Toward Accountable Discrimination-Aware Data Mining: The Importance of Keeping the Human in the Loop—and Under the Looking Glass

Analysing and Assessing Accountability: A Conceptual Framework ¹

Bovens, M. (2007) · European Law Journal · doi:10.1111/j.1468-0386.2007.00378.x · Cited by 2,667 · see citing works

Data collection in a flat world: the strengths and weaknesses of Mechanical Turk samples

Goodman, J. K., Cryder, C. E., & Cheema, A. (2013) · Journal of Behavioral Decision Making · doi:10.1002/bdm.1753 · Cited by 2,242 · see citing works

Inside the Turk: Understanding Mechanical Turk as a Participant Pool

Paolacci, G., & Chandler, J. (2014) · Current Directions in Psychological Science · doi:10.1177/0963721414531598 · Cited by 2,136 · see citing works

An American Dilemma: The Negro Problem and Modern Democracy.

Young, K., Myrdal, G., Sterner, R., & Rose, A. M. (1944) · American Sociological Review · doi:10.2307/2086087 · Cited by 2,017 · see citing works

Certifying and Removing Disparate Impact

Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827 · see citing works

Discrimination in online ad delivery

Sweeney, L. (2013) · Communications of the ACM · doi:10.1145/2447976.2447990 · Cited by 783 · see citing works

Three naive Bayes approaches for discrimination-free classification.

Calders, T., & Verwer, S. (2010) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0190-x · Cited by 764 · see citing works

To Predict and Serve?

Lum, K., & Isaac, W. (2016) · Significance · doi:10.1111/j.1740-9713.2016.00960.x · Cited by 611 · see citing works

Privacy Design Strategies

Hoepman, J.-H. (2014) · IFIP advances in information and communication technology · doi:10.1007/978-3-642-55415-5_38 · Cited by 318 · see citing works

A Methodology for Direct and Indirect Discrimination Prevention in Data Mining

Hajian, S., & Domingo-Ferrer, J. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.72 · Cited by 312 · see citing works

A multidisciplinary survey on discrimination analysis

Romei, A., & Ruggieri, S. (2014) · The Knowledge Engineering Review · doi:10.1017/s0269888913000039 · Cited by 288 · see citing works

Measuring discrimination in algorithmic decision making

Žliobaitė, I. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-017-0506-1 · Cited by 272 · see citing works

Why Muslim Integration Fails in Christian-Heritage Societies

Adida, C. L., Laitin, D. D., & Valfort, M. (2016) · Harvard University Press eBooks · doi:10.4159/9780674088962 · Cited by 253 · see citing works

DECISION MAKING IN AN ORGANIZATIONAL SETTING: COGNITIVE AND ORGANIZATIONAL INFLUENCES ON RISK ASSESSMENT IN COMMERCIAL LENDING.

McNamara, G. M., & Bromiley, P. (1997) · Academy of Management Journal · doi:10.2307/256927 · Cited by 193 · see citing works

Increasing cheat robustness of crowdsourcing tasks

Eickhoff, C., & de Vries, A. P. (2012) · Information Retrieval · doi:10.1007/s10791-011-9181-9 · Cited by 171 · see citing works

Quantifying explainable discrimination and removing illegal discrimination in automated decision making

Kamiran, F., Žliobaitė, I., & Calders, T. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-012-0584-8 · Cited by 99 · see citing works

A blot on the profession

Lowry, S., & Macpherson, G. (1988) · BMJ · doi:10.1136/bmj.296.6623.657 · Cited by 72 · see citing works

Automated Profiling: Minding the Machine: Article 15 of the EC Data Protection Directive and Automated Profiling

Bygrave, L. (2001) · Computer Law and Security Review · doi:10.1016/s0267-3649(01)00104-2 · Cited by 65 · see citing works

Choice Architecture for Human-Computer Interaction

Jameson, A., Berendt, B., Gabrielli, S., Cena, F., Cristina Gena, A., Vernero, F., & Reinecke, K. (2014) · Foundations and Trends® in Human–Computer Interaction · doi:10.1561/1100000028 · Cited by 64 · see citing works

Better decision support through exploratory discrimination-aware data mining: foundations and empirical evidence

Berendt, B., & Preibusch, S. (2014) · Artificial Intelligence and Law · doi:10.1007/s10506-013-9152-0 · Cited by 44 · see citing works

The Statistical Physics of Learning (in a nutshell):News from the stoneage of neural networks

