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The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity
Abstract
Recently, there has been an upsurge of attention focused on bias and its impact on specialized artificial intelligence (AI) applications. Allegations of racism and sexism have permeated the conversation as stories surface about search engines delivering job postings for well-paying technical jobs to men and not women, or providing arrest mugshots when keywords such as "black teenagers" are entered. Learning algorithms are evolving; they are often created from parsing through large datasets of online information while having truth labels bestowed on them by crowd-sourced masses. These specialized AI algorithms have been liberated from the minds of researchers and startups, and released onto the public. Yet intelligent though they may be, these algorithms maintain some of the same biases that permeate society. They find patterns within datasets that reflect implicit biases and, in so doing, emphasize and reinforce these biases as global truth. This paper describes specific examples of how bias has infused itself into current AI and robotic systems, and how it may affect the future design of such systems. More specifically, we draw attention to how bias may affect the functioning of (1) a robot peacekeeper, (2) a self-driving car, and (3) a medical robot. We conclude with an overview of measures that could be taken to mitigate or halt bias from permeating robotic technology.
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“Human-induced bias is intentionally or unintentionally written by the developers because individuals are always influenced by their own moral perceptions and relevant interests, which affects data training”
From Ethics and governance of trustworthy medical artificial intelligence (2023) · cites this paper as [26]
In the citing paper
“Various high-profle controversies (e.g.[10,22,112]) have raised concerns over the technology's potential to both perpetuate existing societal inequalities and introduce new types of discrimination [52,67,116].”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [67]
In the citing paper
“AI applications released to the public are not free from racial and gender biases (Chen, Szolovits & Ghassemi, 2019; Howard & Borenstein, 2018).”
From Digital ageism: challenges and opportunities in artificial intelligence for older adults (2022) · cites this paper as Howard & Borenstein, 2018
Citations
Cites
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Stereotype Threat and Women's Math Performance
Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999) · Journal of Experimental Social Psychology · doi:10.1006/jesp.1998.1373 · Cited by 3,435
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
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536
Hall, W. J., Chapman, M. V., Lee, K. M., Merino, Y. M., Thomas, T. W., Payne, B. K., Eng, E., Day, S. H., & Coyne-Beasley, T. (2015) · American Journal of Public Health · doi:10.2105/ajph.2015.302903 · Cited by 2,176
Nwana, H. S. (1996) · The Knowledge Engineering Review · doi:10.1017/s026988890000789x · Cited by 1,588
Green, A. R., Carney, D. R., Pallin, D. J., Ngo, L. H., Raymond, K. L., Iezzoni, L. I., & Banaji, M. R. (2007) · Journal of General Internal Medicine · doi:10.1007/s11606-007-0258-5 · Cited by 1,460
Gender stereotypes about intellectual ability emerge early and influence children's interests
Bian, L., Leslie, S.-J., & Cimpian, A. (2017) · Science · doi:10.1126/science.aah6524 · Cited by 1,206
Handling imbalanced datasets: A review
Kotsiantis, S. B., Kanellopoulos, D. N., & Pintelas, P. (2006) · Cited by 920
Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination
Datta, A., Tschantz, M. C., & Datta, A. (2015) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2015-0007 · Cited by 676
Research works printed but not linked: 4
- Bolukbasi, T., Chang K-W, Zou, J., Saligrama, V., & Kalai, A. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. https://arxiv.org/pdf/1607.06520.pdf . Accessed July 3, 2017. · arXiv
- Harpur, P. (2013). From universal exclusion to universal equality: Regulating ableism in a digital age. Northern Kentucky Law Review, 40(3), 529–565.
- IEEE. (2017). Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems. http://standards.ieee.org/develop/indconn/ec/ead_brochure.pdf . Accessed February 7, 2017.
- Steele, C. M. (2010). Whistling Vivaldi: And other clues to how stereotypes affect us (issues of our time). New York, NY: W. W. Norton and Co.
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“Human-induced bias is intentionally or unintentionally written by the developers because individuals are always influenced by their own moral perceptions and relevant interests, which affects data training [25, 26].”
