, Atlanta, GA, USA. source
- Not checked yetcites [54] Raji, I.D., and Buolamwini, J. (2019, January 29–31). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products. Proceedings of the Conference on Fairness, Accountability, and Transparency, Atlanta, GA, USA. source
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In the paper
“Research has shown that the system was biased against African-American defendants, as it was more likely to falsely predict that they would re-offend compared to white defendants [19].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 5.3. Real-World Examples of Fairness in AI
What the sources say
- Not checked yetcites [19] Angwin, J., Larson, J., Mattu, S., and Kirchner, L. (2016). Ethics of Data and Analytics, Auerbach Publications.
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In the paper
“However, it is important to recognize that bias is not the only factor that can lead to unfairness, and achieving fairness may require additional efforts beyond bias mitigation [27].”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 5.2. Comparison of Fairness and Bias in AI
What the sources say
- Not checked yetcites [27] Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., and Huq, A. (2017, January 13–17). Algorithmic decision making and the cost of fairness. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada. source
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- Rests on an assumption: Addressing bias in training data or algorithms can help reduce the likelihood of unfair outcomes.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While these approaches have shown promising results in promoting fairness in AI, they are not without limitations and challenges.”
§ Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies > 6.2. Discussion of the Limitations and Challenges of These Approaches
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- Rests on an assumption: Many current approaches to ensuring fairness in AI rely on statistical methods and assumptions that may not accurately capture the complexity of human behavior and decision-making.

41 of 41

The paper relies on this without support.
The use of AI systems is growing.
“The growing use of AI systems has intensified discussions around fairness and bias in artificial intelligence as potential biases and discrimination become more apparent.”
The paper relies on this without support.
Generative AI is becoming more prevalent in creating content that influences public perception.
“especially as generative AI becomes more prevalent in creating content that influences public perception.”
The paper relies on this without support.
Bias can be unintentional, while fairness is inherently a deliberate and intentional goal.
“One key difference between fairness and bias is that while bias can be unintentional, fairness is inherently a deliberate and intentional goal.”
The paper relies on this without support.
Addressing bias in training data or algorithms can help reduce the likelihood of unfair outcomes.
“For example, addressing bias in training data or algorithms can help reduce the likelihood of unfair outcomes.”
The paper relies on this without support.
Group fairness can be achieved through various techniques such as re-sampling, pre-processing, or post-processing of the data used to train the AI model.
“This can be achieved through various techniques such as re-sampling, pre-processing, or postprocessing of the data used to train the AI model.”
The paper relies on this without support.
Individual fairness can be achieved through techniques such as counterfactual fairness or causal fairness.
“Individual fairness can be achieved through techniques such as counterfactual fairness or causal fairness.”
The paper relies on this without support.
Many current approaches to ensuring fairness in AI rely on statistical methods and assumptions that may not accurately capture the complexity of human behavior and decision-making.
“Furthermore, many of the current approaches to ensuring fairness in AI rely on statistical methods and assumptions that may not accurately capture the complexity of human behavior and decision-making.”
The paper relies on this without support.
Bias can arise due to various factors, such as biased data or algorithmic design.
“Bias can arise due to various factors, such as biased data or algorithmic design, but fairness requires a conscious effort to ensure that the algorithm does not discriminate against any group or individual.”
The paper relies on this without support.
Fairness requires a conscious effort to ensure that the algorithm does not discriminate against any group or individual.
“Bias can arise due to various factors, such as biased data or algorithmic design, but fairness requires a conscious effort to ensure that the algorithm does not discriminate against any group or individual.”
The paper relies on this without support.
If an AI model is trained on data that are biased toward a particular group, re-sampling techniques can be used to create a balanced dataset where each group is represented equally.
“For example, if an AI model is trained on data that are biased toward a particular group, re-sampling techniques can be used to create a balanced dataset where each group is represented equally.”
The paper relies on this without support.
Pre-processing or post-processing can be used to adjust the output of the AI model to ensure that it does not unfairly disadvantage any group.
“Other techniques, such as pre-processing or post-processing, can be used to adjust the output of the AI model to ensure that it does not unfairly disadvantage any group.”

11 of 11

Claims other papers make about this one

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Fairness both excludes and includes favoritism
Minor
The paper says fairness is concerned only with negative bias, but elsewhere defines fairness as the absence of discrimination or favoritism. It also defines positive bias as systematically favoring a group, so the stated scope of fairness conflicts with its definition.
Model training classified as pre-processing
Minor
The paper defines pre-processing as addressing bias before the model is trained, then lists a technique involving training the model as an example of pre-processing.

2 of 2

Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory, and Antiracist Politics
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Dissecting racial bias in an algorithm used to manage the health of populations
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“A study conducted by Obermeyer et al. [20] found that the system was more likely to assign higher-risk scores to African-American patients, even when other factors, such as age and health status, were the same.”
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812
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Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 4,567 · cited 3×
“When generative AI (GenAI) models are prompted to create images of CEOs, they tend to reinforce stereotypes by depicting CEOs predominantly as men [23].”
BackgroundMethodInfluential
Untitled work
(2018) · Cited by 3,510
Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501 · cited 4×
“Similarly, an image generation model trained on datasets with limited diversity in human portraits may struggle to accurately represent a broad range of ethnicities.”
BackgroundMethodInfluential
Big Data's Disparate Impact
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048 · cited 2×
“Can be complex and require large amounts of additional data [32].”
Background
Semantics derived automatically from language corpora contain human-like biases
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017) · Science · doi:10.1126/science.aal4230 · Cited by 2,887 · cited 1×
“This survey study delves into the complex and multifaceted issues surrounding fairness and bias in AI, covering the sources of bias, their impacts, and proposed mitigation strategies.”
Background
Algorithms of Oppression: How Search Engines Reinforce Racism
Noble, S. U. (2020) · doi:10.2307/j.ctt1pwt9w5 · Cited by 2,709

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Research works printed but not linked: 3

Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, St. Martin’s Press.
Corbett-Davies, S., and Goel, S. (2018). The measure and mismeasure of fairness: A critical review of fair machine learning. arXiv.
Crenshaw, K. (1989). Feminist Legal Theories, Routledge.

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Authors: Ferrara, E.
Affiliations: University of Southern California (US)
Corresponding author: Ferrara, Emilio
ORCID: Ferrara, Emilio
Venue: Sci
Publisher: MDPI AG
Published: 2023-12-26
Volume: 6
Issue: 1
Pages: 3
ISSN: 2413-4155 (ISSN-L)
DOI: 10.3390/sci6010003
OpenAlex: W4390229867
Semantic Scholar: 53b04ccd2a001467d7ce168e9ce20b16a9466a69
Cited by: 875
References: 59
Citations by year: 2026: 271 · 2025: 433 · 2024: 162 · 2023: 6
FWCI: 218.36
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Sci (publishedVersion, cc-by)
SSRN Electronic Journal (acceptedVersion)
arXiv (Cornell University) (submittedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
arXiv (Cornell University) (cc-by)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
Keywords: algorithmic fairness, algorithmic bias, generative AI bias, generative AI, synthetic media, data bias, bias mitigation, bias mitigation strategies, data preprocessing, model selection, post-processing, societal impact of AI, stereotypes, artificial intelligence in healthcare, AI ethics

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Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies

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