References
- Adadi, A., and M. Berrada. 2018. “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI).” IEEE Access 6: 52138–52160. doi:10.1109/ACCESS.2018.2870052.
- Adams, R. 2021. “Can Artificial Intelligence be Decolonized?” Interdisciplinary Science Reviews 46 (1-2): 176–197. doi:10.1080/03080188.2020.1840225.
- Antoniadi, A. M., Y. Du, Y. Guendouz, L. Wei, C. Mazo, B. A. Becker, and C. Mooney. 2021. “Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review.” Applied Sciences 11 (11): 5088. doi:10.3390/app11115088. MDPI AG.
- Aulck, L., D. Nambi, and J. West. 2019. “Using Machine Learning and Genetic Algorithms to Optimize Scholarship Allocation for Student Yield.” In SIGKDD ‘19: ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 4–8, 2019, Anchorage, AK. ACM, New York, NY, USA. doi:10.1145/1122445.1122456.
- Baker, R. S., and A. Hawn. 2021. “Algorithmic Bias in Education.” doi:10.35542/osf.io/pbmvz.
- Baur, D. 2020. “Four Reasons Why Hyping AI is an Ethical Problem.” Medium. https://dorotheabaur.medium.com/four-reasons-why-hyping-ai-is-an-ethical-problem-8db47b17bf43.
- Beetham, H., A. Collier, L. Czerniewicz, B. Lamb, Y. Lin, J. Ross, A.-M. Scott, and A. Wilson. 2022. “Surveillance Practices, Risks and Responses in the Post Pandemic University.” Digital Culture & Education 14 (1): 16–37. https://www.digitalcultureandeducation.com/volume-14-1.
- Birhane, A., E. Ruane, T. Laurent, M. S. Brown, J. Flowers, A. Ventresque, and C. L. Dancy. 2022. “The Forgotten Margins of AI Ethics.” FAccT ‘22: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Forthcoming). doi:10.1145/3531146.3533157.
- Bloch-Wehba, H. 2020. “Access to Algorithms.” Fordham Law Review 1265. https://ir.lawnet.fordham.edu/flr/vol88/iss4/2.
- Bulathwela, S., M. Pérez-Ortiz, C. Holloway, and J. Shawe-Taylor. 2021. “Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution.” 35th Conference on Neural Information Processing Systems (NeurIPS 2021). ArXiv, abs/2112.02034. doi:10.48550/arXiv.2112.02034.
- Byrne, R. M. J. 2019. “Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning.” In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence Survey track. 6276–6282. doi:10.24963/ijcai.2019/876.
- Carman, M., and B. Rosman. 2020. “Applying a Principle of Explicability to AI Research in Africa: Should We Do It?” Ethics and Information Technology. doi:10.1007/s10676-020-09534-2.
- Chatfield, T. 2020. “There’s No Such Thing As ‘Ethical A.I.’.” Medium. https://onezero.medium.com/theres-no-such-thing-as-ethical-a-i-38891899261d.
- Coghlan, S., T. Miller, and J. Paterson. 2021. “Good Proctor or “Big Brother”? Ethics of Online Exam Supervision Technologies.” Philosophy and Technology 34: 1581–1606. doi:10.1007/s13347-021-00476-1.
- Cole, S. 2022. “Google’s AI-Powered ‘Inclusive Warnings’ Feature Is Very Broken.” Vice (19th April). https://www.vice.com/en/article/v7dk8m/googles-ai-powered-inclusive-warnings-feature-is-very-broken.
- Crawford, K. 2021. The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press. doi:10.2307/j.ctv1ghv45t.
- Crawford, K. and Joler, V. (2018). “Anatomy of an AI System: The Amazon Echo As An Anatomical Map of Human Labor, Data and Planetary Resources.” AI Now Institute and Share Lab. https://anatomyof.ai/
- Čyras, K., A. Rago, E. Albini, P. Baroni, and F. Toni. 2021. “Argumentative XAI: A Survey.” In 30th International Joint Conference on Artificial Intelligence, edited by Z.-H. Zhou, 4392–4399. Montreal: IJCAI.
- Dignum, V. 2021. “The Role and Challenges of Education for Responsible AI.” London Review of Education 19 (1): 1–11. doi:10.14324/LRE.19.1.01.
- Ehsan, U., Q. Vera Liao, M. Muller, M. O. Riedl, and J. D. Weisz. 2021. “Expanding Explainability: Towards Social Transparency in AI Systems.” In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ‘21). Association for Computing Machinery, New York, NY, USA, Article 82, 1–19. doi:10.1145/3411764.3445188.
