Categorization and eccentricity of AI risks: a comparative study of the global AI guidelines

Kai Jia1, Nan Zhang2
1School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China
2School of Public Policy and Management, Tsinghua University, Beijing, China

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Abubakar, A. M., Behravesh, E., Rezapouraghdam, H., & Yildiz, S. B. (2019). Applying artificial intelligence technique to predict knowledge hiding behavior. International Journal of Information Management, 49, 45–57. https://doi.org/10.1016/j.ijinfomgt.2019.02.006

Acemoglu, D., & Restrepo, P. (2020). The wrong kind of AI? Artificial intelligence and the future of labour demand. Cambridge Journal of Regions, Economy and Society, 13(1), 25–35. https://doi.org/10.1093/cjres/rsz022

Andreessen, M. (2011). Why software is eating the world. Wall Street Journal, 20(2011), C2.

Anthony (Tony) Cox Jr, L. (2008). What’s wrong with risk matrices? Risk Analysis: an International Journal, 28(2), 497–512. https://doi.org/10.1111/j.1539-6924.2008.01030.x

Appenzeller, T. (2017). The AI revolution in science. Science. https://www.sciencemag.org/news/2017/07/ai-revolution-science

Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, pp. 19–32. https://doi.org/10.1080/1364557032000119616

Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., & Rahwan, I. (2018). The moral machine experiment. Nature, 563(7729), 59–64. https://doi.org/10.1038/s41586-018-0637-6

Awad, E., Anderson, M., Anderson, S. L., & Liao, B. (2020). An approach for combining ethical principles with public opinion to guide public policy. Artificial Intelligence, 287, 103349. https://doi.org/10.1016/j.artint.2020.103349

Balkin, J. M. (2018). Free Speech is a Triangle. Columbia Law Review, 118(7), 2011–2056.

Bandara, R., Fernando, M., & Akter, S. (2020). Privacy concerns in E-commerce: A taxonomy and a future research agenda. Electronic Markets, 30(3) 629–647. https://doi.org/10.1007/s12525-019-00375-6

Benkler, Y. (2019). Don’t let industry write the rules for AI. Nature, 569, 161.

Biswas, B., & Mukhopadhyay, A. (2018). G-RAM framework for software risk assessment and mitigation strategies in organizations. Journal of Enterprise Information Management, 31(2), 276–299. https://doi.org/10.1108/JEIM-05-2017-0069

Boddington, P. (2018). Alphabetical list of resources. Ethics for Artificial Intelligence. https://www.cs.ox.ac.uk/efai/resources/alphabetical-list-of-resources/

Calo, R. (2017). Artificial Intelligence policy: a primer and roadmap. UCDL Review, 51, 399.

Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial intelligence and the ‘good society’: the US, EU, and UK approach. Science and Engineering Ethics, 24(2), 505–528. https://doi.org/10.1007/s11948-017-9901-7

Chinese National Governance Committee for the New Generation Artificial Intelligence. (2019). Governance Principles for the New Generation Artificial Intelligence–Developing Responsible Artificial Intelligence. China Daily. https://www.chinadaily.com.cn/a/201906/17/WS5d07486ba3103dbf14328ab7.html

Cox, L. A., Jr., Babayev, D., & Huber, W. (2005). Some limitations of qualitative risk rating systems. Risk Analysis: an International Journal, 25(3), 651–662. https://doi.org/10.1111/j.1539-6924.2005.00615.x

Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds and Machines 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5

Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020). How to design AI for social good: seven essential factors. Science and Engineering Ethics, 26(3), 1771–1796. https://doi.org/10.1007/s11948-020-00213-5

Future of Life Institute. (2017). Asilomar AI Principles. https://futureoflife.org/ai-principles/

Goldacre, B. (2014). When data gets creepy: the secrets we don’t realize we’re giving away. The Guardian. https://www.theguardian.com/technology/2014/dec/05/when-data-gets-creepy-secrets-were-giving-away

Greene, D., Hoffman, A. L., & Stark, L. (2019). Better, nicer, clearer, fairer: a critical assessment of the movement for ethical artificial intelligence and machine learning. Hawaii International Conference on System Sciences (HICSS), 1–10. https://doi.org/10.24251/HICSS.2019.258

Grimmelmann, J. (2004). Regulation by Software. Yale LJ, 114, 1719.

Hagendorff, T. (2020). The ethics of AI ethics: an evaluation of guidelines. Minds and Machines, 1–22. https://doi.org/10.1007/s11023-020-09517-8

Harari, Y. N. (2017). Reboot for the AI revolution. Nature, 550, 324–327. https://doi.org/10.1038/550324a

Heckmann, I., Comes, T., & Nickel, S. (2015). A critical review on supply chain risk—definition, measure and modeling, Omega, 52, 119–132. https://doi.org/10.1016/j.omega.2014.10.004

Hong, J. I., & Landay, J. A. (2004). An architecture for privacy-sensitive ubiquitous computing. Proceedings of the 2nd International Conference on Mobile Systems, Applications, and Services, 177–189. https://doi.org/10.1145/990064.990087

ISO. (2002). Risk Management: Guidelines for use in standards. ISO/IEC Guide 73.

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2

Krafft, T. D., Zweig, K. A., & König, P. D. (2020). How to regulate algorithmic decision‐making: a framework of regulatory requirements for different applications. Regulation & Governance. https://doi.org/10.1111/rego.12369

Lessig, L. (2009). Code: And other laws of cyberspace.Version 2.0. New York: Basic Books.

