Cited by 262Ethics and Social Impacts of AIArtificial Intelligence in LawDigitalization, Law, and Regulation

The DOI matches its Crossref record.

Racist in the Machine

Garcia, Megan·2016·World Policy Journal

Open this paper in CoChat — free account

Abstract

Companies and governments need to pay attention to the unconscious and institutional biases that seep into their algorithms, argues cybersecurity expert Megan Garcia. Distorted data can skew results in web searches, home loan decisions, or photo recognition software. But the combination of increased attention to the inputs, greater clarity about the properties of the code itself, and the use of crowd-level monitoring could contribute to a more equitable online world. Without careful consideration, Garcia writes, our technology will be just as racist, sexist, and xenophobic as we are.

Integrity signals

Registry

Crossref · 10.1215/07402775-3813015

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Sorted by citations

Items 101–200 of 262

Locality of Technical Objects and the Role of Structural Interventions for Systemic Change
Cortés, E. C., Rajtmajer, S. M., & Ghosh, D. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3534646 · Cited by 7
Exploiting Transferable Knowledge for Fairness-Aware Image Classification
Hwang, S., Park, S., Lee, P., Jeon, S., Kim, D., & Byun, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69538-5_2 · Cited by 8
“In addition, face recognition systems usually perform well on Caucasians, yet they often fail to identify faces of other races [8, 9].”
Background
From Pixels to Principles: A Decade of Progress and Landscape in Trustworthy Computer Vision
Huang, K., Teng, Y., Chen, Y., & Wang, Y. (2024) · Science and Engineering Ethics · doi:10.1007/s11948-024-00480-6 · Cited by 9
“However, the labeling process can be subjective and limited, which may amplify data bias regarding gender, age, ethnicity, skin color, etc. (Garcia, 2016; Buolamwini & Gebru, 2018; Raji & Buolamwini, 2019).”
Background
4. Moral programming
Aliman, N.-M., & Kester, L. J. H. M. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_4 · Cited by 7
12. Designing a griefbot-for-good
van der Vorst, R., & Kamp, J.-A. M. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_12 · Cited by 6
The effect of candidates and assessors culture on nonverbal expression and nonverbal judgments in the job interview
Cannata, D., O’Hora, D. P., & Redfern, S. (2024) · International Journal of Cross Cultural Management · doi:10.1177/14705958241244689 · Cited by 10
“Other systems for the recognition of nonverbal behaviors, however, have demonstrated differential validity and bias (Garcia, 2016).”
Measuring Ethics in AI with AI: A Methodology and Dataset Construction
Avelar, P. H. C., Audibert, R. B., & Lamb, L. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21686-2_26 · Cited by 8
“In turn, such biases coupled with data biases will lead systems that show undesired behaviours and prejudices [3, 9, 13].”
Background
Predicting firm creation in rural Texas: A multi-model machine learning approach to a complex policy problem
Hand, M. C., Shastry, V., & Rai, V. (2023) · PLoS ONE · doi:10.1371/journal.pone.0287217 · Cited by 8
When data meets citizens: an investigation of citizen engagement in data-driven innovation programmes
Thuermer, G., Walker, J., Simperl, E. P. B., & Carr, L. A. (2024) · Data & Policy · doi:10.1017/dap.2023.43 · Cited by 6
“There is a considerable body of work regarding “ fairness ” in data and algorithms, but this largely focuses on improving access to or use of data, preventing biases within the data, and other principles for more ethical algorithms (e.g., Garcia, 2016; Wilkinson et al., 2016).”
Background
Ethical judgement in intelligent control systems for autonomous vehicles
Narayanan, A. (2019) · Australian & New Zealand Control Conference (ANZCC) · doi:10.1109/anzcc47194.2019.8945790 · Cited by 7
Analysing the multiple dimensions of predictive policing’s techno-social harms
Wood, M. A., & Warren, I. (2022) · Justice, power and resistance · doi:10.1332/nyhn8325 · Cited by 7
Does artificial intelligence improve social equity in public services? A systematic literature review on opportunities and pitfalls
