Ethical Guidelines and Principles in the Context of Artificial Intelligence
The interest in Artificial Intelligence (AI) based systems has been gaining momentum at a fast pace, both for software development teams and for society as a whole. This work aims to identify the guidelines and ethical principles for systems based on Artificial Intelligence. Design Science Research methodology was adopted in order to understand the various guidelines and principles existing in the literature. From the current landscape, a body of knowledge in the field of AI ethics is presented, with the purpose of supporting developers and Product Owners in identifying the guidelines and ethical principles in the literature so that they can be used during the software development process. Thus, this work will contribute to the various stakeholders in the development of ethical systems in the context of AI, such as: policy makers, ethicists, users, organizations, data scientists, development teams, among others.
A Aberkane. 2018. Exploring Ethics in Requirements Engineering. Master’s thesis. UTRECHT UNIVERSITY.
Julius Adebayo. 2016. FairML : ToolBox for diagnosing bias in predictive modeling. dissertation. Massachusetts Institute of Technology, https://dspace.mit.edu/handle/1721.1/108212.
Alejandro Barredo Arrieta, Natalia Díaz Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera. 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58(2020), 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John T. Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, and Yunfeng Zhang. 2018. AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias. CoRR abs/1810.01943(2018), 1–20. arxiv:1810.01943http://arxiv.org/abs/1810.01943
Richard Benjamins. 2020. Towards organizational guidelines for the responsible use of AI. CoRR abs/2001.09758(2020), 1–2. arxiv:2001.09758https://arxiv.org/abs/2001.09758
Eleanor Bird, Jasmin Fox-Skelly, Nicola Jenner, Ruth Larbey, Emma Weitkamp, and Alan Winfield. 2020. The ethics of artificial intelligence issues and initiatives : study Panel for the Future of Science and Technology. European Union, Brussels.
Brasil. 2018. Lei nº 13.709, de 14 de agosto de 2018. Lei Geral de Proteção de Dados Pessoais (LGPD). Diário Oficial da República Federativa do Brasil 1 (2018), 1–5. http://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/L13709.htm
Dylan Cawthorne and Aimee Robbins-van Wynsberghe. 2020. An ethical framework for the design, development, implementation, and assessment of drones used in public healthcare. Science and Engineering Ethics 26 (2020), 1–25.
Joymallya Chakraborty, Suvodeep Majumder, Zhe Yu, and Tim Menzies. 2020. Fairway: a way to build fair ML software. In ESEC/SIGSOFT FSE. ACM, 654–665. https://doi.org/10.1145/3368089.3409697
Joymallya Chakraborty, Kewen Peng, and Tim Menzies. 2020. Making Fair ML Software using Trustworthy Explanation. CoRR abs/2007.02893(2020), 1–5.
Conselho da União Europeia 2016. General Data Protection Regulation (GDPR) (UE) 2016/679. Conselho da União Europeia. https://data.consilium.europa.eu/doc/document/ST-5419-2016-INIT/en/pdf
Ángel Gómez de Ágreda. 2020. Ethics of autonomous weapons systems and its applicability to any AI systems. Telecommunications Policy 44 (2020), 101953.
Christian Detweiler and Maaike Harbers. 2014. Value Stories: Putting Human Values into Requirements Engineering. In REFSQ Workshops(CEUR Workshop Proceedings, Vol. 1138). CEUR-WS.org, http://ceur-ws.org/Vol-1138/creare.pdf, 2–11.
Virginia Dignum. 2019. Responsible Artificial Intelligence - How to Develop and Use AI in a Responsible Way. Springer. https://doi.org/10.1007/978-3-030-30371-6
European Commission 2019. Ethics Guidelines for Trustworthy AI High-Level Expert Group on artificial intelligence. European Commission. [link].
Jessica Fjeld, Nele Achten, Hannah Hilligoss, Adam Nagy, and Madhulika Srikumar. 2020. Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI. SSRN Electronic Journal 1, 1 (2020), 1–39. https://doi.org/10.2139/ssrn.3518482
Luciano Floridi and Josh Cowls. 2019. A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review 1, 1 (1 7 2019), 2–15. https://doi.org/10.1162/99608f92.8cd550d1 https://hdsr.mitpress.mit.edu/pub/l0jsh9d1.