Biehl, M. L. (2018) · University of Groningen research database (University of Groningen / Centre for Information Technology) · Cited by 40 · see citing works

The Meaning of ‘Accountability’ in the Information Privacy Context

Raab, C. D. (2012) · Palgrave Macmillan UK eBooks · doi:10.1057/9781137032225_2 · Cited by 35 · see citing works

Exploring Discrimination: A User-centric Evaluation of Discrimination-Aware Data Mining

Berendt, B., & Preibusch, S. (2012) · 2012 IEEE 12th International Conference on Data Mining Workshops · doi:10.1109/icdmw.2012.109 · Cited by 24 · see citing works

Strong Accountability: Beyond Vague Promises

Butin, D., Chicote, M., & Métayer, D. L. (2013) · Springer eBooks · doi:10.1007/978-94-007-7540-4_16 · Cited by 21 · see citing works

Considerations on Fairness-Aware Data Mining

Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2012) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2012.101 · Cited by 15 · see citing works

Discrimination discovery in scientific project evaluation: A case study

Romei, A., Ruggieri, S., & Turini, F. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.05.016 · Cited by 13 · see citing works

Generalized discrimination discovery on semi-structured data supported by ontology

Thanh, B. L. (2011) · Cited by 5 · see citing works

Explaining Explanations in AI

Mittelstadt, B., Russell, C., & Wachter, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287574 · Cited by 794 · see citing works

Legal and human rights issues of AI: Gaps, challenges and vulnerabilities

Rodrigues, R. (2020) · Journal of Responsible Technology · doi:10.1016/j.jrt.2020.100005 · Cited by 494 · see citing works

Big Data and discrimination: perils, promises and solutions. A systematic review

Favaretto, M., Clercq, E. D., & Elger, B. S. (2019) · Journal Of Big Data · doi:10.1186/s40537-019-0177-4 · Cited by 223 · see citing works

The agency of algorithms: Understanding human-algorithm interaction in administrative decision-making

Peeters, R. (2020) · Information Polity · doi:10.3233/ip-200253 · Cited by 78 · see citing works

Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach

Kolyshkina, I., & Simoff, S. (2021) · Frontiers in Big Data · doi:10.3389/fdata.2021.660206 · Cited by 60 · see citing works

From FAIR data to fair data use: Methodological data fairness in health-related social media research

Leonelli, S., Lovell, R., Wheeler, B. W., Fleming, L. E., & Williams, H. T. P. (2021) · Big Data & Society · doi:10.1177/20539517211010310 · Cited by 52 · see citing works

Explainable outlier detection: What, for Whom and Why?

Sejr, J. H., & Schneider-Kamp, A. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100172 · Cited by 32 · see citing works

Interpretability and Transparency in Artificial Intelligence

Mittelstadt, B. (2022) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198857815.013.20 · Cited by 30 · see citing works

Integrating Behavioral, Economic, and Technical Insights to Understand and Address Algorithmic Bias: A Human-Centric Perspective

Adomavicius, G., & Yang, M. (2022) · ACM Transactions on Management Information Systems · doi:10.1145/3519420 · Cited by 20 · see citing works

Bias in artificial intelligence systems

Rejmaniak, R. (2021) · Białostockie Studia Prawnicze · doi:10.15290/bsp.2021.26.03.02 · Cited by 22 · see citing works

Excitement and Concerns about Machine Learning-Based Chatbots and Talkbots: A Survey

Rivas, P., Holzmayer, K., Hernandez, C. G., & Grippaldi, C. (2018) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas.2018.8638280 · Cited by 14 · see citing works

Inteligencia artificial y derechos de las personas con discapacidad. El poder de los algoritmos

Escolano, R. V. (2023) · Revista Española de Discapacidad · doi:10.5569/2340-5104.11.01.01 · Cited by 12 · see citing works

AI for the Common Good?! Pitfalls, challenges, and ethics pen-testing

Berendt, B. (2019) · Paladyn, Journal of Behavioral Robotics · doi:10.1515/pjbr-2019-0004 · Cited by 8 · see citing works

Discrimination grounds and personalised pricing: Consumer perceptions of fairness, norm alignment, legality, and trust in markets

Heidary, K., van der Rest, J.-P., & Custers, B. (2024) · Internet Policy Review · doi:10.14763/2024.4.1809 · Cited by 7 · see citing works

Artificial intelligence and rights of people with disabilities: The power of algorithms

Escolano, R. V. (2023) · Revista Española de Discapacidad · doi:10.5569/2340-5104.11.01.02 · Cited by 5 · see citing works