Liu, H. W., Lin, C. F., & Chen, Y. J. (2019). Beyond State v Loomis: artificial intelligence, government algorithmization and accountability. International Journal of Law and Information Technology, 27(2), 122–141. https://doi.org/10.1093/ijlit/eaz001

Markowski, A. S., & Mannan, M. S. (2008). Fuzzy risk matrix. Journal of Hazardous Materials, 159(1), 152–157. https://doi.org/10.1016/j.jhazmat.2008.03.055

McNamara, A., Smith, J., & Murphy-Hill, E. (2018). Does ACM’s code of ethics change ethical decision making in software development? In G. T. Leavens, A. Garcia, C. S. Păsăreanu (Eds.) Proceedings of the 26th ACM joint meeting on european software engineering conference and sym- posium on the foundations of software engineering—ESEC/FSE 2018, 1–7. New York: ACM Press. https://doi.org/10.1145/3236024.3264833

Meek, T., Barham, H., Beltaif, N., Kaadoor, A., & Akhter, T. (2016). Managing the ethical and risk implications of rapid advances in Artificial Intelligence. International Conference on Management of Engineering and Technology (PICMET), Portland, 682–693, 108. https://doi.org/10.1109/PICMET.2016.7806752

Microsoft. (2018). Responsible bots: 10 guidelines for developers of conversational AI. https://www.microsoft.com/en-us/research/publication/responsible-bots/

National and international AI strategies. (2018). Future of Life Institute. https://futureoflife.org/national-international-ai-strategies

Nelson, G. S. (2019). Bias in Artificial Intelligence. North Carolina Medical Journal, 80(4), 220–222. https://doi.org/10.18043/ncm.80.4.220

Ni, H., Chen, A., & Chen, N. (2010). Some extensions on risk matric approach. Safety Science, 48, 1269–1278. https://doi.org/10.1016/j.ssci.2010.04.005

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342

OECD. (2019). OECD Principles on AI. https://www.oecd.org/going-digital/ai/principles/

Polanyi, M. (2009). The tacit dimension. University of Chicago Press.

Renfroe, N. A., & Smith, J. L. (2007). Whole building design guide: threat/vulnerability assessments and risk analysis. Washington, DC: National Institute of Building Sciences. http://www.wbdg.org/design/riskanalysis.php

Roberts, H., Cowls, J., Morley, J., Taddeo, M., Wang, V., & Floridi, L. (2020). The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation. AI & Society, 1–19. https://doi.org/10.1007/s00146-020-00992-2

Rosenbloom, J.S. (1972). Case Study in Risk Management. Prentice Hall, 63–67.

Sajjadiani, S., Sojourner, A. J., Kammeyer-Mueller, J. D., & Mykerezi, E. (2019). Using machine learning to translate applicant work history into predictors of performance and turnover. Journal of Applied Psychology, 104(10), 1207. https://doi.org/10.1037/apl0000405

Sampson, C. J., Arnold, R., Bryan, S., Clarke, P., Ekins, S., Hatswell, A., Hawkins, N., Langham, S., Marshall, D., Sadatsafavi, M., Sullivan, W., Wilson, E. C. F., & Wrightson, T. (2019). Transparency in decision modelling: what, why, who and how?. Pharmacoeconomics, 1–15. https://doi.org/10.1007/s40273-019-00819-z

Sánchez, E. C., Sánchez-Medina, A. J., & Pellejero, M. (2020). Identifying critical hotel cancellations using artificial intelligence. Tourism Management Perspectives, 35, 100718. https://doi.org/10.1016/j.tmp.2020.100718

Sánchez-Medina, A. J., Galván-Sánchez, I., & Fernández-Monroy, M. (2020). Applying artificial intelligence to explore sexual cyberbullying behaviour. Heliyon, 6(1), e03218. https://doi.org/10.1016/j.heliyon.2020.e03218

Schaar, P. (2010). Privacy by design. Identity in the Information Society, 3(2), 267–274. https://doi.org/10.1007/s12394-010-0055-x

Summaries of AI policy resources. (2018). Future of Life Institute. https://futureoflife.org/ai-policy-resources/

Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: machine learning and Artificial Intelligence in sales research and practice. Industrial Marketing Management, 69, 135–146. https://doi.org/10.1016/j.indmarman.2017.12.019.

Tan, L., Liu, C., Li, Z., Wang, X., Zhou, Y., & Zhai, C. (2014). Bug characteristics in open source software. Empirical Software Engineering, 19(6), 1665–1705. https://doi.org/10.1007/s10664-013-9258-8

Thiebes, S., Lins, S., & Sunyaev, A. (2020). Trustworthy artificial intelligence. Electronic Markets, 1–18. https://doi.org/10.1007/s12525-020-00441-4

Torresen, J. (2018). A review of future and ethical perspectives of robotics and AI. Frontiers in Robotics and AI, 4, 75. https://doi.org/10.3389/frobt.2017.00075

Turton, W., & Martin, A. (2020). How deepfakes make disinformation more real than ever. Bloomberg. https://www.bloomberg.com/news/articles/2020-01-06/how-deepfakes-make-disinformation-more-real-than-ever-quicktake

Vogl, T. M., Seidelin, C., Ganesh, B., & Bright, J. (2020). Smart technology and the emergence of algorithmic bureaucracy: Artificial Intelligence in UK local authorities. Public Administration Review, 80(6), 946–961. https://doi.org/10.1111/puar.13286

Williams, C. A., & Heins, R. M. (1985). Risk Management and Insurance, 7–9. McGraw Hill.

Winfield, A. (2017). A round up of robotics and AI ethics. Alan Winfield’s Web Log. http://alanwinfield.blogspot.com/2019/04/an-updated-round-up-of-ethical

Zhang, Y., Guo, K., Ren, J., Zhou, Y., Wang, J., & Chen, J. (2017). Transparent computing: A promising network computing paradigm. Computing in Science & Engineering, 19(1), 7–20. https://doi.org/10.1109/MCSE.2017.17