Paglieri, L., Costumato, L., & Savignon, A. B. (2025) · International Journal of Public Sector Management · doi:10.1108/ijpsm-10-2024-0333 · Cited by 13
Ethics and the use of generative AI in professional editing
Otmar, R., Michael, R., Mullins, S., & Day, K. (2024) · AI and Ethics · doi:10.1007/s43681-024-00521-7 · Cited by 6
“Other studies have reported undue influence through word embeddings and algorithms in older and newer versions of LLMs, including gender bias [49], racial bias [21] and political bias [55].”
The Human Factor: Algorithms, Dissenters, and Detention in Immigration Enforcement
Koulish, R. E., & Calvo, E. F. (2021) · Social Science Quarterly · doi:10.1111/ssqu.12957 · Cited by 5
Populærkulturelle forestillinger av utvidet virkelighet: Makt og (u)leselige identiteter når verden blir en skjerm
Gunderson, M. (2021) · Tidsskrift for kjønnsforskning · doi:10.18261/issn.1891-1781-2021-02-03-03 · Cited by 5
“Over the past decade, there have been numerous examples of algorithms that (re)produce gender discrimination and/or stereotypical representations of marginalised identities (Benjamin 2019; Bolawumni and Gebru 2018; Garcia 2016).”
Background
TIER2: enhancing Trust, Integrity and Efficiency in Research through next-level Reproducibility
Ross‐Hellauer, T., Klebel, T., Bannach‐Brown, A., Horbach, S. P. J. M., Jabeen, H., Manola, N., Metodiev, T., Papageorgiou, H., Reczko, M., Sansone, S., et al. (2022) · Research Ideas and Outcomes · doi:10.3897/rio.8.e98457 · Cited by 5
The case for epistocratic republicanism
Blunt, G. D. (2019) · Politics · doi:10.1177/0263395719889563 · Cited by 5
Aggregation Bias: A Proposal to Raise Awareness Regarding Inclusion in Visual Analytics
Vázquez‐Ingelmo, A., García‐Peñalvo, F. J., & Therón, R. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-45697-9_40 · Cited by 5
With eyes of a machine: A three-step guide for applying machine learning to visual content analysis in social research
Møller, A. H. K., & Airoldi, M. (2025) · Big Data & Society · doi:10.1177/20539517251343860 · Cited by 6
“Although retraining on custom data-sets, as suggested in step B, can largely mitigate this issue and enhance transparency, it is important to recognize that AI technologies encode the gender, ethnic, and cultural biases introduced in their training data (Garcia, 2016; Zou and Schiebinger, 2018).”
Ethical and Legal Considerations of Medical Artificial Intelligence
Montalbano, P. (2025) · Primary Care Clinics in Office Practice · doi:10.1016/j.pop.2025.07.009 · Cited by 5
Encoding the Enemy: The Politics Within and Around Ethical Algorithmic War
Moses, J., & Ford, G. (2023) · Global Society · doi:10.1080/13600826.2023.2234395 · Cited by 4
“A growing literature on algorithmic bias (Barocas and Selbst 2016; Benjamin 2019; Garcia 2016; Kordzadeh and Ghasemaghaei 2022; Noble 2018) and the need to recognise the politics within algorithms has gained a lot of critical traction in this ﬁ eld over recent years.”
Background
5. Values and virtues in technology
Wernaart, B. F. W. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_5 · Cited by 4
Return on Investment in Machine Learning: Crossing the Chasm between Academia and Business
Mizgajski, J. T., Szymczak, A., Morzy, M., Augustyniak, Ł., Szymański, P., & Żelasko, P. (2020) · Foundations of Computing and Decision Sciences · doi:10.2478/fcds-2020-0015 · Cited by 8
“• model bias caused by objective functions [55, 86],”
Background
Machine learning as the new approach in understanding biomarkers of suicidal behavior
Paska, A. V., & Kouter, K. (2020) · Bosnian Journal of Basic Medical Sciences · doi:10.17305/bjbms.2020.5146 · Cited by 6
“collection, such as racial and gender differences [64], and”
Background
Towards explainable real estate valuation via evolutionary algorithms
Angrick, S., Bals, B., Hastrich, N., Kleissl, M., Schmidt, J., Doskoč, V., Molitor, L., Friedrich, T., & Katzmann, M. (2022) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3512290.3528801 · Cited by 6
Comparing Deep Sentiment Models using Quantified Local Explanations