Integrating Behavioral, Economic, and Technical Insights to Address Algorithmic Bias: Challenges and Opportunities for IS ResearchPreprint

Adomavicius, G., & Yang, M. (2019) · Social Science Research Network · doi:10.2139/ssrn.3446944 · Cited by 3 · see citing works

Fairness and Artificial Intelligence

Naudts, L., & Vedder, A. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009367783.006 · Cited by 4 · see citing works

Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions

Al-Zanbouri, Z., Sharma, G., & Raza, S. (2024) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi61247.2024.00105 · Cited by 4 · see citing works

Schwerpunkt: Digitale Transformation und Gender Bias

Bröcher, L., Gredel, E., & Schelenz, L. (2023) · GENDER – Zeitschrift für Geschlecht Kultur und Gesellschaft · doi:10.3224/gender.v15i1 · Cited by 3 · see citing works

Algorithmische Entscheidungsfindung aus der Gleichstellungsperspektive – ein Balanceakt zwischen Gender Data Gap, Gender Bias, Machine Bias und Regulierung

Lütz, F. (2023) · GENDER – Zeitschrift für Geschlecht Kultur und Gesellschaft · doi:10.3224/gender.v15i1.03 · Cited by 2 · see citing works

The Terminator of Social Welfare? The Economic Consequences of Algorithmic DiscriminationPreprint

Bauer, K., Pfeuffer, N., Abdel-Karim, B. M., Hinz, O. F., & Kosfeld, M. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3675313 · Cited by 2 · see citing works

Learning by Design: Structuring and Documenting the Human Choices in Machine Learning DevelopmentPreprint

Enni, S. A., & Assent, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.00687 · Cited by 2 · see citing works

AI, Ethics and Philosophy

Smuha, N. A. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009367783.002 · Cited by 1 · see citing works

Fairness von KI-Algorithmen

Hütt, M. ‐., & Schubert, C. (2020) · Springer Reference Geisteswissenschaften · doi:10.1007/978-3-658-23715-8_39-1 · Cited by 1 · see citing works

Mere algorithms can be demotivating

Karumathil, A., & Tripathi, R. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-022-00112-5 · Cited by 1 · see citing works

Contribución al diseño de sistemas respetuosos con la privacidad usando patrones

Ñacato, J. C. C. (2020) · doi:10.20868/upm.thesis.65829

Elementos Reusables para Experimentar con Metodologías Basadas en Estrategias y Patrones de Privacidad

Caiza, J. C., López, G., & Guamán, D. S. (2023) · Revista Politécnica · doi:10.33333/rp.vol51n2.10

Revista Española de Discapacidad

(2023) · Revista Española de Discapacidad · doi:10.5569/2340-5104.11.01

Explainable Artificial Intelligence (Xai) for Facilitating Recognition of Algorithmic Bias: An Experiment from Imposed Users’ PerspectivesPreprint

Chuan, C., Sun, R., Tian, S., & Tsai, W. S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4624907

The hard problem of the androcentric context of AI: challenges for EU policy agendas

González-Martín, J. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-18851-0.00013-5 · Cited by 1 · see citing works

Fairness von KI-Algorithmen

Hütt, M. ‐., & Schubert, C. (2024) · Springer eBooks · doi:10.1007/978-3-658-19606-6_39

Gaps in Algorithmic AI Audits: Legal and Transparency Issues in Cryptocurrency Trading

Arsyad, I., Kharisma, D. B., & Wiwoho, J. (2026) · International Journal of Legal Information · doi:10.1515/ijli-2025-0013

Better decision support through exploratory discrimination-aware data mining: foundations and empirical evidence

Berendt, B., & Preibusch, S. (2014) · Artificial Intelligence and Law · doi:10.1007/s10506-013-9152-0 · Cited by 44 · see citing works

Fighting Algo-Discrimination: Relying on AI's Learning Skills and Embedding Fair Information Principles within Machines’ Heart-Design-SpecificationsPreprint

Bouchagiar, G., & Canellopoulou‐Bottis, M. (2018) · SSRN Electronic Journal

Algorithmic discrimination: How do big data tools unwittingly cause disparity?

Harju, E. (2022) · Aaltodoc (Aalto University)

Data Mining and Automated Discrimination: A Mixed Legal/Technical Perspective

Carmichael, L., Stalla-Bourdillon, S., & Staab, S. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.96 · Cited by 25 · see citing works

Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining

Hajian, S., Bonchi, F., & Castillo, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2945386 · Cited by 334 · see citing works