There is a blind spot in AI research

Big Data's Disparate Impact

Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048 · see citing works

Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission

Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685 · see citing works

The master algorithm: how the quest for the ultimate learning machine will remake our world

(2016) · Choice Reviews Online · doi:10.5860/choice.194685 · Cited by 808 · see citing works

Discrimination in Online Ad Delivery: Google ads, black names and white names, racial discrimination, and click advertising

Sweeney, L. (2013) · Queue · doi:10.1145/2460276.2460278 · Cited by 477 · see citing works

Predictions put into practice: a quasi-experimental evaluation of Chicago’s predictive policing pilot

Saunders, J., Hunt, P., & Hollywood, J. S. (2016) · Journal of Experimental Criminology · doi:10.1007/s11292-016-9272-0 · Cited by 193 · see citing works

Autonomes Fahren

Maurer, M., Gerdes, J. C., Lenz, B., & Winner, H. (2015) · Springer eBooks · doi:10.1007/978-3-662-45854-9 · Cited by 161 · see citing works

The global landscape of AI ethics guidelines

Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · see citing works

Key challenges for delivering clinical impact with artificial intelligence

Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine · doi:10.1186/s12916-019-1426-2 · Cited by 3,136 · see citing works

Conversational agents in healthcare: a systematic review

Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy072 · Cited by 1,388 · see citing works

Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management

Lee, M. K. (2018) · Big Data & Society · doi:10.1177/2053951718756684 · Cited by 1,338 · see citing works

Automation, Algorithms, and Beyond: Why Work Design Matters More Than Ever in a Digital World

Parker, S. K., & Grote, G. (2022) · Applied Psychology · doi:10.1111/apps.12241 · Cited by 960 · see citing works

From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices

Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-019-00165-5 · Cited by 882 · see citing works

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

Ferrara, E. (2023) · Sci · doi:10.3390/sci6010003 · Cited by 875 · see citing works

The impact of artificial intelligence on learner–instructor interaction in online learning

Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00292-9 · Cited by 788 · see citing works

Governing artificial intelligence: ethical, legal and technical opportunities and challenges

Cath, C. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0080 · Cited by 745 · see citing works

Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach

Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2017) · Science and Engineering Ethics · doi:10.1007/s11948-017-9901-7 · Cited by 733 · see citing works

Seven HCI grand challenges

Stephanidis, C., Salvendy, G., Antona, M., Chen, J. Y. C., Dong, J., Duffy, V. G., Fang, X., Fidopiastis, C., Fragomeni, G., Fu, L. P., et al. (2019) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2019.1619259 · Cited by 524 · see citing works

Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry

Köbis, N., & Mossink, L. D. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106553 · Cited by 406 · see citing works

Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard?

Zerilli, J., Knott, A., Maclaurin, J., & Gavaghan, C. (2019) · Philosophy & Technology · doi:10.1007/s13347-018-0330-6 · Cited by 420 · see citing works

Machine learning, social learning and the governance of self-driving cars

Stilgoe, J. (2018) · Social Studies of Science · doi:10.1177/0306312717741687 · Cited by 375 · see citing works

Blockchain-enabled Federated Learning: A Survey

Qu, Y., Uddin, M. P., Gan, C., Xiang, Y., Gao, L., & Yearwood, J. (2023) · ACM Computing Surveys · doi:10.1145/3524104 · Cited by 286 · see citing works

Governing Artificial Intelligence to benefit the UN Sustainable Development Goals

Truby, J. (2020) · Sustainable Development · doi:10.1002/sd.2048 · Cited by 266 · see citing works

Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects

Passi, S., & Jackson, S. J. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274405 · Cited by 225 · see citing works

Generative AI in Higher Education: The ChatGPT Effect

Chan, C. K. Y., & Colloton, T. (2024) · Routledge · doi:10.4324/9781003459026 · Cited by 203 · see citing works

An AI-based decision support system for predicting mental health disorders

Tutun, S., Johnson, M. E., Ahmed, A., Albizri, A., Irgil, S., Yesilkaya, I., Ucar, E. N., Sengun, T., & Harfouche, A. (2023) · Information Systems Frontiers · doi:10.1007/s10796-022-10282-5 · Cited by 177 · see citing works

International migration management in the age of artificial intelligence

Beduschi, A. (2021) · Migration Studies · doi:10.1093/migration/mnaa003 · Cited by 141 · see citing works

Generative AI in higher education and beyond

Hashmi, N., & Bal, A. S. (2024) · Business Horizons · doi:10.1016/j.bushor.2024.05.005 · Cited by 141 · see citing works

Governing the safety of artificial intelligence in healthcare

Macrae, C. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2019-009484 · Cited by 123 · see citing works

