Governance in Ethical and Trustworthy AI Systems: Extension of the ECCOLA Method for AI Ethics Governance Using GARP

• Autorzy: Agbese Mamia , Alanen Hanna-Kaisa , Antikainen Jani , Erika Halme , Isomaki Hannakaisa , Jantunen Marianna , Kemell Kai-Kristian , Rousi Rebekah , Vainio-Pekka Heidi , Vakkuri Ville

Identyfikatory

DOI

10.37190/e-Inf230101

• Języki publikacji: EN

Abstrakty

EN

Background: The continuous development of artificial intelligence (AI) and increasing rate of adoption by software startups calls for governance measures to be implemented at the design and development stages to help mitigate AI governance concerns. Most AI ethical design and development tools mainly rely on AI ethics principles as the primary governance and regulatory instrument for developing ethical AI that inform AI governance. However, AI ethics principles have been identified as insufficient for AI governance due to lack of information robustness, requiring the need for additional governance measures. Adaptive governance has been proposed to combine established governance practices with AI ethics principles for improved information and subsequent AI governance. Our study explores adaptive governance as a means to improve information robustness of AI ethical design and development tools. We combine information governance practices with AI ethics principles using ECCOLA, a tool for ethical AI software development at the early developmental stages. Aim: How can ECCOLA improve its robustness by adapting it with GARP® IG practices? Methods: We use ECCOLA as a case study and critically analyze its AI ethics principles with information governance practices of the Generally Accepted Recordkeeping principles (GARP®). Results: We found that ECCOLA’s robustness can be improved by adapting it with Information governance practices of retention and disposal. Conclusions: We propose an extension of ECCOLA by a new governance theme and card, # 21.

Słowa kluczowe

EN

AI; artificial intelligence; AI ethics; trustworthy AI; AI governance; adaptive governance; ECCOLA

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: e-Informatica Software Engineering Journal
• Rocznik: 2023
• Tom: Vol. 17, nr 1
• Strony: art. no. 230101
• Opis fizyczny: Bibliogr. 83 poz., rys., tab.

