The combination of increased availability of large amounts of fine-grained human behavioral data and advances in machine learning is presiding over a growing reliance on algorithms to address complex societal problems. Algorithmic decision-making processes might lead to more objective and thus potentially fairer decisions than those made by humans who may be influenced by greed, prejudice, fatigue, or hunger. However, algorithmic decision-making has been criticized for its potential to enhance discrimination, information and power asymmetry, and opacity. In this paper, we provide an overview of available technical solutions to enhance fairness, accountability, and transparency in algorithmic decision-making. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy-makers, and citizens to co-develop, deploy, and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness and transparency. In doing so, we describe the Open Algortihms (OPAL) project as a step towards realizing the vision of a world where data and algorithms are used as lenses and levers in support of democracy and development.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)
Akerlof, G. (1970). The market for lemons: quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84(3), 488–500.
Akerlof, G., & Shiller, R. (2009). Animal spirits: how human psychology drives the economy, and why it matters for global capitalism. Princeton: Princeton University Press.
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.
Barocas, S., & Selbst, A. (2016). Big data’s disparate impact. California Law Review, 104, 671–732.
Barry-Jester, A.M., Casselman, B., & Goldstein, D. (2015). The new science of sentencing. The Marshall Project. https://www.themarshallproject.org/2015/08/04/the-new-science-of-sentencing.
Bhargava, R., Deahl, E., Letouzé, E., Noonan, A., Sangokoya, D., & Shoup, N. (2015). Beyond data literacy: reinventing community engagement and empowerment in the age of data. Data-Pop Alliance White Paper Series. http://datapopalliance.org/wp-content/uploads/2015/11/Beyond-Data-Literacy-2015.pdf.
Boyd, D., & Crawford, K. (2012). Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Information, Communication, & Society, 15(5), 662–679.
Burrell, J. (2016). How the machine thinks: understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12.
Calders, T., & Verwer, S. (2010). Three naive bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery, 21(2), 277–292.
Calders, T., & Zliobaite, I. (2013). Why unbiased computational processes can lead to discriminative decision procedures. In Custers, B., Calders, T., Schermer, B., & Zarsky, T. (Eds.) Discrimination and privacy in the information society (pp. 43–57).
Caruana, R., Kangarloo, H., David, J., Dionisio, N., Sinha, U., & Johnson, D. (1999). Case-based explanation of non-case-based learning methods. In Proceedings of the 1999 american medical informatics association (AMIA) symposium (pp. 212–215).
Chalfin, A., Danieli, O., Hillis, A., Jelveh, Z., Luca, M., Ludwig, J., & Mullainathan, S. (2016). Productivity and selection of human capital with machine learning. American Economic Review, 106(5), 124–127.
Chouldechova, S. (2016). Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. arXiv:1610.07524.
Christin, A., Rosenblatt, A., & Boyd, D. (2015). Courts and predictive algorithms. Data & Civil Rights Primer.
Citron, D., & Pasquale, F. (2014). The scored society. Washington Law Review, 89(1), 1–33.
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017). Fair algorithms and the equal treatment principle. Working Paper.
Crawford, K., & Schultz, J. (2014). Big data and due process: toward a framework to redress predictive privacy harms. Boston College Law Review, 55(1), 93–128.
Datta, A., Tschantz, M.C., & Datta, A. (2015). Automated experiments on ad privacy settings. In Proceedings on privacy enhancing technologies (pp. 92–112).
Diakopoulos, N. (2015). Algorithmic accountability: journalistic investigation of computational power structures. Digital Journalism.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness throug awareness. In Proceedings of the 3rd innovations in theoretical computer science conference (pp. 214–226). New York: ACM.
Dworkin, R. (2000). Sovereign virtue: the theory and the practice of equality. Cambridge: Harvard University Press.
Feldman, M., Friedler, S., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015). Certifying and removing disparate impact. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 259–268).