Governo do Reino Unido 2019. Data Ethics Framework. Governo do Reino Unido. https://www.gov.uk/government/publications/data-ethics-framework
D. Greene, A. Hoffmann, and Luke Stark. 2019. Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning. In Proceedings of the 52nd Hawaii International Conference on System Sciences–HICSS. HICSS, 1–10. https://hdl.handle.net/10125/59651
Renata S. S. Guizzardi, Glenda Carla Moura Amaral, Giancarlo Guizzardi, and John Mylopoulos. 2020. Ethical Requirements for AI Systems. In Canadian Conference on AI(Lecture Notes in Computer Science, Vol. 12109). Springer, https://doi.org/10.1007/978-3-030-47358-7_24, 251–256.
Thilo Hagendorff. 2020. The Ethics of AI Ethics: An Evaluation of Guidelines. Minds Mach. 30(2020), 99–120.
Alexa Hagerty and Igor Rubinov. 2019. Global AI Ethics: A Review of the Social Impacts and Ethical Implications of Artificial Intelligence. CoRR abs/1907.07892, 1 (2019), 1–27.
Marek Havrda and Bogdana Rakova. 2020. Enhanced well-being assessment as basis for the practical implementation of ethical and rights-based normative principles for AI. CoRR abs/2007.14826(2020), .arxiv:2007.14826https://arxiv.org/abs/2007.14826
Anna Jobin, Marcello Ienca, and Effy Vayena. 2019. The global landscape of AI ethics guidelines. Nature Machine Intelligence 1, 9 (2019), 389–399.
Margot E. Kaminski and Gianclaudio Malgieri. 2020. Multi-layered explanations from algorithmic impact assessments in the GDPR. In FAT* ’20: Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27-30, 2020. ACM, ., 68–79. https://doi.org/10.1145/3351095.3372875
Tobias Krafft, Marc Hauer, Lajla Fetic, Andreas Kaminski, Michael Puntschuh, Philipp Otto, Christoph Hubig, Torsten Fleischer, Paul Grünke, Rafaela Hillerbrand, Carla Hustedt, and Sebastian Hallensleben. 2020. From Principles to Practice - An interdisciplinary framework to operationalise AI ethics.
David Leslie. 2019. Understanding artificial intelligence ethics and safety. CoRR abs/1906.05684(2019), .arxiv:1906.05684http://arxiv.org/abs/1906.05684
Michael A. Madaio, Luke Stark, Jennifer Wortman Vaughan, and Hanna M. Wallach. 2020. Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI. In CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020. ACM, ., 1–14. https://doi.org/10.1145/3313831.3376445
Andrew McNamara, Justin Smith, and Emerson R. Murphy-Hill. 2018. Does ACM’s code of ethics change ethical decision making in software development?. In Proceedings of the 2018 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/SIGSOFT FSE 2018, Lake Buena Vista, FL, USA, November 04-09, 2018. ACM, ., 729–733. https://doi.org/10.1145/3236024.3264833
Brent Mittelstadt. 2019. Principles alone cannot guarantee ethical AI. Nature Machine Intelligence 1, 11 (Nov. 2019), 501–507. https://doi.org/10.1038/s42256-019-0114-4
Jessica Morley, Luciano Floridi, Libby Kinsey, and Anat Elhalal. 2019. From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices. Science and Engineering Ethics 1, 1 (2019), 1–28.
Jessica Newman. 2020. Decision Points in AI Governance: Three case studies explore efforts to operationalize AI principles. Center for Long-Term Cybersecurity - UC Berkeley. [link].
Luca Oneto and Silvia Chiappa. 2020. Fairness in Machine Learning. In Recent Trends in Learning From Data. Springer, ., 155–196.
Dorian Peters, Karina Vold, Diana Robinson, and Rafael A Calvo. 2020. Responsible AI—Two Frameworks for Ethical Design Practice. IEEE Transactions on Technology and Society 1, 1 (2020), 34–47.
Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. 2020. Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. In FAT* ’20: Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27-30, 2020. ACM, ., 33–44. https://doi.org/10.1145/3351095.3372873
Lea Rothenberger, Benjamin Fabian, and Elmar Arunov. 2019. Relevance of Ethical Guidelines for Artificial Intelligence - a Survey and Evaluation. In 27th European Conference on Information Systems - Information Systems for a Sharing Society, ECIS 2019, Stockholm and Uppsala, Sweden, June 8-14, 2019. ECIS. https://aisel.aisnet.org/ecis2019_rip/26
Mark Ryan and Bernd Carsten Stahl. 2020. Artificial intelligence ethics guidelines for developers and users: clarifying their content and normative implications. Journal of Information, Communication and Ethics in Society 1, 1(2020), .