BackgroundArtificial intelligence for good health: a scoping review of the ethics literature.
Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00577-8 · Cited by 435
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Trust and medical AI: the challenges we face and the expertise needed to overcome them
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Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., & Tang, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3546872 · Cited by 261
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Scheuerman, M. K., Wade, K., Lustig, C., & Brubaker, J. R. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3392866 · Cited by 193
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Schiff, D. S., Schiff, K. J., & Pierson, P. (2022) · Public Administration · doi:10.1111/padm.12742 · Cited by 155
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Lim, H. S. M., & Taeihagh, A. (2019) · Sustainability · doi:10.3390/su11205791 · Cited by 136
Gupta, M., Parra, C. M., & Dennehy, D. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10156-2 · Cited by 154
d’Elia, A., Gabbay, M., Rodgers, S., Kierans, C., Jones, E., Durrani, I., Thomas, A., & Frith, L. (2022) · Family Medicine and Community Health · doi:10.1136/fmch-2022-001670 · Cited by 129
Kamulegeya, L., Bwanika, J., Okello, M., Rusoke, D., Nassiwa, F., Lubega, W., Musinguzi, D., & Börve, A. (2023) · African Health Sciences · doi:10.4314/ahs.v23i2.86 · Cited by 111
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Marabelli, M., Newell, S., & Handunge, V. R. (2021) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2021.101683 · Cited by 140
Pennisi, F., Pinto, A., Ricciardi, G. E., Signorelli, C., & Gianfredi, V. (2025) · Antibiotics · doi:10.3390/antibiotics14020134 · Cited by 122
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Endsley, M. R. (2023) · Ergonomics · doi:10.1080/00140139.2023.2243404 · Cited by 118
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Chu, C. H., Donato‐Woodger, S., Khan, S. S., Nyrup, R., Leslie, K., Lyn, A., Shi, T., Bianchi, A., Rahimi, S. A., & Grenier, A. M. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-01999-y · Cited by 104
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Shrestha, S., & Das, S. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.976838 · Cited by 105
Butler, L., Yiğitcanlar, T., & Paz, A. (2020) · Applied Sciences · doi:10.3390/app10186306 · Cited by 104
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Phan, L. V., & Rauthmann, J. F. (2021) · Social and Personality Psychology Compass · doi:10.1111/spc3.12624 · Cited by 87
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Riegler, A., Riener, A., & Holzmann, C. (2021) · Frontiers in Human Dynamics · doi:10.3389/fhumd.2021.689856 · Cited by 86
Does Gender Matter? Towards Fairness in Dialogue Systems
Liu, H., Dacon, J., Fan, W., Liu, H., Liu, Z., & Tang, J. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.390 · Cited by 81
Misra, S. K., Sharma, S. K., Gupta, S., & Das, S. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122721 · Cited by 95
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Micheli, M., Lutz, C., & Büchi, M. (2018) · Journal of Information Communication and Ethics in Society · doi:10.1108/jices-02-2018-0014 · Cited by 88
Sparrow, R. (2019) · Science Technology & Human Values · doi:10.1177/0162243919862862 · Cited by 89
Mantello, P. A., Ho, T. M., Nguyen, M., & Vuong, Q. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-01837-1 · Cited by 92
Hodgson, D., Goldingay, S., Boddy, J., Nipperess, S., & Watts, L. (2022) · The British Journal of Social Work · doi:10.1093/bjsw/bcab168 · Cited by 74
Anagnostou, M., Karvounidou, O., Katritzidaki, C., Kechagia, C., Melidou, K., Mpeza, E., Konstantinidis, I., Kapantai, E., Berberidis, C., Magnisalis, I., et al. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09634-1 · Cited by 58
Racism, responsibility and autonomy in HCI: Testing perceptions of an AI agent
Hong, J.-W., & Williams, D. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.06.012 · Cited by 69