Sangani, R. B., Shukla, A., & Selvamani, B. R. (2021) · International Conference on Smart Technologies, Communication and Robotics (STCR) · doi:10.1109/stcr51658.2021.9588834 · Cited by 5
Designing inclusion and continuity for resilient communication during disasters
Banerjee, I., Warnier, M., & Brazier, F. M. T. (2022) · Sustainable and Resilient Infrastructure · doi:10.1080/23789689.2022.2124717 · Cited by 4
“If these biases are unavoidable, different algorithms’ bias should affect different segments, rather than always disadvantaging the same group (Danks & London, 2017; Garcia, 2016; Kirkpatrick, 2016).”
Background
Facebook and cultural identity: Discourses on mediation among international students
Dumitrica, D. (2019) · International Journal of Media and Cultural Politics · doi:10.1386/macp_00003_1 · Cited by 4
Raw data or hypersymbols? Meaning-making with digital data, between discursive processes and machinic procedures
Crémier, L., Bonenfant, M., & St-Martin, L. I. L. (2019) · Semiotica · doi:10.1515/sem-2018-0110 · Cited by 4
Commentary: Neuroprosthetic Speech: Pragmatics, Norms, and Self-Fashioning
Kreitmair, K. (2019) · Cambridge Quarterly of Healthcare Ethics · doi:10.1017/s0963180119000616 · Cited by 4
Data for Societal Good: A Contextual Approach
Kaefer, F., Mora, G. L., & Nath, R. (2023) · IEEE Technology and Society Magazine · doi:10.1109/mts.2023.3306528 · Cited by 4
“However, historical biases often get incorporated into the algorithms derived from past data—leading to what is referred to as “algorithm bias” [2]—a negative outcome.”
Background
‘It wasn’t me’: the impact of social responsibility and social dominance attitudes on AI programmers’ moral imagination (intention to correct bias)
Almanzar, A. D. R., Grüning, D. J., & Edinger‐Schons, L. M. (2024) · AI and Ethics · doi:10.1007/s43681-024-00516-4 · Cited by 5
“A growing body of literature is shedding light on biases [13] in areas such as hiring [73], resource allocation [4] and policing [2], revealing that AI applications often discriminate against marginalized populations such as women, people of color, and people with disabilities [30, 46, 92].”
Societal Implications of Recommendation Systems: A Technical Perspective
Gonçalves-Sá, J., & Pinheiro, F. L. (2023) · Law, governance and technology series · doi:10.1007/978-3-031-41264-6_3 · Cited by 5
A Benchmark Proposal for Non‐Generative Fair Adversarial Learning Strategies Using a Fairness‐Utility Trade‐off Metric
de Lima, L. D., Ricarte, D. R., & Siebra, C. A. (2024) · International Conference on Climate Informatics · doi:10.1111/coin.70003 · Cited by 2
Algorithms and Bias
Smith, J. M. (2020) · Advances in logistics, operations, and management science book series · doi:10.4018/978-1-7998-3473-1.ch065 · Cited by 5
Entropic boundary conditions towards safe artificial superintelligence
Núñez-Corrales, S., & Jakobsson, E. (2021) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2021.1952653 · Cited by 3
“It also can have dangerous side effects, sometimes when the AI goes awry and does something unexpected, sometimes when one group of society members captures the AI and uses it to exploit or manipulate others (Garcia, 2016; Jiang et al., 2020).”
Background
Bias, Prejudice and Shame in Predictive Policing: State-of-the-Art and Potential Interventions for Professionals
Mayer, C. (2021) · Shame 4.0 · doi:10.1007/978-3-030-59527-2_6
Unconscious Other’s Impression Changer: A Method to Manipulate Cognitive Biases That Subtly Change Others’ Impressions Positively/Negatively by Making AI Bias in Emotion Estimation AI
Futami, K., Yanase, S., Murao, K., & Terada, T. (2022) · Sensors · doi:10.3390/s22249961 · Cited by 4
“, sexism) [21,22], a criminal behavior estimation AI that makes adverse predictions for black prisoners [20,22,23], image labeling AI that estimates black men as gorillas [19,23], and AI chatbots that perform racist speech and behavior [18].”
Background
Understanding Artificial Intelligence in Education
Jackson, K. M., & Papa, R. (2024) · Springer eBooks · doi:10.1007/978-3-031-71415-3_2 · Cited by 3
Consequences of unexplainable machine learning for the notions of a trusted doctor and patient autonomy
Klincewicz, M., & Frank, L. E. (2020) · PhilPapers (PhilPapers Foundation) · Cited by 3