Pedro Saleiro, Benedict Kuester, Abby Stevens, Ari Anisfeld, Loren Hinkson, J. London, and R. Ghani. 2018. Aequitas: A Bias and Fairness Audit Toolkit. ArXiv abs/1811.05577(2018), .
D. Schiff, A. Ayesh, Laura Musikanski, and John C. Havens. 2020. IEEE 7010: A New Standard for Assessing the Well-being Implications of Artificial Intelligence. ArXiv abs/2005.06620, 1 (2020), .
Daniel Schiff, Bogdana Rakova, Aladdin Ayesh, Anat Fanti, and Michael Lennon. 2020. Principles to Practices for Responsible AI: Closing the Gap. CoRR abs/2006.04707(2020), .arxiv:2006.04707https://arxiv.org/abs/2006.04707
Shubham Sharma, Jette Henderson, and Joydeep Ghosh. 2019. CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models. CoRR abs/1905.07857(2019), .arxiv:1905.07857http://arxiv.org/abs/1905.07857
Keng Siau and Weiyu Wang. 2020. Artificial Intelligence (AI) Ethics: Ethics of AI and Ethical AI. Journal of Database Management (JDM) 31, 2 (2020), 74–87.
Koen Smit, Martijn Zoet, and John van Meerten. 2020. A Review of AI Principles in Practice. In 24th Pacific Asia Conference on Information Systems, PACIS 2020, Dubai, UAE, June 22-24, 2020. PACIS, ., 198. https://aisel.aisnet.org/pacis2020/198
Charlotte Stix. 2019. A survey of the European Union’s artificial intelligence ecosystem. Leverhulme Centre for the Future of Intelligence, University of Cambridge 1, 1 (2019), .
Steven Umbrello. 2019. Beneficial Artificial Intelligence Coordination by Means of a Value Sensitive Design Approach. Big Data Cogn. Comput. 3, 1 (2019), 5. https://doi.org/10.3390/bdcc3010005
Steven Umbrello and Angelo Frank De Bellis. 2018. A value-sensitive design approach to intelligent agents. Artificial Intelligence Safety and Security (2018) CRC Press (. ed) Roman Yampolskiy 1, 1 (2018), .
Vijay K Vaishnavi and William Kuechler. 2015. Design science research methods and patterns: innovating information and communication technology. Crc Press, .
Ville Vakkuri and Kai-Kristian Kemell. 2019. Implementing Artificial Intelligence Ethics: A Tutorial. In Software Business - 10th International Conference, ICSOB 2019, Jyväskylä, Finland, November 18-20, 2019, Proceedings(Lecture Notes in Business Information Processing, Vol. 370). Springer, ., 439–442. https://doi.org/10.1007/978-3-030-33742-1_38
Ville Vakkuri, Kai-Kristian Kemell, and Pekka Abrahamsson. 2020. ECCOLA - a Method for Implementing Ethically Aligned AI Systems. CoRR abs/2004.08377(2020), .arxiv:2004.08377https://arxiv.org/abs/2004.08377
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen, and Pekka Abrahamsson. 2020. The Current State of Industrial Practice in Artificial Intelligence Ethics. IEEE Softw. 37, 4 (2020), 50–57. https://doi.org/10.1109/MS.2020.2985621
Andreas Vogelsang and Markus Borg. 2019. Requirements Engineering for Machine Learning: Perspectives from Data Scientists. In 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW). IEEE, IEEE, ., 245–251.
Meredith Whittaker, Kate Crawford, Roel Dobbe, Genevieve Fried, Elizabeth Kaziunas, Varoon Mathur, Sarah Mysers West, Rashida Richardson, Jason Schultz, and Oscar Schwartz. 2018. AI now report 2018. AI Now Institute at New York University New York, .
Jess Whittlestone, Rune Nyrup, Anna Alexandrova, and Stephen Cave. 2019. The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, AIES 2019, Honolulu, HI, USA, January 27-28, 2019. ACM, ., 195–200. https://doi.org/10.1145/3306618.3314289
Yi Zeng, Enmeng Lu, and Cunqing Huangfu. 2019. Linking Artificial Intelligence Principles. In Workshop on Artificial Intelligence Safety 2019 co-located with the Thirty-Third AAAI Conference on Artificial Intelligence 2019 (AAAI-19), Honolulu, Hawaii, January 27, 2019 (CEUR Workshop Proceedings, Vol. 2301). CEUR-WS.org. http://ceur-ws.org/Vol-2301/paper_15.pdf