Burk-Rafel, J., Reinstein, I., Feng, J., Kim, M. B., Miller, L. H., Cocks, P. M., Marin, M., & Aphinyanaphongs, Y. (2021) · Academic Medicine · doi:10.1097/acm.0000000000004317 · Cited by 65
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Liu, H., Wang, W., Wang, Y., Liu, H., Liu, Z., & Tang, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.64 · Cited by 57
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Nwosu, A. C., Sturgeon, B., McGlinchey, T., Goodwin, C. D. G., Behera, A., Mason, S. R., Stanley, S., & Payne, T. R. (2019) · Palliative Medicine · doi:10.1177/0269216319857628 · Cited by 51
Rubegni, E., Malinverni, L., & Yip, J. C. (2022) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3501712.3529726 · Cited by 54
Chubb, J., Missaoui, S., Concannon, S. J., Maloney, L., & Walker, J. A. (2021) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2021.100403 · Cited by 59
Neher, M., Petersson, L., Nygren, J., Svedberg, P., Larsson, I., & Nilsén, P. (2023) · Implementation Science Communications · doi:10.1186/s43058-023-00458-8 · Cited by 61
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Ostrowski, A. K., Walker, R., Das, M., Yang, M., Breazea, C., Park, H. W., & Verma, A. (2022) · 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) · doi:10.1109/ro-man53752.2022.9900805 · Cited by 58
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Paproki, A., Salvado, O., & Fookes, C. (2024) · ACM Computing Surveys · doi:10.1145/3663759 · Cited by 57
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Sparrow, R. (2019) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2019.2927372 · Cited by 46
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Pillai, V. S., & Matus, K. (2020) · Science and Public Policy · doi:10.1093/scipol/scaa073 · Cited by 49
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Gillet, S., Vázquez, M., Andrist, S., Leite, I., & Sebo, S. S. (2024) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3643803 · Cited by 51
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Çetindamar, D., & Phaal, R. (2021) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2021.3101196 · Cited by 50
Inclusion in human–machine interactions
Reid, T., & Gibert, J. M. (2022) · Science · doi:10.1126/science.abf2618 · Cited by 43
Robinson, F., & Nejat, G. (2022) · Journal of Rehabilitation and Assistive Technologies Engineering · doi:10.1177/20556683221101389 · Cited by 45
Large language models and political science
Linegar, M., Kocielnik, R., & Alvarez, R. M. (2023) · Frontiers in Political Science · doi:10.3389/fpos.2023.1257092 · Cited by 57
Jain, N., Olmo, A., Sengupta, S., Manikonda, L., & Subbarao Kambhampati, B. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103652 · Cited by 61
Can Robots Make us Better Humans?
Cappuccio, M. L., Sandoval, E. B., Mubin, O., Obaid, M., & Velonaki, M. (2020) · International Journal of Social Robotics · doi:10.1007/s12369-020-00700-6 · Cited by 41
AI ethics: a long history and a recent burst of attention
Borenstein, J., Grodzinsky, F. S., Howard, A., Miller, K. W., & Wolf, M. J. (2021) · Computer · doi:10.1109/mc.2020.3034950 · Cited by 38
Self-citationEthics of Social Robotics: Individual and Societal Concerns and Opportunities
Carme Torras, A. (2023) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-062023-082238 · Cited by 36
The effectiveness of explainable AI on human factors in trust models
Cheung, J. C. T., & Ho, S. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-04189-9 · Cited by 36
Treatment of inconclusives in the AFTE range of conclusions
Hofmann, H., Carriquiry, A. L., & VanderPlas, S. (2020) · Law Probability and Risk · doi:10.1093/lpr/mgab002 · Cited by 36
Ethics of robotized public services: The role of robot design and its actions
Willems, J., Schmidthuber, L., Vogel, D., Ebinger, F., & Vanderelst, D. F. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2022.101683 · Cited by 41
Ho, S. S., & Cheung, J. C. T. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102533 · Cited by 44
Blösser, M., & Weihrauch, A. (2023) · European Journal of Marketing · doi:10.1108/ejm-01-2023-0009 · Cited by 35
It's Time for Artistic Correspondence in Music and Video