Données brutes ou hypersymboles ? Signification et données numériques, entre processus discursif et procédure machinique
Crémier, L., Bonenfant, M., & St-Martin, L. I. L. (2022) · Cygne noir · doi:10.7202/1089330ar · Cited by 3
The Dynamic Pathosome: A Surrogate for Health and Disease
Lenárt, P., Scheringer, M., & Vašků, J. B. (2020) · Healthy ageing and longevity · doi:10.1007/978-3-030-52663-4_16 · Cited by 3
When is it right and good for an intelligent autonomous vehicle to take over control (and hand it back)?Preprint
Narayanan, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.08221 · Cited by 3
Ethics of Artificial Intelligence in SurgeryPreprint
Rudzicz, F., & Saqur, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.14302 · Cited by 17
“This is reflected in a litany of research confirming racial [8, 9, 27, 55] and gender bias [11] emanating from insufficiently examined autonomous decision support systems.”
Background
Gaslighting Ourselves: Racial Challenges of Artificial Intelligence in Economics and Finance Applications
Johnson, D. K. N. (2025) · The Review of Black Political Economy · doi:10.1177/00346446251331615 · Cited by 3
“There is a cottage industry around reviews of the litigation and regulation pending in AI (Almada, 2019; Barocas & Selbst, 2016; Domanski, 2019; Garcia, 2016; Hacker, 2018).”
AI, social desirability, and personality assessments: Impression management in large language models
Treglown, L., & Furnham, A. F. (2025) · Personality and Individual Differences · doi:10.1016/j.paid.2025.113563 · Cited by 2
Artificial intelligence in supply chain management: enhancing customer engagement through real-world cases
Roy, S. N., Kumar, V., Dixit, A., & Patil, A. R. (2026) · Production Planning & Control · doi:10.1080/09537287.2026.2659197 · Cited by 2
Prologue: Algorithmic Antitrust—A Primer
Portuese, A. (2022) · Economic analysis of law in European legal scholarship · doi:10.1007/978-3-030-85859-9_1 · Cited by 2
1. An introduction to moral design and technology
Wernaart, B. F. W. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_1 · Cited by 2
8. Ethics in local politics: a case study of the city of Eindhoven
van Veen, M., & Visser-Knijff, P. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_8 · Cited by 2
A Design-Led Exploration of Material Interactions Between Machine Learning and Digital Portraiture
Green, D. P., Lindley, J., Mason, Z., et al. (2022) · [ ] With Design: Reinventing Design Modes · doi:10.1007/978-981-19-4472-7_211 · Cited by 2
„Computer sagt nein“ – Gesellschaftliche Teilhabe und strukturelle Diskriminierung im Zeitalter Künstlicher Intelligenz
Rentsch, S. (2023) · Sozialwissenschaften und Berufspraxis · doi:10.1007/978-3-658-38268-1_2 · Cited by 2
Nature-Robot Interaction
Reynolds-Cuéllar, P., & Salazar-Gómez, A. F. (2023) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3568294.3580034 · Cited by 2
Biases and Ethical Considerations for Machine Learning Pipelines in the Computational Social Sciences
De, S., Jangra, S., Agarwal, V., Johnson, J., & Sastry, N. (2023) · Studies in computational intelligence · doi:10.1007/978-981-99-7184-8_6 · Cited by 3
Algorithmic Exploitation of Consumers
Mik, E. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009483599.019 · Cited by 2
Trust in Cognitive Assistants
Siddike, M. A. K., & Kohda, Y. (2019) · International Journal of Applied Industrial Engineering · doi:10.4018/ijaie.2019010104 · Cited by 2
“Researchersinthefieldsofsociologyandpsychology,organizationalscience,economics, automation,androboticshavefocusedontrust(Costa,Heras,Palanca,Novais,&Julián,2016; Friemel, Morana, Pfeiffer, & Maedche, 2017; Garcia, 2016; Hancock et al., 2011; Hoff & Bashir,2015;Mayer,Davis,&S…”
Can Lethal Autonomous Robots Learn Ethics?
Narayanan, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64984-5_18 · Cited by 2
AIM, Philosophy, and Ethics
Rainey, S., Erden, Y. J., & Rességuier, A. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_243 · Cited by 2
10. Immoral programming
Aliman, N.-M., & Kester, L. J. H. M. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_10 · Cited by 3
15. Ethical usage of data in long-term care: how do we proceed?
