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AI ethics: from principles to practice

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Abstract

Much of the current work on AI ethics has lost its connection to the real-world impact by making AI ethics operable. There exist significant limitations of hyper-focusing on the identification of abstract ethical principles, lacking effective collaboration among stakeholders, and lacking the communication of ethical principles to real-world applications. This position paper presents challenges in making AI ethics operable and highlights key obstacles to AI ethics impact. A preliminary practice example is provided to initiate practical implementations of AI ethics. We aim to inspire discussions on making AI ethics operable and focus on its impact on real-world applications.

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  1. https://legistar.council.nyc.gov/LegislationDetail.aspx?ID=4344524&GUID=B051915D-A9AC-451E-81F8-6596032FA3F9 .

References

  • AlgorithmWatch (2020) AI ethics guidelines global inventory. https://algorithmwatch.org/en/project/ai-ethics-guidelines-global-inventory/. Accessed 18 Oct 2020

  • Alvarez-Melis D, Jaakkola TS (2018) Towards robust interpretability with self-explaining neural networks. arXiv:1806.07538 [cs, stat]

  • Amrollahi M, Hadayeghparast S, Karimipour H et al (2020) Enhancing network security via machine learning: opportunities and challenges. In: Choo K-KR, Dehghantanha A (eds) Handbook of big data privacy. Springer International Publishing, Cham, pp 165–189

    Chapter  Google Scholar 

  • Anastasopoulos LJ, Whitford AB (2019) Machine learning for public administration research, with application to organizational reputation. J Public Adm Res Theory 29:491–510. https://doi.org/10.1093/jopart/muy060

    Article  Google Scholar 

  • Awoyemi JO, Adetunmbi AO, Oluwadare SA (2017) Credit card fraud detection using machine learning techniques: a comparative analysis. In: 2017 international conference on computing networking and informatics (ICCNI), pp 1–9

  • Batmaz Z, Yurekli A, Bilge A, Kaleli C (2019) A review on deep learning for recommender systems: challenges and remedies. Artif Intell Rev 52:1–37. https://doi.org/10.1007/s10462-018-9654-y

    Article  Google Scholar 

  • Bird E, Fox-Skelly J, Jenner N, et al (2020) The ethics of artificial intelligence: issues and initiatives. European Parliamentary Research Service

  • Blackman R (2022) Why you need an AI ethics committee. Harvard Bus Rev. July-August 2022

  • Bojarski M, Del Testa D, Dworakowski D et al (2016) End to end learning for self-driving cars. arXiv preprint. arXiv:1604.07316

  • Bossmann J (2016) Top 9 ethical issues in artificial intelligence. In: World economic forum. https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/. Accessed 9 Sept 2019

  • Capgemini (2019) Why addressing ethical questions in AI will benefit organizations. In: Capgemini worldwide. https://www.capgemini.com/research/why-addressing-ethical-questions-in-ai-will-benefit-organizations. Accessed 10 Oct 2021

  • Corinium (2019) Ethics of AI. https://cdn2.hubspot.net/hubfs/2631050/CDAO%20New%20Zealand/Corinium_Ethics-of-AI_brochure_NZ.pdf. Accessed 8 Aug 2019

  • Dawson D, Schleiger E, Horton J, et al (2019) Artificial intelligence—Australia’s ethics framework. In: Data61, CSIRO, Australia

  • Deane M (2018) AI and the future of privacy. In: Towards data science. https://towardsdatascience.com/ai-and-the-future-of-privacy-3d5f6552a7c4. Accessed 16 May 2019

  • Dewey J, Tufts JH (2019) Ethics. Good Press, Glasgow

    Google Scholar 

  • Floridi L, Cowls J (2019) A unified framework of five principles for AI in society. Harvard Data Sci Rev. https://doi.org/10.1162/99608f92.8cd550d1

    Article  Google Scholar 

  • Foreman E (2014) An agent-centered account of rightness: the importance of a good attitude. Ethical Theory Moral Pract 17:941–954

    Article  Google Scholar 

  • Gao X, Tian Y, Qi Z (2020) RPD-GAN: learning to draw realistic paintings with generative adversarial network. IEEE Trans Image Process 29:8706–8720. https://doi.org/10.1109/TIP.2020.3018856

    Article  Google Scholar 

  • Gupta A, Lanteigne C, Heath V et al (2020) The state of AI ethics report (June 2020). arXiv:2006.14662 [cs]

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  • Hursthouse R, Pettigrove G (2018) Virtue ethics. In: Zalta EN (ed) The stanford encyclopedia of philosophy, winter 2018 Edition, Available online: https://plato.stanford.edu/archives/win2018/entries/ethics-virtue/ (This is an online encyclopedia published by Stanford. There is no publisher name and location specificlly. If we need to provide, it could be "Center for the Study of Language and Information, Stanford Univeristy", (this is from the webpage))

  • IEEE (2019) The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. In: IEEE Standards Association. https://standards.ieee.org/industry-connections/ec/autonomous-systems.html

  • IEEE (2018) IEEE launches ethics certification program for autonomous and intelligent systems. In: IEEE Standards Association. https://standards.ieee.org/news/2018/ieee-launches-ecpais.html. Accessed 18 Sept 2019