Twórcy

autor

Agbese Mamia

• Faculty of Information Technology, The University of Jvaskyla, Finland

autor

Alanen Hanna-Kaisa

• Faculty of Information Technology, The University of Jvaskyla, Finland

autor

Antikainen Jani

• Faculty of Information Technology, The University of Jvaskyla, Finland

autor

Erika Halme

• Faculty of Information Technology, The University of Jvaskyla, Finland

autor

Isomaki Hannakaisa

• Faculty of Information Technology, The University of Jvaskyla, Finland

autor

Jantunen Marianna

• Faculty of Information Technology, The University of Jvaskyla, Finland

autor

Kemell Kai-Kristian

• Faculty of Information Technology, The University of Jvaskyla, Finland

autor

Rousi Rebekah

• Faculty of Information Technology, The University of Jvaskyla, Finland

autor

Vainio-Pekka Heidi

• Faculty of Information Technology, The University of Jvaskyla, Finland

autor

Vakkuri Ville

• Faculty of Information Technology, The University of Jvaskyla, Finland

Bibliografia

• 1. A. Taeihagh, “Governance of artificial intelligence,” Policy and Society , 2021, pp. 1–21.
• 2. M. Schulte-Althoff, D. Fürstenau, and G.M. Lee, “A scaling perspective on AI startups,” in Proceedings of the 54th Hawaii International Conference on System Sciences , 2021, p. 6515.
• 3. C. Giardino, S.S. Bajwa, X. Wang, and P. Abrahamsson, “Key challenges in early-stage software startups,” in International conference on agile software development . Springer, 2015, pp. 52–63.
• 4. C. Newton, J. Singleton, C. Copland, S. Kitchen, and J. Hudack, “Scalability in modeling and simulation systems for multi-agent, AI, and machine learning applications,” in Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III , Vol. 11746. International Society for Optics and Photonics, 2021, p. 1174626.
• 5. S. Reddy, S. Allan, S. Coghlan, and P. Cooper, “A governance model for the application of ai in health care,” Journal of the American Medical Informatics Association: JAMIA , Vol. 27, No. 3, 2020, pp. 491–497.
• 6. P. Liu, M. Du, and T. Li, “Psychological consequences of legal responsibility misattribution associated with automated vehicles,” Ethics and information technology , Vol. 23, No. 4, 2021, pp. 763–776.
• 7. G. Falco, B. Shneiderman, J. Badger, R. Carrier, A. Dahbura et al., “Governing ai safety through independent audits,” Nature Machine Intelligence , Vol. 3, No. 7, 2021, pp. 566–571.
• 8. S. Du and C. Xie, “Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities,” Journal of Business Research , Vol. 129, 2021, pp. 961–974.
• 9. “Ethics guidelines for trustworthy AI,” European Commission, High-Level Expert Group on AI, Tech. Rep., 2019. [Online]. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
• 10. V. Vakkuri, K.K. Kemell, M. Jantunen, E. Halme, and P. Abrahamsson, “Eccola—a method for implementing ethically aligned ai systems,” Journal of Systems and Software , Vol. 182, 2021, p. 111067.
• 11. D. Lewis, W. Reijers, H. Pandit, and W. Reijers, Ethics canvas manual , ADAPT Centre and Trinity College Dublin and Dublin City University, 2017. [Online]. https://www.ethicscanvas.org/download/handbook.pdf
• 12. R. Eitel-Porter, “Beyond the promise: implementing ethical AI,” AI and Ethics , Vol. 1, No. 1, 2021, pp. 73–80.
• 13. R. Hamon, H. Junklewitz, and I. Sanchez, “Robustness and explainability of artificial intelligence,” Publications Office of the European Union , 2020.
• 14. I. Linkov, B.D. Trump, K. Poinsatte-Jones, and M.V. Florin, “Governance strategies for a sustainable digital world,” Sustainability , Vol. 10, No. 2, 2018, p. 440.
• 15. U. Pagallo, P. Aurucci, P. Casanovas, R. Chatila, P. Chazerand et al., “On good AI governance: 14 priority actions, a smart model of governance, and a regulatory toolbox,” 2019.