Aarts, S. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_15 · Cited by 2
Measurement of Ethical Issues in Software ProductsPreprint
Tria, F. D. (2020) · Preprints.org · doi:10.20944/preprints202006.0294.v1 · Cited by 2
“For example, in an insurance company or a bank, can an artificial intelligence algorithm trained on biased data be impartial when deciding whether granting an insurance [17]? An interesting case is discussed in [19].”
Background
It wasn’t me: The impact of social responsibility and social dominance attitudes on AI programmers’ moral imaginationPreprint
Almanzar, A. D. R., Edinger‐Schons, L. M., & Grüning, D. J. (2024) · doi:10.31234/osf.io/r3td6 · Cited by 2
Emerging Futures and Technology Ethics
Sage, M., & Griffin, G. (2022) · Springer eBooks · doi:10.1007/978-981-16-3059-0_21-2 · Cited by 1
A Journey Toward Self-Reconciliation via Indigenous Wisdom Unraveling Identity: A Contemporary Issue in Nursing
Jang, G. (2025) · Nurse Education Today · doi:10.1016/j.nedt.2025.106680 · Cited by 1
Moral Framing in AI-Mediated Communication: Exploration of LLM Effects on Engagement Patterns Among Black Americans
Sussman, K. L., Atkinson, L., Anderson, J. T. L., Williamson, L. D., Upshaw, S. J., & Ntang-Beb, J.-L. (2025) · Journal of Interactive Advertising · doi:10.1080/15252019.2025.2576174 · Cited by 1
“Researchers investigating AI-MC designed to appeal to racial minorities must consider the potential for unconscious and institutional biases to be embedded in algorithms (Garcia 2016).”
Unintended Consequences
(2020) · Advances in human and social aspects of technology book series · doi:10.4018/978-1-7998-4607-9.ch003 · Cited by 1
The Data Gap in Sports Analytics and How to Close It
Harell, A., & Bajić, I. V. (2019) · Summit (Simon Fraser University) · Cited by 1
Homo ex machina. Artificial Intelligence in a Brave New World—A Glimpse from Europe
Wiewiórowska-Domagalska, A. (2021) · Law and Business · doi:10.2478/law-2021-0005 · Cited by 1
“12 The bias can, of course, originate elsewhere—perhaps a false logic or even the prejudice of the programmers.”
Background
Digital Behavioral Technology, Deep Learning, and Self-Optimization
Kreitmair, K. (2021) · Advances in neuroethics · doi:10.1007/978-3-030-74188-4_9 · Cited by 2
AI, the New-Age Lawyer
Singhal, V., Sethi, S., & Pranjal, P. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-3550-5.ch014 · Cited by 1
Harm
DiMatteo, L. A., Poncibò, C., & Howells, G. G. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009483599.017 · Cited by 1
Move fast and break teachers, or what if Facebook bought Pearson?
Kupferman, D. W. (2021) · Policy Futures in Education · doi:10.1177/14782103211039872 · Cited by 1
AIM, Philosophy and Ethics
Rainey, S., Erden, Y. J., & Rességuier, A. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_243-1 · Cited by 1
Design Anthropology, Algorithmic Bias, Behavioral Capital, and the Creator Economy
Artz, M. (2022) · Practicing Anthropology · doi:10.17730/0888-4552.44.2.33 · Cited by 1
18. Information ethics as a theoretical foundation for ethical assessment and moral design of AI systems
Bruneault, F., & Laflamme, A. S. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_18 · Cited by 2
2. Moral authority
Wernaart, B. F. W. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_2 · Cited by 1
Emerging Futures and Technology Ethics
Sage, M., & Griffin, G. (2023) · Social Work · doi:10.1007/978-981-19-1015-9_21 · Cited by 1
Predicting Firm Creation in Rural Texas: A Multi-Model Machine Learning Approach to a Complex Policy ProblemPreprint
Hand, M. C., Shastry, V., & Rai, V. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4090050 · Cited by 1
Visions of augmented reality in popular culture: Power and (un)readable identities when the world becomes a screenPreprint
Gunderson, M. (2024) · doi:10.33767/osf.io/5u8md · Cited by 1
EPiC Series in Computing
Frehse, G., & Althoff, M. (2017) · Institute of Science and Technology Austria
4. Unintended Consequences
Sternfeld, L. B. (2020) · Stanford University Press eBooks · doi:10.1515/9781503607170-008
Studying and handling iterated algorithmic biases in human and machine learning interaction.