Fiske, S. (1998). Stereotyping, prejudice, and discrimination. In Gilbert, D., Fiske, S., & Lindzey, G. (Eds.) Handbook of social psychology (pp. 357–411). Boston: McGraw-Hill.
Foster, D., & Vohra, R.V. (1998). Asymptotic calibration. Biometrika, 85(2), 379–390.
Friedler, S.A., Scheidegger, C., & Venkatasubramanian, S. (2016). On the (im)possibility of fairness. arXiv:1609.07236.
Gillespie, T. (2014). The relevance of algorithms. In Gillespie, T., Boczkowski, P., & Foot, K. (Eds.) Media technologies: essays on communication, materiality, and society (pp. 167–193). Cambridge: MIT Press.
Hajian, S., Bonchi, F., & Castillo, C. (2016). Algorithmic bias: from discrimination discovery to fairness-aware data mining. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 2125–2126). New York: ACM.
Hardt, M., Megiddo, N., Papadimitriou, C., & Wootters, M. (2016). Strategic classification. In Proceedings of the 2016 ACM conference on innovations in theoretical computer science (pp. 111–122). New York: ACM.
Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. In Proceedings of the international on advances in neural information processing systems (NIPS) (pp. 3315–3323).
Joseph, M., Kearns, M., Morgenstern, J., Neel, S., & Roth, A. (2016). Rawlsian fairness for machine learning. arXiv:1610.09559.
Kamiran, F., Calders, T., & Pechenizkiy, M. (2010). Discrimination aware decision tree learning. In Proceedings of 2010 IEEE international conference on data mining (pp. 869–874). Washington, DC: IEEE.
Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2011). Fairness-aware classifier with prejudice remover regularizer, In: Proceedings of the european conference on machine learning and principles of knowledge discovery in databases (ECMLPKDD), Part II (pp. 35–50).
Kearns, M., & Nevmyvaka, Y. (2013). Machine learning for market microstructure and high frequency trading. In O’hara, M., Lopez de prado, M., & Easley, D. (Eds.) High frequency trading. London: Risk books.
Khandani, A.E., Kim, A.J., & Lo, A.W. (2010). Consumer credit risk models via machine-learning algorithms. Journal of Banking and Finance, 34, 2767–2787.
Khanna, P. (2017). Technocracy in America: rise of the info-state. CreateSpace Independent Publishing Platform.
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. In Proceedings of the 8th innovations in theoretical computer science conference. New York: ACM.
Kroll, J.A., Huey, J., Barocas, S., Felten, E.W., Reidenberg, J.R., Robinson, D.G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165, 633–707.
Lepri, B., Staiano, J., Sangokoya, D., Letouzé, E., & Oliver, N. (2017). The tyranny of data? The bright and dark sides of data-driven decision-making for social good. arXiv:1612.00323.
Lipton, Z.C. (2016). The mythos of model interpretability. In 2016 ICML workshop on human interpretability in machine learning.
Lou, Y., Caruana, R., Gehrke, J., & Hooker, G. (2012). Accurate intelligible models with pairwise interactions. In Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 623–631). New York: ACM.
Macnish, K. (2012). Unblinking eyes: the ethics of automating surveillance. Ethics and Information Technology, 14(2), 151–167.
McAuley, J., & Leskovec, J. (2013). Hidden factors and hidden topics: understanding rating dimensions with review text. In Proceedings of the 7th ACM conference on recommender systems.
Munoz, C., Smith, M., & Patil, D. (2016). Big data: a report on algorithmic systems, opportunity, and civil rights. Tech. rep., Executive Office of the President.
Nozick, R. (1974). Anarchy, state, and utopia. New York: Basic Books.
O’Neil, C. (2016). Weapons of math destruction: how big data increases inequality and threatens democracy. Crown.
Pager, D., & Shepherd, H. (2008). The sociology of discrimination: racial discrimination in employment, housing, credit and consumer market. Annual Review of Sociology, 34, 181–209.
Pasquale, F. (2015). The Black Blox Society: the secret algorithms that control money and information. Cambridge: Harvard University Press.
Pedreschi, D., Ruggieri, S., & Turini, F. (2008). Discrimination-aware data mining. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 560–568).
Pentland, A. (2014). Saving big data from itself. Scientific American, 311(2), 64–67.
Podesta, J., Pritzker, P., Moniz, E., Holdren, J., & Zients, J. (2014). Big data: seizing opportunities, preserving values. Tech. rep., Executive Office of the President.
Ramirez, E., Brill, J., Ohlhausen, M., & McSweeny, T. (2016). Big data: a tool for inclusion or exclusion? Tech. rep., Federal Trade Commission.
Rawls, J. (1971). A theory of justice. Cambridge: Harvard University Press.
Ribeiro, M., Singh, S., & Guestrin, C. (2016). Why should I trust you?: explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135–1144).
Roemer, J.E. (1996). Theories of distributive justice. Cambridge: Harvard University Press.
Roemer, J.E. (1998). Equality of opportunity. Cambridge: Harvard University Press.
Romei, A., & Ruggieri, S. (2014). A multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review, 29(5), 582–638.
Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of Risk and Uncertainty, 1(1), 7–59.
San Pedro, J., Proserpio, D., & Oliver, N. (2015). Mobiscore: towards universal credit scoring from mobile phone data. In Proceedings of the international conference on user modeling, adaptation and personalization (UMAP) (pp. 195–207).
Sandvig, C., Hamilton, K., Karahalios, K., & Langbort, C. (2014). Auditing algorithms: research methods for detecting discrimination on internet platforms. In Data and discrimination: converting critical concerns into productive inquiry, a preconference at the 64th annual meeting of the international communication association.
Schermer, B.W. (2011). The limits of privacy in automated profiling and data mining. Computer Law & Security Review, 27(1), 45–52.
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013). Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv:1312.6034.
Sunstein, C. (2012). Regulation in an uncertain world. National Academy of Sciences. https://www.whitehouse.gov/sites/default/files/omb/inforeg/speeches/regulation-in-an-uncertain-world-06202012.pdf.
Sweeney, L. (2013). Discrimination in online ad delivery. Available at SSRN: http://ssrn.com/abstract=2208240.
Tobler, C. (2008). Limits and potential of the concept of indirect discrimination. Tech. rep., European Network of Legal Experts in Anti-Discrimination.
Tverksy, A., & Kahnemann, D. (1974). Judgment under uncertainty: heuristics and biases. Science, 185(4157), 1124–1131.
Wang, T., Rudin, C., Wagner, D., & Sevieri, R. (2013). Learning to detect patterns of crime. In Machine learning and knowledge discovery in databases (pp. 515–530). Springer.
Willson, M. (2016). Algorithms (and the) everyday. Information, Communication & Society.
Zafar, M.B., Martinez, I.V., Rodriguez, M.D., & Gummadi, K.P. (2015). Learning fair classifiers. arXiv:1507.05259.
Zarsky, T. (1989). Automated prediction: perception, law and policy. Communications of the ACM, 4, 167–186.
Zarsky, T. (2016). The trouble with algorithmic decisions: an analytic road map to examine efficiency and fairness in automated and opaque decision making. Science, Technology, and Human Values, 41(1), 118–132.
Zemel, R., Wu, Y., Swersky, K., Pitassi, T., & Dwork, C. (2012). Learning fair representation. In Proceedings of the 2013 international conference on machine learning (ICML) (pp. 325–333).
About this article
Cite this article
Lepri, B., Oliver, N., Letouzé, E. et al. Fair, Transparent, and Accountable Algorithmic Decision-making Processes. Philos. Technol. 31, 611–627 (2018). https://doi.org/10.1007/s13347-017-0279-x
- Algorithmic decision-making
- Algorithmic transparency
- Social good