• 16. S.Y. Tan and A. Taeihagh, “Adaptive governance of autonomous vehicles: Accelerating the adoption of disruptive technologies in Singapore,” Government Information Quarterly , Vol. 38, No. 2, 2021, p. 101546.
• 17. S. Jain, M. Luthra, S. Sharma, and M. Fatima, “Trustworthiness of artificial intelligence,” in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) . IEEE, 2020, pp. 907–912.
• 18. I. Brass and J.H. Sowell, “Adaptive governance for the internet of things: Coping with emerging security risks,” Regulation & Governance , Vol. 15, No. 4, 2021, pp. 1092–1110.
• 19. A.B. Whitford and D. Anderson, “Governance landscapes for emerging technologies: The case of cryptocurrencies,” Regulation & Governance , 2021.
• 20. M. Agbese, H.K. Alanen, J. Antikainen, E. Halme, H. Isomäki et al., “Governance of ethical and trustworthy al systems: Research gaps in the eccola method,” in 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) . IEEE, 2021, pp. 224–229.
• 21. C. Collins, D. Dennehy, K. Conboy, and P. Mikalef, “Artificial intelligence in information systems research: A systematic literature review and research agenda,” International Journal of Information Management , Vol. 60, 2021, p. 102383.
• 22. A.B. Simmons and S.G. Chappell, “Artificial intelligence-definition and practice,” IEEE journal of oceanic engineering , Vol. 13, No. 2, 1988, pp. 14–42.
• 23. S. Leijnen, H. Aldewereld, R. van Belkom, R. Bijvank, and R. Ossewaarde, “An agile framework for trustworthy ai.” in NeHuAI@ ECAI , 2020, pp. 75–78.
• 24. A. HLEG, A definition of AI: main capabilities and disciplines , 2019. [Online]. https://ec.europa.eu/digital-single
• 25. C.F. Tan, L. Wahidin, S. Khalil, N. Tamaldin, J. Hu et al., “The application of expert system: A review of research and applications,” ARPN Journal of Engineering and Applied Sciences , Vol. 11, No. 4, 2016, pp. 2448–2453.
• 26. L. Ma and B. Sun, “Machine learning and AI in marketing–connecting computing power to human insights,” International Journal of Research in Marketing , Vol. 37, No. 3, 2020, pp. 481–504.
• 27. E.J. Rykiel Jr, “Artificial intelligence and expert systems in ecology and natural resource management,” Ecological Modelling , Vol. 46, No. 1-2, 1989, pp. 3–8.
• 28. P. Hu, Y. Lu et al., “Dual humanness and trust in conversational AI: A person-centered approach,” Computers in Human Behavior , Vol. 119, 2021, p. 106727.
• 29. Y. Bengio, Learning deep architectures for AI . Now Publishers Inc, 2009.
• 30. R.R. Kumar, M.B. Reddy, and P. Praveen, “Text classification performance analysis on machine learning,” International Journal of Advanced Science and Technology , Vol. 28, No. 20, 2019, pp. 691–697.
• 31. K. Oosthuizen, E. Botha, J. Robertson, and M. Montecchi, “Artificial intelligence in retail: The ai-enabled value chain,” Australasian Marketing Journal , No 3, 2020
• 32. B.W. Wirtz, J.C. Weyerer, and B.J. Sturm, “The dark sides of artificial intelligence: An integrated ai governance framework for public administration,” International Journal of Public Administration , Vol. 43, No. 9, 2020, pp. 818–829.
• 33. K. Siau and W. Wang, “Artificial intelligence (ai) ethics: ethics of ai and ethical ai,” Journal of Database Management (JDM) , Vol. 31, No. 2, 2020, pp. 74–87.
• 34. K. Michael, R. Abbas, P. Jayashree, R.J. Bandara, and A. Aloudat, “Biometrics and ai bias,” IEEE Transactions on Technology and Society , Vol. 3, No. 1, 2022, pp. 2–8.
• 35. P. Mikalef, K. Conboy, J.E. Lundström, and A. Popovič, “Thinking responsibly about responsible ai and ‘the dark side’of ai,” European Journal of Information Systems , Vol. 31, No. 3, 2022, pp. 257–268.
• 36. C. Trocin, P. Mikalef, Z. Papamitsiou, and K. Conboy, “Responsible ai for digital health: a synthesis and a research agenda,” Information Systems Frontiers , 2021, pp. 1–19.
• 37. M. Kiener, “Artificial intelligence in medicine and the disclosure of risks,” Ai & Society , Vol. 36, No. 3, 2021, pp. 705–713.
• 38. V.C. Muller, “Ethics of artificial intelligence and robotics,” in Stanford Encyclopedia of Philosophy , 2020.
• 39. A. Jobin, M. Ienca, and E. Vayena, “The global landscape of AI ethics guidelines,” Nature Machine Intelligence , Vol. 1, No. 9, Sep. 2019, pp. 389–399. [Online]. http://www.nature.com/articles/s42256-019-0088-2
• 40. J. Morley, L. Floridi, L. Kinsey, and A. Elhalal, “From what to how: an initial review of publicly available ai ethics tools, methods and research to translate principles into practices,” in Ethics, Governance, and Policies in Artificial Intelligence . Springer, 2021, pp. 153–183.
• 41. M. Hickok, “Lessons learned from ai ethics principles for future actions,” AI and Ethics , Vol. 1, No. 1, 2021, pp. 41–47.
• 42. P. Sondergaard, AI governance – what are the kpis? And who is accountable? , 2021.AI, (2019, Nov). [Online]. https://2021.ai/ai-governance-kpi Accessed: 16- Jun- 2021.
• 43. R.I. Rotberg, “Good governance means performance and results,” Governance , Vol. 27, No. 3, 2014, pp. 511–518.
• 44. M. Ashok, R. Madan, A. Joha, and U. Sivarajah, “Ethical framework for artificial intelligence and digital technologies,” International Journal of Information Management , Vol. 62, 2022, p. 102433.
• 45. U. Gasser and V.A. Almeida, “A layered model for AI governance,” IEEE Internet Computing , Vol. 21, No. 6, 2017, pp. 58–62.
• 46. A.F. Winfield, K. Michael, J. Pitt, and V. Evers, “Machine ethics: The design and governance of ethical ai and autonomous systems [scanning the issue],” Proceedings of the IEEE , Vol. 107, No. 3, 2019, pp. 509–517.
• 47. J. Butcher and I. Beridze, “What is the state of artificial intelligence governance globally?” The RUSI Journal , Vol. 164, No. 5-6, 2019, pp. 88–96.
• 48. H. Yu, Z. Shen, C. Miao, C. Leung, V.R. Lesser et al., “Building ethics into artificial intelligence,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence , IJCAI’18. AAAI Press, 2018, p. 5527–5533.
• 49. A. Daly, T. Hagendorff, H. Li, M. Mann, V. Marda et al., “Ai, governance and ethics: global perspectives,” Available at SSRN , 2020.
• 50. B. Perry and R. Uuk, “Ai governance and the policymaking process: key considerations for reducing ai risk,” Big data and cognitive computing , Vol. 3, No. 2, 2019, p. 26.
• 51. W. Wu, T. Huang, and K. Gong, “Ethical principles and governance technology development of AI in China,” Engineering , Vol. 6, No. 3, 2020, pp. 302–309.
• 52. P. Cihon, “Standards for ai governance: international standards to enable global coordination in AI research & development,” Future of Humanity Institute. University of Oxford , 2019.
• 53. B. Mittelstadt, “Principles alone cannot guarantee ethical ai,” Nature Machine Intelligence , Vol. 1, No. 11, 2019, pp. 501–507.
• 54. R. Leenes and F. Lucivero, “Laws on robots, laws by robots, laws in robots: regulating robot behaviour by design,” Law, Innovation and Technology , Vol. 6, No. 2, 2014, pp. 193–220.
• 55. M. Firlej and A. Taeihagh, “Regulating human control over autonomous systems,” Regulation & Governance , Vol. 15, No. 4, 2021, pp. 1071–1091.
• 56. K. Yeung, A. Howes, and G. Pogrebna, “Ai governance by human rights–centered design, deliberation, and oversight,” in The Oxford Handbook of Ethics of AI . Oxford University Press, 2020, p. 77.
• 57. X. Cao, D. Lv, L. Zhang, and Z. Xing, “Adaptive governance, loose coupling, forward-looking strategies and responsible innovation,” IEEE Access , Vol. 8, 2020, pp. 228163–228177.
• 58. R. Radu, “Steering the governance of artificial intelligence: national strategies in perspective,” Policy and society , 2021, pp. 1–16.
• 59. J. Ayling and A. Chapman, “Putting AI ethics to work: are the tools fit for purpose?” AI and Ethics , 2021, pp. 1–25.
• 60. O.E. Williamson, “The economics of governance: framework and implications,” Zeitschrift für die gesamte Staatswissenschaft/Journal of Institutional and Theoretical Economics , 1984, pp. 195–223.
• 61. H. Borgman, H. Heier, B. Bahli, and T. Boekamp, “Dotting the i and crossing (out) the t in it governance: New challenges for information governance,” in 2016 49th Hawaii International Conference on System Sciences (HICSS) . IEEE, 2016, pp. 4901–4909.
• 62. H. Dogiparthi, “History of information governance,” University of the Cumberlands, Dept. Of Information Technology, Research Paper, 02 2019.
• 63. E.M. Coyne, J.G. Coyne, and K.B. Walker, “Big data information governance by accountants,” International Journal of Accounting & Information Management , 2018.
• 64. J. Hagmann, “Information governance–beyond the buzz,” Records Management Journal , 2013.
• 65. Generally Accepted Recordkeeping Principles® . , ARMA International„ 2017
• 66. D. Hofman, V.L. Lemieux, A. Joo, and D.A. Batista, ““the margin between the edge of the world and infinite possibility”: Blockchain, gdpr and information governance,” Records Management Journal , 2019.
• 67. E. Lomas, “Information governance: information security and access within a uk context,” Records Management Journal , 2010.
• 68. R. Yin, “Case study research: Design and methods, 2nd edn, beverly hills,” CA: Sage Publishing. Zhang, GP (2003)‘Time series forecasting using a hybrid ARIMA and neural network model’, Neurocomputing. Elsevier , Vol. 50, 1994, pp. 159–175.
• 69. A.R. Hevner, S.T. March, J. Park, and S. Ram, “Design science in information systems research,” MIS quarterly , 2004, pp. 75–105.
• 70. J.W. Creswell, W.E. Hanson, V.L. Clark Plano, and A. Morales, “Qualitative research designs: Selection and implementation,” The counseling psychologist , Vol. 35, No. 2, 2007, pp. 236–264.
• 71. K. Peffers, T. Tuunanen, M.A. Rothenberger, and S. Chatterjee, “A design science research methodology for information systems research,” Journal of management information systems , Vol. 24, No. 3, 2007, pp. 45–77.
• 72. R.E. Stake, The art of case study research . sage, 1995.
• 73. S. Bennett, “What is information governance and how does it differ from data governance?” Governance Directions , Vol. 69, No. 8, 2017, pp. 462–467.
• 74. J.W. Drisko and T. Maschi, Content analysis . Pocket Guide to Social Work Re, 2016.
• 75. R.P. Weber, Basic content analysis , Quantitative Applications in the Social Sciences ; No. 07-049. Sage, 1990, No. 49.
• 76. S. Elo, M. Kääriäinen, O. Kanste, T. Pölkki, K. Utriainen et al., “Qualitative content analysis: A focus on trustworthiness,” SAGE open , Vol. 4, No. 1, 2014, p. 2158244014522633.
• 77. M.S. Caron, “The transformative effect of AI on the banking industry,” Banking & Finance Law Review , Vol. 34, No. 2, 2019, pp. 169–214.
• 78. M. Janssen, P. Brous, E. Estevez, L.S. Barbosa, and T. Janowski, “Data governance: Organizing data for trustworthy artificial intelligence,” Government Information Quarterly , Vol. 37, No. 3, 2020, p. 101493.
• 79. M.J. Culnan, “Policy to avoid a privacy disaster,” Journal of the Association for Information Systems , Vol. 20, No. 6, 2019, p. 1.
• 80. J. In, R. Bradley, B.C. Bichescu, and C.W. Autry, “Supply chain information governance: Toward a conceptual framework,” The International Journal of Logistics Management , 2018.
• 81. M. Hind, S. Houde, J. Martino, A. Mojsilovic, D. Piorkowski et al., “Experiences with improving the transparency of AI models and services,” in Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems , 2020, pp. 1–8.
• 82. J.A. Kroll, “Data science data governance [AI ethics],” IEEE Security & Privacy , Vol. 16, No. 6, 2018, pp. 61–70.
• 83. M. Mitchell, S. Wu, A. Zaldivar, P. Barnes, L. Vasserman et al., “Model cards for model reporting,” in Proceedings of the Conference on Fairness, Accountability, and Transparency , FAT* ’19. New York, NY, USA: Association

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).