- Elicit. n.d. https://elicit.org/search.
- European Commission. 2018. “Statement on Artificial Intelligence, Robotics and ‘Autonomous’ Systems.” European Commission Directorate-General for Research and Innovation, European Group on Ethics in Science and New Technologies, Brussels, 9 March 2018, Publications Office. doi:10.2777/531856.
- European Commission. 2021. Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL LAYING DOWN HARMONISED RULES ON ARTIFICIAL INTELLIGENCE (ARTIFICIAL INTELLIGENCE ACT) AND AMENDING CERTAIN UNION LEGISLATIVE ACTS. 2021/0106(COD). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri = CELEX%3A52021PC0206.
- European Parliament. 2022. “REPORT on Artificial Intelligence in a Digital Age (2020/2266(INI)).” Special Committee on Artificial Intelligence in a Digital Age. Rapporteur: Axel Voss. European Parliament. https://www.europarl.europa.eu/doceo/document/A-9-2022-0088_EN.pdf.
- EU. 2018. “Statement on Artificial Intelligence, Robotics and ‘Autonomous' Systems.” European Union. https://op.europa.eu/en/publication-detail/-/publication/dfebe62e-4ce9-11e8-be1d-01aa75ed71a1
- Everett, J. 2021. “From A-Levels to Pensions, Algorithms Make Easy Targets – But They Aren’t to Blame.” Guardian (17th August). https://www.theguardian.com/commentisfree/2021/aug/17/a-levels-pensions-algorithms-easy-targets-blame-mutant-maths.
- Fazelpour, S., and M. De-Arteaga. 2022. “Diversity in Sociotechnical Machine Learning Systems.” Big Data & Society, doi:10.1177/20539517221082027.
- Feathers, T. 2021. “AI Can Guess Your Race Based On X-Rays, and Researchers Don't Know How.” Motherboard. https://www.vice.com/en/article/wx5ypb/ai-can-guess-your-race-based-on-x-rays-and-researchers-dont-know-how.
- Felten, E. 2017. “What Does It Mean to Ask for an “Explainable” Algorithm?” Accessed 29 August, 2017. https://freedom-to-tinker.com/2017/05/31/what-does-it-mean-to-ask-for-anexplainable-algorithm/.
- Fiok, K., F. V. Farahani, W. Karwowski, and T. Ahram. 2022. “Explainable Artificial Intelligence for Education and Training.” The Journal of Defense Modeling and Simulation 19 (2): 133–144. doi:10.1177/15485129211028651.
- Floridi, L., and J. Cowls. 2019. “A Unified Framework of Five Principles for AI in Society.” Harvard Data Science Review 1 (1), doi:10.1162/99608f92.8cd550d1.
- Floridi, L., and J. Cowls. 2019. “A Unified Framework of Five Principles for AI in Society.” Harvard Data Science Review 1 (1), doi:10.1162/99608f92.8cd550d1.
- Floridi, L., J. Cowls, M. Beltrametti, et al. 2018. “AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations.” Minds & Machines 28: 689–707. doi:10.1007/s11023-018-9482-5.
- Floridi, L., M. Holweg, M. Taddeo, J. Amaya Silva, J. Mökander, and Y. Wen. 2022. “capAI - A Procedure for Conducting Conformity Assessment of AI Systems in Line with the EU Artificial Intelligence Act (March 23, 2022).” Available at SSRN: https://ssrn.com/abstract = 4064091. doi:10.2139/ssrn.4064091.
- Future of Life. 2017. “Asilomar AI Principles.” https://futureoflife.org/ai-principles/.
- Gilpin, L. H., D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal. 2018. “Explaining Explanations: An Overview of Interpretability of Machine Learning.” In 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), 2018, pp. 80-89, doi:10.1109/DSAA.2018.00018.
- Google Scholar. n.d. https://scholar.google.com/.
- Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F. and Pedreschi, D. (2018, August). “A Survey of Methods for Explaining Black Box Models.” ACM Computing Surveys 51 (5): Article 93. doi:10.1145/3236009.
- Gunning, D., M. Stefik, J. Choi, T. Miller, S. Stumpf, and G. Z. Yang. 2019. “XAI—Explainable Artificial Intelligence.” Science Robotics 4 (37), doi:10.1126/scirobotics.aay7120.
- Hanif, A., X. Zhang, and S. Wood. 2021. “A Survey on Explainable Artificial Intelligence Techniques and Challenges.” In IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW). pp. 81–89, doi:10.1109/EDOCW52865.2021.00036.