  • Jobin A, Ienca M, Vayena E (2019) The global landscape of AI ethics guidelines. Nat Mach Intell 1:389–399

    Article  Google Scholar 

  • Korinek A (2020) Integrating ethical values and economic value to steer progress in artificial intelligence. In: Dubber MD, Pasquale F, Das S (eds) The Oxford handbook of ethics of AI. Oxford University Press, Oxford

    Google Scholar 

  • Lo Piano S (2020) Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward. Humanit Soc Sci Commun 7:1–7

    Article  Google Scholar 

  • Monroe D (2017) Deep learning takes on translation. Commun ACM 60:12–14. https://doi.org/10.1145/3077229

    Article  Google Scholar 

  • Mooney SJ, Pejaver V (2018) Big data in public health: terminology, machine learning, and privacy. Annu Rev Public Health 39:95–112. https://doi.org/10.1146/annurev-publhealth-040617-014208

    Article  Google Scholar 

  • Morley J, Floridi L, Kinsey L, Elhalal A (2019) From what to how. An overview of AI ethics tools, methods and research to translate principles into practices. arXiv:1905.06876 [cs]

  • Morley J, Kinsey L, Elhalal A et al (2021) Operationalising AI ethics: barriers, enablers and next steps. AI Soc. https://doi.org/10.1007/s00146-021-01308-8

    Article  Google Scholar 

  • Nguyen TT, Nguyen CM, Nguyen DT et al (2020) Deep learning for deepfakes creation and detection: a survey. arXiv:1909.11573 [cs, eess]

  • NSW Government (2020) Mandatory ethical principles for the use of AI. In: Artificial intelligence (AI) ethics policy. https://www.digital.nsw.gov.au/policy/artificial-intelligence-ai/artificial-intelligence-ai-ethics-policy/mandatory-ethical. Accessed 20 Oct 2020

  • Rolnick D, Donti PL, Kaack LH et al (2019) Tackling climate change with machine learning. arXiv:1906.05433 [cs, stat]

  • Rovatsos M (2019) From AI ethics to ethical AI. IJCAI 2019 tutorial, Macau, China

  • Samuel AL (1960) Some moral and technical consequences of automation—a refutation. Science 132:741–742. https://doi.org/10.1126/science.132.3429.741

    Article  Google Scholar 

  • Shen H, Deng WH, Chattopadhyay A, et al (2021) Value cards: an educational toolkit for teaching social impacts of machine learning through deliberation. In: Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. Association for Computing Machinery, New York, NY, USA, pp 850–861

  • Taddeo M, Floridi L (2018) How AI can be a force for good. Science 361:751–752. https://doi.org/10.1126/science.aat5991

    Article  MathSciNet  Google Scholar 

  • The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (2019) Ethically aligned design: a vision for prioritizing human well-being with autonomous and intelligent systems. IEEE

  • Tolosana R, Vera-Rodriguez R, Fierrez J et al (2020) DeepFakes and beyond: a survey of face manipulation and fake detection. arXiv:2001.00179 [cs]

  • UKRI (2010) Principles of robotics. In: Engineering and physical sciences research council. https://epsrc.ukri.org/research/ourportfolio/themes/engineering/activities/principlesofrobotics/. Accessed 4 Nov 2020

  • Wiener N (1960) Some moral and technical consequences of automation. Science 131:1355–1358. https://doi.org/10.1126/science.131.3410.1355

    Article  Google Scholar 

  • Zhao S, Gao Y, Jiang X et al (2014) Exploring principles-of-art features for image emotion recognition. In: Proceedings of the 22nd ACM international conference on multimedia. Association for Computing Machinery, New York, NY, USA, pp 47–56

  • Zhou J, Chen F (2019) AI in the public interest. In: Bertram C, Gibson A, Nugent A (eds) Closer to the machine: technical, social, and legal aspects of AI. Office of the Victorian Information Commissioner, Melbourne, Australia

  • Zhou J, Chen F (eds) (2018) Human and machine learning: visible, explainable, trustworthy and transparent. Springer, Berlin

    Google Scholar 

  • Zhou J, Chen F, Berry A, et al (2020) A survey on ethical principles of AI and implementations. In: Proceedings of 2020 IEEE symposium series on computational intelligence (IEEE SSCI), Canberra, Australia

  • Zhou J, Chen F, Berry A (2021) AI ethics: from principles to practice. https://open.uts.edu.au/uts-open/study-area/Technology/ethical-ai-from-principles-to-practice/. Accessed 15 Aug 2021

  • Zhou J, Li Z, Zhi W et al (2017) Using convolutional neural networks and transfer learning for bone age classification. In: 2017 international conference on digital image computing: techniques and applications, DICTA 2017, Sydney, Australia, November 29–December 1, 2017. IEEE

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  1. Data Science Institute, University of Technology Sydney, Sydney, Australia

    Jianlong Zhou & Fang Chen

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  1. Jianlong Zhou
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Zhou, J., Chen, F. AI ethics: from principles to practice. AI & Soc 38, 2693–2703 (2023). https://doi.org/10.1007/s00146-022-01602-z

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