The growing ubiquity of algorithms in society: implications, impacts and innovations

Olhede, S. C., & Wolfe, P. J. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2017.0364 · Cited by 120 · see citing works

Responsible AI for conservation

Wearn, O. R., Freeman, R., & Jacoby, D. M. P. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0022-7 · Cited by 112 · see citing works

The “big red button” is too late: an alternative model for the ethical evaluation of AI systems

Arnold, T., & Scheutz, M. (2018) · Ethics and Information Technology · doi:10.1007/s10676-018-9447-7 · Cited by 94 · see citing works

Responsible AI in Africa: Challenges and Opportunities

(2023) · Social and Cultural Studies of Robots and AI · doi:10.1007/978-3-031-08215-3 · Cited by 82 · see citing works

Learning from the Failure of Autonomous and Intelligent Systems: Accidents, Safety, and Sociotechnical Sources of Risk

Macrae, C. (2022) · Risk Analysis · doi:10.1111/risa.13850 · Cited by 76 · see citing works

Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives

Aquino, Y. S. J., Carter, S. M., Houssami, N., Braunack-Mayer, A., Win, K. T., Degeling, C., Wang, L., & Rogers, W. A. (2025) · Journal of Medical Ethics · doi:10.1136/jme-2022-108850 · Cited by 62 · see citing works

Building truths in AI: Making predictive algorithms doable in healthcare

Henriksen, A., & Bechmann, A. (2020) · Information, Communication & Society · doi:10.1080/1369118x.2020.1751866 · Cited by 58 · see citing works

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Christou, C. D., & Tsoulfas, G. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i37.6191 · Cited by 54 · see citing works

The Challenge of Value Alignment: From Fairer Algorithms to AI Safety

Gabriel, I., & Ghazavi, V. (2022) · Oxford Handbook of Digital Ethics · doi:10.1093/oxfordhb/9780198857815.013.18 · Cited by 52 · see citing works

Advancing impact assessment for intelligent systems

Calvo, R. A., Peters, D., & Cave, S. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0151-z · Cited by 39 · see citing works

From models to tools: clinical translation of machine learning studies in psychosis

Mechelli, A., & Vieira, S. (2020) · npj Schizophrenia · doi:10.1038/s41537-020-0094-8 · Cited by 35 · see citing works

Ethics dumping in artificial intelligence

Bélisle-Pipon, J.-C., & Victor, G. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1426761 · Cited by 26 · see citing works

Incorporation of artificial intelligence into nursing research: A scoping review

Yasin, Y. M., Al‐Hamad, A., Metersky, K., & Kehyayan, V. (2025) · International Nursing Review · doi:10.1111/inr.13013 · Cited by 21 · see citing works

The ethics of artificial intelligence in psychotherapy

Manríquez Roa, T., Biller-Andorno, N., & Trachsel, M. (2021) · Oxford Handbook of Psychotherapy Ethics · doi:10.1093/oxfordhb/9780198817338.013.91 · Cited by 19 · see citing works

Enabling ‘AI’? The situated production of commensurabilities

Jaton, F., & Sormani, P. (2023) · Social Studies of Science · doi:10.1177/03063127231194591 · Cited by 17 · see citing works

Does AI Raise Any Distinctive Ethical Questions?

Boddington, P. (2017) · Artificial Intelligence: Foundations, Theory, and Algorithms · doi:10.1007/978-3-319-60648-4_3 · Cited by 15 · see citing works

No wheel but a dial: why and how passengers in self-driving cars should decide how their car drives

Himmelreich, J. (2022) · Ethics and Information Technology · doi:10.1007/s10676-022-09668-5 · Cited by 4 · see citing works

Regulating AI Beyond Product Liability

Trikanad, S. (2026) · Michigan Technology Law Review · doi:10.36645/mtlr.31.2.regulating

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Stronger perceptual filling-in of spatiotemporal information in the blind spot compared to artificial gapsPreprint

Revina, Y., & Maus, G. W. (2019) · doi:10.31234/osf.io/5jfnd · Cited by 2 · see citing works

Test-Retest Variability in Visual Fields With Steep Boundaries: The Blind Spot vs. a Dense Scotoma

Wyatt, H. J. (2009) · Investigative Ophthalmology & Visual Science

Description of Visual Field Defects

Greve, E. L. (1973) · Springer eBooks · doi:10.1007/978-94-011-7765-8_10

A Systematic Review: What is the Normative Size of the Blind Spot Scotoma in Adults?

Rhodes, M. J. (2013) · Ophthalmology Research An International Journal · doi:10.9734/or/2013/4789