- Hao, K., and A. P. Hernández. 2022. “How the AI Industry Profits From Catastrophe.” MIT Technology Review (April 20th). https://www.technologyreview.com/2022/04/20/1050392/ai-industry-appen-scale-data-labels.
- Heaven, H. W. 2021. “Hundreds of AI Tools Have Been Built to Catch Covid. None of Them Helped.” MIT Technology Review. https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/.
- Hickok, M. 2021. “Lessons Learned from AI Ethics Principles for Future Actions.” AI Ethics 1: 41–47. doi:10.1007/s43681-020-00008-1.
- HoL. 2018. “AI in the UK: ready, willing and able? House of Lords Select Committee on Artificial Intelligence Report of Session 2017–19.” HL Paper 100. https://publications.parliament.uk/pa/ld201719/ldselect/ldai/100/100.pdf.
- Hu, B., P. Tunison, B. Vasu, N. Menon, R. Collins, and A. Hoogs. 2021. “XAITK: The Explainable AI Toolkit.” Applied AI Letters 2 (4), doi:10.1002/ail2.40.
- Human Rights Watch. 2022. “How Dare They Peep into My Private Life?” Children’s Rights Violations by Governments that Endorsed Online Learning During the Covid-19 Pandemic. Human Rights Watch. https://www.hrw.org/report/2022/05/25/how-dare-they-peep-my-private-life/childrens-rights-violations-governments.
- IEEE. n.d. Ethically Aligned Design: A Vision for Prioritizing Human Well-Being with Autonomous and Intelligent Systems. Institute of Electrical and Electronics Engineers. https://standards.ieee.org/content/dam/ieee-standards/standards/web/documents/other/ead_v2.pdf.
- Jardas, E., D. Wasserman, and D. Wendler. 2022. “Autonomy-based Criticisms of the Patient Preference Predictor.” Journal of Medical Ethics 48 (5): 304–10. doi:10.1136/medethics-2021-107629.
- Jiang, J., S. Kahai, and M. Yang. 2022. “Who Needs Explanation and When? Juggling Explainable AI and User Epistemic Uncertainty.” International Journal of Human-Computer Studies 165), doi:10.1016/j.ijhcs.2022.102839.
- Kaye, K. 2022. “The FTC’s New Enforcement Weapon Spells Death for Algorithms.” Protocol (March 14th). https://www.protocol.com/policy/ftc-algorithm-destroy-data-privacy.
- Khosravi, H., S. Buckingham Shum, G. Chen, C. Conati, Y.-S. Tsai, J. Kay, S. Knight, R. Martinez-Maldonado, S. Sadiq, and D. Gašević. 2022. “Explainable Artificial Intelligence in Education.” Computers and Education: Artificial Intelligence 3), doi:10.1016/j.caeai.2022.100074.
- Kiecza, D. 2022. “Practice Sets: A More Personal Path to Learning.” Google Classroom Blog. https://blog.google/outreach-initiatives/education/introducing-practice-sets/.
- Kiourti, P., K. Wardega, S. Jha, and W. Li. 2019. “TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents.” ArXiv, abs/1903.06638.
- Lecy, J., and K. Beatty. 2012. “Representative Literature Reviews Using Constrained Snowball Sampling and Citation Network Analysis.” SSRN Electronic Journal, doi:10.2139/ssrn.1992601.
- Luckin, R., W. Holmes, M. Griffiths, and L. B. Forcier. 2016. Intelligence Unleashed. An Argument for AI in Education. London: Pearson. https://discovery.ucl.ac.uk/id/eprint/1475756/.
- Market Research Future. 2020. “Artificial Intelligence Education Market Research Report.” https://www.marketresearchfuture.com/reports/artificial-intelligence-education-market-6365.
- Markus, A. F., J. A. Kors, and P. R. Rijnbeek. 2021. “The Role of Explainability in Creating Trustworthy Artificial Intelligence for Health Care: A Comprehensive Survey of the Terminology, Design Choices, and Evaluation Strategies.” Journal of Biomedical Informatics 113), doi:10.1016/j.jbi.2020.103655.
- Montréal Declaration for a Responsible Development of Artificial Intelligence. 2017. https://www.montrealdeclaration-responsibleai.com/the-declaration.
- Morley, J., A. Elhalal, F. Garcia, L. Kinsey, J. Mökander, and L. Floridi. 2021. “Ethics as a service: a pragmatic operationalisation of AI Ethics (February 11th).” Available at SSRN: https://ssrn.com/abstract = 3784238. doi:10.2139/ssrn.3784238.