Sun, W. (2019) · ThinkIR: The University of Louisville's Institutional Repository (University of Louisville) · doi:10.18297/etd/3241
“Garcia [115] stated that algorithmic bias may worsen racist problems in certain circumstances.”
Background
Interdisciplinary Ideas: What Biases are in my Internet Searches?
Ridgway, R. (2019) · Science Scope · doi:10.2505/4/ss19_043_03_24
Rassismus im Netz
Nolden, M. (2020) · Springer eBooks · doi:10.1007/978-3-658-08357-1_73
The HOSPITAL score and LACE index for predicting the risk of hospital readmission do not show sex discrimination in a retrospective studyPreprint
ROBINSON, R. L. (2020) · medRxiv · doi:10.1101/2020.02.22.20026740
“COMPAS explicitly uses race as a risk of negative outcomes in the criminal justice system, reflecting the real-world data used to develop this decision support system (Angwin et al.; 2016; Garber, 2016; Garcia, 2016; Flores, Bechtel and Lowenkamp 2016; Liptak, 2017).”
Method
The wiseman in the mirror
Larsson, K. K. (2021) · AI & Society · doi:10.1007/s00146-021-01215-y
“However, AI, believed to be impartial, can make unfair decisions rival-ling to the worst racist, sexist or elitist (Zou and Schiebinger 2018; Garcia 2016).”
Background
Tekoäly, koneoppiminen ja teknologinen murros:
Vartiainen, H., Tedre, M., Jormanainen, I., Kahila, J., Valtonen, T., & Toivonen, T. (2021) · Ainedidaktiikka · doi:10.23988/ad.90776 · Cited by 19
Dumbwaiters and Smartphones: The Responsibility of Intelligence
Schwartz, S. W. (2022) · Social and cultural studies of robots and AI · doi:10.1007/978-3-030-88615-8_5
Rassismus im Netz
Nolden, M. (2019) · Springer Reference Sozialwissenschaften · doi:10.1007/978-3-658-08460-8_73-1
Can the United States' Google Search Algorithm be Reflective of Implicit Racial Bias?
Aleandre, M., & Reyes, M. D. L. (2022) · Journal of Student Research · doi:10.47611/jsrhs.v10i4.2182
11. Moral MindSphere
Bamps, D. A. M., Roeck, V. D., & Verbeke, L. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_11
13. Antiviruses against fake news
Arets, D., Bastiaanse, R. M., & Giesen, A. L. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_13
16. For the citizens
de Graaf, M. J. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_16
3. Moral impact
Kamp, J.-A. M., & van der Vorst, R. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_3
14. Benevolent lies by design
Brankaert, R., Tummers-Heemels, A., & IJsselsteijn, W. A. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_14
19. Towards a moral compass for the future
Bamps, D. A. M., Roeck, V. D., & Verbeke, L. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_19
Front Matter
(2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_fm
20. Looking ahead, and what we can learn from science fiction
Wernaart, B. F. W. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_20
17. AI is not ready for use
Schouten, G. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_17
7. The law as backbone in responsible innovation
Cuijpers, C. (2022) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0_7

Authors: Garcia, M.
Corresponding author: Garcia, Megan
Venue: World Policy Journal
Publisher: Duke University Press
Published: 2016-12-01
Volume: 33
Issue: 4
Pages: 111-117
ISSN: 0740-2775 (ISSN-L)
1936-0924
DOI: 10.1215/07402775-3813015
OpenAlex: W2569124586
Cited by: 257
References: 0
Citations by year: 2026: 8 · 2025: 30 · 2024: 26 · 2023: 34 · 2022: 55 · 2021: 32 · 2020: 37 · 2019: 27 · 2018: 6 · 2017: 2
FWCI: 28.49
Type: journal article
Language: en
Open access: closed
Locations: World Policy Journal (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Social Sciences › Law › Artificial Intelligence in Law
Social Sciences › Law › Digitalization, Law, and Regulation
Keywords: algorithmic bias, institutional bias, implicit bias, algorithmic discrimination, algorithmic accountability, crowd monitoring, algorithmic transparency, algorithmic fairness, sexism, xenophobia, automated decision-making

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 ›

Are you an author, or do you see a problem with this page? Request a review

Racist in the Machine

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks