Applied AI Ethics Reading & Resource List

Below is the full reading and resource list I have compiled for the What to How of AI Ethics project with Luciano Floridi, Libby Kinsey and Anat Elhalal

Current version of the preprint is here: https://arxiv.org/abs/1905.06876

Current version of the typology is here: https://docs.google.com/document/d/1h6nK9K7qspG74_HyVlT0Lx97URM0dRoGbJ3ivPxMhaE/edit

a3i. (n.d.). The Trust-in-AI Framework. Retrieved from http://a3i.ai/trust-in-ai

Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. (2018). Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems — CHI ’18, 1–18. https://doi.org/10.1145/3173574.3174156

Ackerly, B. A. (2018). Human Rights: Principles in Practice Without the Promise of Principles. Human Rights Review, 19(3), 391–394. https://doi.org/10.1007/s12142-018-0523-5

Acquisti, A. (2009). Nudging privacy: The behavioral economics of personal information. IEEE Security and Privacy, 7(6), 82–85. https://doi.org/10.1109/MSP.2009.163

Adamson, G., Havens, J. C., & Chatila, R. (2019). Designing a Value-Driven Future for Ethical Autonomous and Intelligent Systems. Proceedings of the IEEE, 107(3), 518–525. https://doi.org/10.1109/JPROC.2018.2884923

Aequias. Bias and Fairness Audit Toolkit. (n.d.). Retrieved from http://aequitas.dssg.io/

Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., & Wallach, H. (2018). A Reductions Approach to Fair Classification. ArXiv:1803.02453 [Cs]. Retrieved from http://arxiv.org/abs/1803.02453

AI Commons. (n.d.). Retrieved from AI Commons website: https://aicommons.com/

AI Now Institute Algorithmic Accountability Policy Toolkit. (n.d.). Retrieved from https://ainowinstitute.org/aap-toolkit.pdf

AINow Institute. (2018, October 24). AI IN 2018: A YEAR IN REVIEW ETHICS, ORGANIZING, AND ACCOUNTABILITY. Retrieved from https://medium.com/@AINowInstitute/ai-in-2018-a-year-in-review-8b161ead2b4e

AI-RFX Procuement Framework. (n.d.). Retrieved from https://ethical.institute/rfx.html

Aitchison, G. (2018). Are Human Rights Moralistic? Human Rights Review, 19(1), 23–43. https://doi.org/10.1007/s12142-017-0480-4

Aközer, M., & Aközer, E. (2016). Basing Science Ethics on Respect for Human Dignity. Science and Engineering Ethics, 22(6), 1627–1647. https://doi.org/10.1007/s11948-015-9731-4

Alfino, M. (2012). Twenty Years of Information Ethics and the Journal of Information Ethics. Journal of Information Ethics, 21(2), 13–16. https://doi.org/10.3172/JIE.21.2.13

Algorithm Tips Resources and leads for investigating algorithms in society. (n.d.). Retrieved from Northwestern University website: http://algorithmtips.org/resources/

Aliman, N. M., & Kester, L. (2018). Hybrid strategies towards safe “self-aware” superintelligent systems.

Allen, C., Varner, G., & Zinser, J. (2000). Prolegomena to any future artificial moral agent. Journal of Experimental & Theoretical Artificial Intelligence, 12(3), 251–261. https://doi.org/10.1080/09528130050111428

Alshammari, M., & Simpson, A. (2017). Towards a Principled Approach for Engineering Privacy by Design. In E. Schweighofer, H. Leitold, A. Mitrakas, & K. Rannenberg (Eds.), Privacy Technologies and Policy (Vol. 10518, pp. 161–177). https://doi.org/10.1007/978-3-319-67280-9_9

Altman, M. C. (2011). Kant and applied ethics: The uses and limits of Kant’s practical philosophy. Malden, MA: Wiley-Blackwell.

Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete Problems in AI Safety. ArXiv:1606.06565 [Cs]. Retrieved from http://arxiv.org/abs/1606.06565

Anabo, I. F., Elexpuru-Albizuri, I., & Villardón-Gallego, L. (2019). Revisiting the Belmont Report’s ethical principles in internet-mediated research: Perspectives from disciplinary associations in the social sciences. Ethics and Information Technology, 21(2), 137–149. https://doi.org/10.1007/s10676-018-9495-z

Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645

Anderson, M., & Anderson, S. L. (2018). GenEth: A general ethical dilemma analyzer. Paladyn, Journal of Behavioral Robotics, 9(1), 337–357. https://doi.org/10.1515/pjbr-2018-0024

Andreopoulos, G. (2018). Human Rights Reporting: Rights, Responsibilities, and Challenges. Human Rights Review, 19(2), 147–166. https://doi.org/10.1007/s12142-018-0499-1

Antignac, T., Sands, D., & Schneider, G. (2016). Data Minimisation: A Language-Based Approach (Long Version). ArXiv:1611.05642 [Cs]. Retrieved from http://arxiv.org/abs/1611.05642

Antunes, N., Balby, L., Figueiredo, F., Lourenco, N., Meira, W., & Santos, W. (2018). Fairness and Transparency of Machine Learning for Trustworthy Cloud Services. 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 188–193. https://doi.org/10.1109/DSN-W.2018.00063

Archard, D. (2011). WHY MORAL PHILOSOPHERS ARE NOT AND SHOULD NOT BE MORAL EXPERTS: Why Moral Philosophers Are Not and Should Not Be Moral Experts. Bioethics, 25(3), 119–127. https://doi.org/10.1111/j.1467-8519.2009.01748.x

Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilovic, A., … Varshney, K. R. (2018). FactSheets: Increasing Trust in AI Services through Supplier’s Declarations of Conformity. ArXiv:1808.07261 [Cs]. Retrieved from http://arxiv.org/abs/1808.07261

Arnold, T, Kasenberg, D., & Scheutz, M. (2017a). Value Alignment or Misalignment — What Will Keep Systems Accountable? AAAI Workshops.

Arnold, T, Kasenberg, D., & Scheutz, M. (2017b, March 21). Value Alignment or Misalignment — What Will Keep Systems Accountable? Presented at the Workshops at the Thirty-First AAAI Conference on Artificial Intelligence. Retrieved from https://www.aaai.org/ocs/index.php/WS/AAAIW17/paper/viewFile/15216/14648

Arnold, Thomas, & Scheutz, M. (2016). Against the moral Turing test: Accountable design and the moral reasoning of autonomous systems. Ethics and Information Technology, 18(2), 103–115. https://doi.org/10.1007/s10676-016-9389-x

Arnold, Thomas, & Scheutz, M. (2018). The “big red button” is too late: An alternative model for the ethical evaluation of AI systems. Ethics and Information Technology, 20(1), 59–69. https://doi.org/10.1007/s10676-018-9447-7

Arsovski, S., Wong, S. H., & Cheok, A. D. (2018). Open-domain neural conversational agents: The step towards artificial general intelligence. International Journal of Advanced Computer Science and Applications, 9(6), 403–408. https://doi.org/10.14569/IJACSA.2018.090654

Arvan, M. (2014). A Better, Dual Theory of Human Rights: A Better, Dual Theory of Human Rights. The Philosophical Forum, 45(1), 17–47. https://doi.org/10.1111/phil.12025

Arvan, M. (2018). Mental time-travel, semantic flexibility, and A.I. ethics. AI & SOCIETY. https://doi.org/10.1007/s00146-018-0848-2

Atenasio, D. (2018). Co-responsibility for Individualists. Res Publica. https://doi.org/10.1007/s11158-018-09409-w

Audi, R. (2012). Virtue Ethics as a Resource in Business. Business Ethics Quarterly, 22(2), 273–291. https://doi.org/10.5840/beq201222220

Autili, M., Ruscio, D. D. I., Inverardi, P., Pelliccione, P., & Tivoli, M. (2019). A software exoskeleton to protect and support citizen’s ethics and privacy in the digital world. IEEE Access, 7, 62011–62021. https://doi.org/10.1109/ACCESS.2019.2916203

Axtell, G., & Olson, P. (2012). RECENT WORK IN APPLIED VIRTUE ETHICS. American Philosophical Quarterly, 49(3), 183–203. Retrieved from http://www.jstor.org/stable/23213479

Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015). On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation. PLOS ONE, 10(7), e0130140. https://doi.org/10.1371/journal.pone.0130140

Balaram, B., Greenham, T., & Leonard, J. (n.d.). Artificial Intelligence: Real Public Engagement. Retrieved from RSA website: https://www.thersa.org/globalassets/pdfs/reports/rsa_artificial-intelligence---real-public-engagement.pdf

Bassily, R., Thakkar, O., & Thakurta, A. (2018). Model-Agnostic Private Learning via Stability. ArXiv:1803.05101 [Cs]. Retrieved from http://arxiv.org/abs/1803.05101

Bauer, W. A. (2018). Virtuous vs. utilitarian artificial moral agents. AI & SOCIETY. https://doi.org/10.1007/s00146-018-0871-3

Baum, S. D. (2017). Social choice ethics in artificial intelligence. AI & SOCIETY. https://doi.org/10.1007/s00146-017-0760-1

BBC Trending. (2018, December 12). Instagram tightens eating disorder filters after BBC investigation. Retrieved from BBC News website: https://www.bbc.co.uk/news/blogs-trending-46505704

Been, K., Rajiv, K., & Oluwasanmi, K. (2016). Examples Are Not Enough, Learn to Criticize! Criticism for Interpretability. Proceedings of the 30th International Conference on Neural Information Processing Systems. Presented at the NIPS’16, Barcelona, Spain. Retrieved from http://dl.acm.org/citation.cfm?id=3157096.3157352

Beer, D. (2017). The social power of algorithms. Information, Communication & Society, 20(1), 1–13. https://doi.org/10.1080/1369118X.2016.1216147

Bender, E. M., & Friedman, B. (2018). Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science. Transactions of the Association for Computational Linguistics, 6, 587–604. https://doi.org/10.1162/tacl_a_00041

Benghozi, P.-J., & Chevalier, H. (2019). The present vision of AI… or the HAL syndrome. Digital Policy, Regulation and Governance. https://doi.org/10.1108/DPRG-12-2018-0079

Benkler, Y. (2019). Don’t let industry write the rules for AI. Nature, 569(7755), 161–161. https://doi.org/10.1038/d41586-019-01413-1

Berdichevsky, D., & Neuenschwander, E. (1999). Toward an ethics of persuasive technology. Communications of the ACM, 42(5), 51–58. https://doi.org/10.1145/301353.301410

Beretta, E., Santangelo, A., Lepri, B., Vetrò, A., & De Martin, J. C. (2019). The invisible power of fairness. How machine learning shapes democracy. ArXiv:1903.09493 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1903.09493

Bibal, A., & Frénay, B. (2016). Interpretability of Machine Learning Models and Representations: an Introduction.

Billiet, L., Van Huffel, S., & Van Belle, V. (2018). Interval Coded Scoring: A toolbox for interpretable scoring systems. PeerJ Computer Science, 4, e150. https://doi.org/10.7717/peerj-cs.150

Binns, R. (2018a). Algorithmic Accountability and Public Reason. Philosophy & Technology, 31(4), 543–556. https://doi.org/10.1007/s13347-017-0263-5

Binns, R. (2018b). What Can Political Philosophy Teach Us about Algorithmic Fairness? IEEE Security & Privacy, 16(3), 73–80. https://doi.org/10.1109/MSP.2018.2701147

Binns, R. (n.d.). An Overview of the Auditing Framework for Artificial Intelligence and its core components. Retrieved from ICO website: https://ai-auditingframework.blogspot.com/2019/03/an-overview-of-auditing-framework-for_26.html

Binns, R., & Gallo, V. (2019, March 26). An overview of the Auditing Framework for Artificial Intelligence and its core components. Retrieved from Ai Auditing Framework website: https://ai-auditingframework.blogspot.com/2019/03/an-overview-of-auditing-framework-for_26.html

Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018). ‘It’s Reducing a Human Being to a Percentage’: Perceptions of Justice in Algorithmic Decisions. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems — CHI ’18, 1–14. https://doi.org/10.1145/3173574.3173951

Bird, S., Hutchinson, B., Kenthapadi, K., Kıcıman, E., & Mitchell, M. (2019). Fairness-aware machine learning: Practical challenges and lessons learned. The Web Conference 2019 — Companion of the World Wide Web Conference, WWW 2019, 1297–1298. https://doi.org/10.1145/3308560.3320086

Bogosian, K. (2017). Implementation of Moral Uncertainty in Intelligent Machines. Minds and Machines, 27(4), 591–608. https://doi.org/10.1007/s11023-017-9448-z

Bohn, J., Coroamă, V., Langheinrich, M., Mattern, F., & Rohs, M. (2005). Social, Economic, and Ethical Implications of Ambient Intelligence and Ubiquitous Computing. In W. Weber, J. M. Rabaey, & E. Aarts (Eds.), Ambient Intelligence (pp. 5–29). Berlin, Heidelberg: Springer Berlin Heidelberg.

Bolukbasi, T., Chang, K., Zou, J., Saligrama, V., & Kalai. (2016). Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. Presented at the NIPS.

Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., … Roselander, J. (2019). Towards Federated Learning at Scale: System Design. ArXiv:1902.01046 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1902.01046

Bonnemains, V., Saurel, C., & Tessier, C. (2018a). Embedded ethics: Some technical and ethical challenges. Ethics and Information Technology, 20(1), 41–58. https://doi.org/10.1007/s10676-018-9444-x

Borenstein, J., & Arkin, R. (2016). Robotic Nudges: The Ethics of Engineering a More Socially Just Human Being. Science and Engineering Ethics, 22(1), 31–46. https://doi.org/10.1007/s11948-015-9636-2

Bostrom, N., & Yudkowsky, E. (2014). The Ethics of Artificial Intelligence. In K. Frankish & W. M. Ramsey (Eds.), The Cambridge handbook of artificial intelligence. Cambridge, UK: Cambridge University Press.

Bozdag, E. (2013). Bias in algorithmic filtering and personalization. Ethics and Information Technology, 15(3), 209–227. https://doi.org/10.1007/s10676-013-9321-6

Bradley, R. (2017). Decision theory with a human face. Cambridge New York, NY Melbourne, VIC: Cambridge University Press.

Brännmark, J. (2017). Respect for Persons in Bioethics: Towards a Human Rights-Based Account. Human Rights Review, 18(2), 171–187. https://doi.org/10.1007/s12142-017-0450-x

Brennan, J. (2012). For-Profit Business as Civic Virtue. Journal of Business Ethics, 106(3), 313–324. https://doi.org/10.1007/s10551-011-0998-3

Brewer, C. D., & Himes, G. N. (2015). Weighing the Ethical Considerations of Autonomy and Efficacy With Respect to Mandatory Warning Labels. The American Journal of Bioethics, 15(3), 14–15. https://doi.org/10.1080/15265161.2014.998379

Brey, P. A. E. (2012). Anticipating ethical issues in emerging IT. Ethics and Information Technology, 14(4), 305–317. https://doi.org/10.1007/s10676-012-9293-y

Brown, S. (2019). An agile approach to designing for the consequences of technology. Retrieved from DotEveryone website: https://medium.com/doteveryone/an-agile-approach-to-designing-for-the-consequences-of-technology-18a229de763b

Brownstein, M. (2016). Attributionism and Moral Responsibility for Implicit Bias. Review of Philosophy and Psychology, 7(4), 765–786. https://doi.org/10.1007/s13164-015-0287-7

Bryson, J., & Winfield, A. (2017). Standardizing Ethical Design for Artificial Intelligence and Autonomous Systems. Computer, 50(5), 116–119. https://doi.org/10.1109/MC.2017.154

Buechner, J. (2017). “Where do we come from? What are we? Where are we going?”: Critical Review of Wendell Wallach. A dangerous master: how to keep technology from slipping beyond our control. Basic Books, 2015; viii + 328 pp: ISBN 978–0–465–05862–4. Ethics and Information Technology, 19(3), 221–236. https://doi.org/10.1007/s10676-017-9433-5

Buiten, M. C. (2019). Towards Intelligent Regulation of Artificial Intelligence. European Journal of Risk Regulation, 10(01), 41–59. https://doi.org/10.1017/err.2019.8

Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 205395171562251. https://doi.org/10.1177/2053951715622512

Butnaru, C., Benrimoh, D., & Theodorou, A. (n.d.). Humans in AI. Retrieved from http://moralmachine.mit.edu/

Butterworth, M. (2018). The ICO and artificial intelligence: The role of fairness in the GDPR framework. Computer Law & Security Review, 34(2), 257–268. https://doi.org/10.1016/j.clsr.2018.01.004

Calders, T., & Verwer, S. (2010). Three naive Bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery, 21(2), 277–292. https://doi.org/10.1007/s10618-010-0190-x

Calders, T., & Žliobaitė, I. (2013). Why Unbiased Computational Processes Can Lead to Discriminative Decision Procedures. In B. Custers, T. Calders, B. Schermer, & T. Zarsky (Eds.), Discrimination and Privacy in the Information Society (Vol. 3, pp. 43–57). https://doi.org/10.1007/978-3-642-30487-3_3

Caldicott, R. (2017). How do you solve a problem like technology? A systems approach to digital regulation. Retrieved from DotEveryone website: https://medium.com/doteveryone/how-do-you-solve-a-problem-like-technology-a-systems-approach-to-digital-regulation-c0c0d8e11bdf

Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334), 183–186. https://doi.org/10.1126/science.aal4230

Calo, R. (2017). Artificial Intelligence Policy: A Roadmap. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3015350

Caplan, A. L. (2014). Why autonomy needs help. Journal of Medical Ethics, 40(5), 301–302. https://doi.org/10.1136/medethics-2012-100492

Caruana, R., Kangarloo, H., Dionisio, J. D., Sinha, U., & Johnson, D. (1999). Case-based explanation of non-case-based learning methods. Proceedings. AMIA Symposium, 212–215.

Cath, C. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080

Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2017). Artificial Intelligence and the ‘Good Society’: The US, EU, and UK approach. Science and Engineering Ethics. https://doi.org/10.1007/s11948-017-9901-7

Cath, C., Zimmer, M., Lomborg, S., & Zevenbergen, B. (2018). Association of Internet Researchers (AoIR) Roundtable Summary: Artificial Intelligence and the Good Society Workshop Proceedings. Philosophy & Technology, 31(1), 155–162. https://doi.org/10.1007/s13347-018-0304-8

Cavoukian, A., Taylor, S., & Abrams, M. E. (2010). Privacy by Design: Essential for organizational accountability and strong business practices. Identity in the Information Society, 3(2), 405–413. https://doi.org/10.1007/s12394-010-0053-z

Chan, S. (2018). Principle Versus Profit: Debating Human Rights Sanctions. Human Rights Review, 19(1), 45–71. https://doi.org/10.1007/s12142-017-0484-0

Chen, K. (2017). Public Rights, Private Relations by Jean Thomas: New York and Oxford: Oxford University Press, 2015. Human Rights Review, 18(3), 361–362. https://doi.org/10.1007/s12142-017-0465-3

Cheney-Lippold, J. (2017). We are data: Algorithms and the making of our digital selves. New York: New York University Press.

Chouldechova, A. (2016). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. ArXiv:1610.07524 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1610.07524

Chowdhury, R. (n.d.). Tackling the challenges of ethics in AI Fairness Tool. Retrieved from Accenture website: https://www.accenture.com/gb-en/blogs/blogs-cogx-tackling-challenge-ethics-ai

Cimakasky, J., & Polansky, R. (2015). Aristotle and Principlism in Bioethics. Diametros, 59–70 Pages. https://doi.org/10.13153/diam.45.2015.796

Citron, D., & Pasquale, F. (2014). The Scored Society: Due process for automated predictions. Washington Law Review, 89(1), 1–33.

Clancey, W. J. (1983). The epistemology of a rule-based expert system — a framework for explanation. Artificial Intelligence, 20(3), 215–251. https://doi.org/10.1016/0004-3702(83)90008-5

Clark, C. D., & Weaver, M. F. (2015). Balancing Beneficence and Autonomy. The American Journal of Bioethics, 15(7), 62–63. https://doi.org/10.1080/15265161.2015.1042717

Codella, N. C. F., Hind, M., Ramamurthy, K. N., Campbell, M., Dhurandhar, A., Varshney, K. R., … Mojsilović, A. (2019). Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning. ArXiv:1906.02299 [Cs, Stat]. Retrieved from http://arxiv.org/abs/1906.02299

Coeckelbergh, M. (2012). Moral Responsibility, Technology, and Experiences of the Tragic: From Kierkegaard to Offshore Engineering. Science and Engineering Ethics, 18(1), 35–48. https://doi.org/10.1007/s11948-010-9233-3

Cohen, A. J. (2004). What Toleration Is. Ethics, 115(1), 68–95. https://doi.org/10.1086/421982

Colburn, B. (2013). Autonomy and liberalism. Place of publication not identified: Routledge.

Collingridge, D. (1980). The social control of technology. New York: St. Martin’s Press.

Cookson, C. (2018, September 6). Artificial intelligence faces public backlash, warns scientist. Financial Times. Retrieved from https://www.ft.com/content/0b301152-b0f8-11e8-99ca-68cf89602132

Copeland, B. J. (2015). Artificial intelligence : a philosophical introduction. Oxford, UK: [Wiley-Blackwell].

Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017). Algorithmic decision making and the cost of fairness. ArXiv:1701.08230 [Cs, Stat]. https://doi.org/10.1145/3097983.309809

Corrales, D., Ledezma, A., & Corrales, J. (2018). From Theory to Practice: A Data Quality Framework for Classification Tasks. Symmetry, 10(7), 248. https://doi.org/10.3390/sym10070248

Cowls, J., King, T., Taddeo, M., & Floridi, L. (2019). Cowls, Josh and King, Thomas and Taddeo, Mariarosaria and Floridi, Luciano, Designing AI for Social Good: Seven Essential Factors (May 15, 2019). Available at SSRN: https://ssrn.com/abstract=.

Craven, M., & Shavlik, J. (1996). Extracting Thee-Structured Representations of Thained Networks. Presented at the NIPs.

Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311–313. https://doi.org/10.1038/538311a

Cushman, F. (2015). Deconstructing intent to reconstruct morality. Current Opinion in Psychology, 6, 97–103. https://doi.org/10.1016/j.copsyc.2015.06.003

D’Agostino, M., & Durante, M. (2018). Introduction: The Governance of Algorithms. Philosophy & Technology, 31(4), 499–505. https://doi.org/10.1007/s13347-018-0337-z

Dahya, R., & Morris, A. (2019). Toward a Conceptual Framework for Understanding AI Action and Legal Reaction. In M.-J. Meurs & F. Rudzicz (Eds.), Advances in Artificial Intelligence (Vol. 11489, pp. 453–459). https://doi.org/10.1007/978-3-030-18305-9_44

Dai, W., Yoshigoe, K., & Parsley, W. (2018). Improving Data Quality through Deep Learning and Statistical Models. ArXiv:1810.07132 [Cs], 558, 515–522. https://doi.org/10.1007/978-3-319-54978-1_66

Dameski, A. (2018). A Comprehensive Ethical Framework for AI Entities: Foundations. In M. Iklé, A. Franz, R. Rzepka, & B. Goertzel (Eds.), Artificial General Intelligence (Vol. 10999, pp. 42–51). https://doi.org/10.1007/978-3-319-97676-1_5

Data Ethics Canvas. (2019). Retrieved from Open Data Institute: https://docs.google.com/document/d/1OXSrA2KDMVkHroxs_8SUoQZ5Uv0eRhtNNtIl9g_Q47M/edit

Datta, A., Sen, S., & Zick, Y. (2017). Algorithmic Transparency via Quantitative Input Influence. In T. Cerquitelli, D. Quercia, & F. Pasquale (Eds.), Transparent Data Mining for Big and Small Data (Vol. 32, pp. 71–94). https://doi.org/10.1007/978-3-319-54024-5_4

De Mul, J., & Philosophy Documentation Center. (2010). Moral Machines: ICTs as Mediators of Human Agencies. Techné: Research in Philosophy and Technology, 14(3), 226–236. https://doi.org/10.5840/techne201014323

de Vries, K. (2010). Identity, profiling algorithms and a world of ambient intelligence. Ethics and Information Technology, 12(1), 71–85. https://doi.org/10.1007/s10676-009-9215-9

Debias: trying to make word embeddings less sexist. (n.d.). Retrieved from https://github.com/tolga-b/debiaswe

Demšar, J., & Bosnić, Z. (2018). Detecting concept drift in data streams using model explanation. Expert Systems with Applications, 92, 546–559. https://doi.org/10.1016/j.eswa.2017.10.003

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