- Morten, C. 2022. “Publicizing Corporate Secrets for Public Good.” University of Pennsylvania Law Review, Vol. 171, Forthcoming. doi:10.2139/ssrn.4041556.
- Moss, E., E. A. Watkins, R. Singh, M. C. Elish, and J. Metcalf. 2021. “Assembling Accountability: Algorithmic Impact Assessment for the Public Interest.” Data & Society. https://datasociety.net/library/assembling-accountability-algorithmic-impact-assessment-for-the-public-interest/.
- Mueller, S. T., R. R. Hoffman, W. Clancey, A. Emrey, and G. Klein. 2019. “Explanation in human-AI systems: A literature meta-review, synopsis of key ideas and publications, and bibliography for explainable AI [Preprint].” DARPA XAI Literature Review. February 9. https://arxiv.org/pdf/1902.01876.pdf.
- Noble, S. U. 2018. Algorithms of Oppression. NYU Press.
- OpenAI. 2023. “ChatGPT.” https://chat.openai.com/auth/login.
- Orr, D., M. Weller, and R. Farrow. 2018. “Models for Online, Open, Flexible and Technology-Enhanced Higher Education Across the Globe – A Comparative Analysis.” International Council for Open and Distance Education (ICDE). Oslo, Norway. https://oofat.oerhub.net/OOFAT/ CC-BY-SA.
- Páez, A. 2019. “The Pragmatic Turn in Explainable Artificial Intelligence (XAI).” Minds & Machines 29: 441–459. doi:10.1007/s11023-019-09502-w.
- Panigutti, C., A. Perotti, and D. Pedreschi. 2020. “Doctor XAI: An Ontology-Based Approach to Black-Box Sequential Data Classification Explanations.” In Conference on Fairness, Accountability, and Transparency (FAT* ‘20), January 27–30, 2020, Barcelona, Spain. ACM, New York, NY, USA, 11 pages. doi:10.1145/3351095.3372855.
- Partnership on AI. 2018. “About Us.” https://partnershiponai.org/about/.
- Pasquale, F. 2020. New Laws of Robotics: Defending Human Expertise in the Age of AI. Harvard University Press.
- Peterson, D., K. Goode, and D. Gehlhaus. 2021. AI Education in China and the United States: A Comparative Assessment. Center for Security and Emerging Technology.
- Prinsloo, P., S. Slade, and M. Khalil. 2022. “At the Intersection of Human and Algorithmic Decision-Making in Distributed Learning.” Journal of Research on Technology in Education, doi:10.1080/15391523.2022.2121343.
- Ricaurte, P. 2022. “Artificial Intelligence and the Feminist Decolonial Imagination.” Bot Populi (March 4th). https://botpopuli.net/artificial-intelligence-and-the-feminist-decolonial-imagination/.
- Robbins, S. A. 2019. “Misdirected Principle with a Catch: Explicability for AI.” Minds & Machines 29: 495–514. doi:10.1007/s11023-019-09509-3.
- Roio, D. 2018. “Algorithmic Sovereignty.” PhD diss., The University of Plymouth. http://hdl.handle.net/10026.1/11101.
- Russell, S. J., and P. Norvig. 2021. Artificial Intelligence: A Modern Approach. 4th ed. Prentice Hall.
- Saeed, W., and C. Omlin. 2021. “Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities.”. doi:10.48550/arXiv.2111.06420.
- Samuel, S. 2021. “AI’s Islamophobia problem.” Vox. https://www.vox.com/future-perfect/22672414/ai-artificial-intelligence-gpt-3-bias-muslim.
- Schlegel, U., H. Arnout, M. El-Assady, D. Oelke, and D. A. Keim. 2019. “Towards A Rigorous Evaluation Of XAI Methods On Time Series.” In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) pp. 4197–4201, doi:10.1109/ICCVW.2019.00516.
- Schwab, K. 2016. The Fourth Industrial Revolution. World Economic Forum.
- Searle, J. 1980. “Minds, Brains and Programs.” Behavioral and Brain Sciences 3 (3): 417–457.
- Selbst, A. D., D. Boyd, F. Sorelle, V. Suresh, and J. Vertesi. 2018. “Fairness and Abstraction in Sociotechnical Systems (August 23, 2018).” In 2019 ACM Conference on Fairness, Accountability, and Transparency (FAT*), 59-68, Available at SSRN: https://ssrn.com/abstract = 3265913.
- Sharples, M., and R. Pérez y Pérez. 2022. Story Machines: How Computers Have Become Creative Writers. Routledge.
- Statista. 2020. “Artificial Intelligence (AI) worldwide - Statistics & Facts.” https://www.statista.com/topics/3104/artificial-intelligence-ai-worldwide/.
- Timms, M. J. 2016. “Letting Artificial Intelligence in Education Out of the Box: Educational Cobots and Smart Classrooms.” International Journal of Artificial Intelligence in Education 26: 701–712. doi:10.1007/s40593-016-0095-y.
- Tjoa, E., and C. Guan. 2021. “A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI.” IEEE Transactions on Neural Networks and Learning Systems 32 (11): 4793–4813. DOI:10.1109/tnnls.2020.3027314. PMID: 33079674.
- Tong, S., N. Jia, X. Luo, and Z. Fang. 2021. “The Janus Face of Artificial Intelligence Feedback: Deployment Versus Disclosure Effects on Employee Performance.” Strategic Management Journal 42), doi:10.1002/smj.3322.
- Tutt, A. 2020. “An FDA for Algorithms.” Administrative Law Review, 69(1). https://administrativelawreview.org/wp-content/uploads/sites/2/2019/09/69-1-Andrew-Tutt.pdf.
- United Nations. 2021. “The Right to Privacy in the Digital Age.” Report of the United Nations High Commissioner for Human Rights. A/HRC/48/31. https://documents-dds-ny.un.org/doc/UNDOC/GEN/G21/249/21/PDF/G2124921.pdf.
- Véliz, C. 2021. “Moral Zombies: Why Algorithms are not Moral Agents.” AI & Society 36: 487–497. doi:10.1007/s00146-021-01189-x.
- Vera Liao, Q., D. Gruen, and S. Miller. 2020. “Questioning the AI: Informing Design Practices for Explainable AI User Experiences.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–15. doi:10.1145/3313831.3376590.
- Viljoen, S. 2021. “A Relational Theory of Data Governance.” The Yale Law Journal (Forthcoming), doi:10.2139/ssrn.3727562.
- Wachter, S. forthcoming. “The Theory of Artificial Immutability: Protecting Algorithmic Groups under Anti-Discrimination Law (February 15, 2022).” Tulane Law Review. Available at SSRN: https://ssrn.com/abstract = 4099100. doi:10.2139/ssrn.4099100.
- Weitz, K. 2022. “Towards Human-Centered AI: Psychological Concepts as Foundation for Empirical XAI Research.” it - Information Technology 64 (1-2): 71–75. doi:10.1515/itit-2021-0047.
- Weizenbaum, J. 1976. Computer Power and Human Reason: From Judgment to Calculation. W. H. Freeman and Company.
- White House. 2022. “Blueprint for an AI Bill of Rights – Making Automated Systems work for the American People.” https://www.whitehouse.gov/ostp/ai-bill-of-rights/.
- Whittlestone, J., R. Nyrup, A. Alexandrova, K. Dihal, and S. Cave. 2019. Ethical and Societal Implications of Algorithms, Data, and Artificial Intelligence: A Roadmap for Research. London: Nuffield Foundation. https://www.nuffieldfoundation.org/sites/default/files/files/Ethical-and-Societal-Implications-of-Data-and-AI-report-Nuffield-Foundat.pdf.
- Wohlin, C. 2014. “Guidelines for snowballing in systematic literature studies and a replication in software engineering.” In EASE ‘14: Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering (May 2014). Article 38, Pages 1–10. doi:10.1145/2601248.2601268.
- Xuesong, Z., X. Chu, C. S. Chai, M. S. Y. Jong, A. Istenic, M. Spector, J.-B. Liu, J. Yuan, and Y. Li. 2021. “A Review of Artificial Intelligence (AI) in Education from 2010 to 2020.” Complexity 2021: 18. doi:10.1155/2021/8812542.
- Zawacki-Richter, O., V. I. Marín, M. Bond, and F. Gouveneur. 2019. “Systematic Review of Research on Artificial Intelligence Applications in Higher Education – Where are the Educators?” International Journal of Educational Technology in Higher Education 16: 39. doi:10.1186/s41239-019-0171-0.
- Zhang, Y., and X. Chen. 2020. “Explainable Recommendation: A Survey and New Perspectives.” Foundations and Trends® in Information Retrieval 14 (1): 1–101. doi:10.1561/1500000066.
- Zuboff, S. 2019. The Age of Surveillance Capitalism. Public Affairs Books.