Surís, D., Vondrick, C., Russell, B. C., & Salamon, J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01031 · Cited by 35
Governance, Standards and Regulation: What Construction and Mining Need to Commit to Industry 4.0
Smith, K. I., & Sepasgozar, S. M. E. (2022) · Buildings · doi:10.3390/buildings12071064 · Cited by 33
The promises and challenges of addressing artificial intelligence with human rights
Bakıner, O. (2023) · Big Data & Society · doi:10.1177/20539517231205476 · Cited by 32
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Alipour, N., Burke, T., & Courtney, J. (2024) · Current Dermatology Reports · doi:10.1007/s13671-024-00440-0 · Cited by 46
Sanpablo, A. I. P., Quinzaños-Fresnedo, J., Gutiérrez-Martínez, J., Rodríguez, I. G. L., & Roldán-Valadéz, E. (2025) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056370620250403030638 · Cited by 36
Age and gender bias in pedestrian detection algorithms
Brandao, M. (2019) · arXiv · doi:10.48550/arxiv.1906.10490 · Cited by 32
Ethical Dilemmas of Using Artificial Intelligence in Medicine
Astărăstoae, V., Rogozea, L. M., Leaşu, F., & Ioan, B. G. (2024) · American Journal of Therapeutics · doi:10.1097/mjt.0000000000001693 · Cited by 36
Lewicki, K., Lee, M. S. A., Cobbe, J., & Singh, J. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581463 · Cited by 30
“[10, 22, 109]) have raised concerns over the technology’s potential to both perpetuate existing societal inequalities and introduce new types of discrimination [51, 66, 113].”
BackgroundSau, A., Sieliwończyk, E., Patlatzoglou, K., Pastika, L., McGurk, K. A., Ribeiro, A. H., Ribeiro, A. L. P., Ho, J. E., Peters, N. S., Ware, J. S., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2024.12.003 · Cited by 36
Misspecification and unreliable interpretations in psychology and social science.
Vowels, M. J. (2021) · Psychological Methods · doi:10.1037/met0000429 · Cited by 41
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Torkamaan, H., Tahaei, M., Buijsman, S., Xiao, Z., Wilkinson, D., & Knijnenburg, B. P. (2024) · Human-computer interaction series · doi:10.1007/978-3-031-55109-3_2 · Cited by 28
Righetti, L., Madhavan, R., & Chatila, R. (2019) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2019.2926996 · Cited by 26
Sarkar, D., & Chapman, C. A. (2021) · Tropical Conservation Science · doi:10.1177/19400829211014740 · Cited by 25
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Skewes, J., Amodio, D. M., & Seibt, J. (2019) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2018.0037 · Cited by 25
LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actions
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Rana, S. A., Azizul, Z. H., & Awan, A. A. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1630 · Cited by 23
Exploring the Potential of Fuzzy Sets in Cyborg Enhancement: A Comprehensive Review
Su, H., Ovur, S. E., Xu, Z., & Alfayad, S. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3491733 · Cited by 23
Silva, J. D. (2021) · Methodological Innovations · doi:10.1177/2059799120987766 · Cited by 22
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Metadata
- Authors
- Howard, A., & Borenstein, J.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Borenstein, Jason
- Venue
- Science and Engineering Ethics
- Published
- 2017-09-21
- Volume
- 24
- Issue
- 5
- Pages
- 1521-1536
- ISSN
- 1353-3452 (ISSN-L)
- 1471-5546
- OpenAlex
- W2760062370
- Cited by
- 337
- References
- 26
- Citations by year
- 2026: 29 · 2025: 64 · 2024: 53 · 2023: 59 · 2022: 38 · 2021: 49 · 2020: 27 · 2019: 13 · 2018: 4 · 2017: 1
- FWCI
- 28.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Science and Engineering Ethics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Artificial Intelligence in Law
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- algorithmic bias, artificial intelligence, social inequities, implicit bias, racial bias, gender bias, autonomous vehicles, medical robots, bias mitigation, robot ethics
- MeSH terms
- Algorithms, Artificial Intelligence, Automobiles, Female, Humans, Learning, Male, Prejudice, Robotics, Social Justice, Bias, Biomedical Technology, Racism, Sexism, Datasets as Topic
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