Implementing AI Ethics (March 22-24)

AI Principles

• Video Content

  • Foundations of Responsible Computing 2020: Keynote by Adrian Weller 

  • Responsible artificial intelligence by Virginia Dignum

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• Papers

  • Review of bias in algorithmic decision making, UK CDEI report

  • Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1-11.

  • Gupta, A., Lanteigne, C., Heath, V., Ganapini, M.B., Galinkin, E., Cohen, A., Gasperis, T.D., Akif, M., & Butalid, R. (2020). The State of AI Ethics Report (June 2020). ArXiv, abs/2006.14662.

  • U. Bhatt, A. Xiang, S. Sharma, A. Weller, A. Taly, Y. Jia, J. Ghosh, R. Puri, J. Moura and P. Eckersley. Explainable Machine Learning in Deployment. In the ACM Conference on Fairness, Accountability, and Transparency (FAT*), 2020.

  • A. Weller. Transparency: Motivations and Challenges. Chapter 2 in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer Lecture Notes in Computer Science, vol 11700, edited by W. Samek, G. Montavon, A. Vedaldi, L. Hansen, K. Muller, 2019.
    Preprint

  • T. Speicher, H. Heidari, N. Grgić-Hlača, K. P. Gummadi, A. Singla, A. Weller and M. Zafar. A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices. In Knowledge Discovery and Data mining (KDD), 2018 

  • N. Grgić-Hlača, E. Redmiles, K. P. Gummadi and A. Weller. Human perceptions of fairness in algorithmic decision making: A case study of criminal risk prediction. In The Web Conference (WWW), 2018.

  • N. Grgić-Hlača, M. Zafar, K. P. Gummadi and A. Weller. Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning. In the Association for the Advancement of Artificial Intelligence conference (AAAI), 2018.

  • M. Zafar, I. Valera, M. Rodriguez, K. P. Gummadi and A. Weller. From parity to preference-based notions of fairness in classification. In Neural Information Processing Systems (NeurIPS), 2017.

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Machine Ethics

• Video Content

  • Three principles for creating safer AI, Stuart Russell, TED, 2017

  • Moral Machines: From Machine Ethics to Value Alignment, Wendell Wallach,

  • Verifiability Talk 1: Verifying Machine Ethics, Louise Dennis, U of Manchester

  • What is Machine Ethics?, Louise Dennis, U of Manchester

• Papers

  • Ruseell, S. (2017).  Provably beneficial artificial intelligence. The Next Step: Exponential Life, BBVA OpenMind.

  • Wallach, W., Allen, C., & Franklin, S. (2011). Consciousness and Ethics: Artificially Conscious Moral Agents. International Journal of Machine Consciousness, 03, 177-192.

  • Tolmeijer, S., Kneer, M., Sarasua, C., Christen, M., & Bernstein, A. (2020). Implementations in Machine Ethics: A Survey. ArXiv, abs/2001.07573.

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Automating Machine Ethics

• Video Content

  • The Ethical Roboticist

  • Robot Ethics in the 21st Century

  • Immanuel the ethical reasoning robot

• Papers​

  • Nallur, V. (2020). Landscape of Machine Implemented Ethics. Science and Engineering Ethics, 1-19.​

  • Anderson, M., Anderson, S. L. and Berenz, V. (2019). A value-driven eldercare robot: Virtual and physical instantiations of a case-supported principle-based behavior paradigm. Proceedings of the IEEE, 107(3), pp. 526–540

  • Berreby, F., Bourgne, G. and Ganascia, J.-G. (2018). Event-based and scenario-based causality for computational ethics. Proceedings of the 17th international conference on autonomous agents and MultiAgent systems. pp. 147–155.

  • Cointe, N., Bonnet, G. and Boissier, O. (2016). Ethical judgment of agents’ behaviors in multi-agent systems. Proceedings of the 2016 international conference on autonomous agents & multiagent systems. pp. 1106–1114. 

  • Dennis, L. and Fisher, M. (2018). Practical challenges in explicit ethical machine reasoning, in International symposium on artificial intelligence and mathematics.

  • Lindner, F., Bentzen, M. M. and Nebel, B. (2017). The HERA approach to morally competent robots. 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp. 6991–6997. doi: 10.1109/iros.2017.8206625.

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Enterprise AI

​Preread Summary: here

• Video Content

  • How can we design responsible AI – World Economic Forum, 2019.

• Papers

  • Responsible AI Toolkit – PwC

  • The Responsible Machine Learning Principles – The Institute for Ethical AI & Machine Learning

  • Responsible AI Resources – Microsoft

  • Responsible AI – Google

  • A practical guide to Responsible AI – PwC

  • Ten Principles of Responsible AI for Corporates

  • Top-down and end-to-end governance for the responsible use of AI

  • [Newly Added]  When Machine Learning Goes off the Rails

  • [Newly Added]  Implementing AI principles: Frameworks, Processes, and Tools

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Public Sector AI

​Preread Summary: here

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• Papers

  • [Newly Added] National Security Commission On Artificial Intelligence (2021). Final Report. 

  • Vincent, V.R. (2020). AI Watch - National strategies on Artificial Intelligence: A European perspective in 2019.

  • Blasch, E., Sung, J., Nguyen, T., Daniel, C.P., & Mason, A.P. (2019). Artificial Intelligence Strategies for National Security and Safety Standards. ArXiv, abs/1911.05727.

  • Hill, S. (2020). AI's Impact on Multilateral Military Cooperation: Experience from NATO. AJIL Unbound, 114, 147 - 151.

  • A framework for developing a National AI Strategy. World Economic Forum.

  • AI Procurement in a box. World Economic Forum.

  • Fhaoláin, Labhaoise Ní and A. Hines. “Could regulating the creators deliver trustworthy AI?” ArXiv abs/2006.14750 (2020).

  • NíFhaoláin, Labhaoise et al. “Assessing the Appetite for Trustworthiness and the Regulation of Artificial Intelligence in Europe.” AICS (2020).

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Operationalizing AI Ethics

• Video Content

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• Papers

  • Canca, C. (2020). Operationalizing AI ethics principles. Communications of the ACM, 63, 18 - 21.

  • Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). 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, 26, 2141 - 2168.

  • Baxter, K., Schlesinger, Y., Aerni, S., Baker, L., Dawson, J., Kenthapadi, K., Kloumann, I.M., & Wallach, H. (2020). Bridging the gap from AI ethics research to practice. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency.

  • Hallensleben, S., Fetic, L., Fleischer, T., Grünke, Hagendorff, T., Hauer, M.P., Hauschke, A., Heesen, J., Herrmann, M., Hillerbrand, R., Hubig, C., Kaminski, A., Krafft, T.D., Loh, W., Otto, P., Puntschuh, M., & Hustedt, C. (2020). From Principles to Practice : An interdisciplinary framework to operationalise AI ethics.

  • Walz, A., & Firth-Butterfield, K. (2019). Implementing ethics into artificial intelligence: a contribution, from a legal perspective, to the development of an AI governance regime. Duke law and technology review, 18, 176-231.

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Certifications and Standards

• Video Content

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• Papers

  • IEEE Ethically Aligned Design

  • Raising the standards in AI Systems by IEEE

  • Global perspective and Insights – The IIA’s AI auditing framework

  • AI applied to risk management – Foundation of European Risk Management Associations

  • Emerging Technology Certifications - CERTNEXUS

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