Content
What is this course about?
Artificial Intelligence has been applied to many corners of our life. And yet, there are raising concerns from the public about ethical issues of AI systems such as accountability, transparency, data privacy, fairness and biases, to name a few. In order for AI to be applied successfully in real- life, important ethical lessons must be learned from the recent failures of AI systems such as Microsoft AI chatbot Taylor (2018), which is turned into a racist chatbot within 24 hours, or the acident of Uber’s self-driving car, which killed a pedestrian in 2018.
The objective of this course is to equip students with necessary tools to deal with ethical issues and dilemmas in Artificial Intelligence systems. These tools are divided into governing methods such as developing code of ethics, and engineering approaches such as technical methods for explainable AI, deploying ethical values into AI systems.
Reference Texts
Text books
- Paula Boddington,Towards a Code of Ethics for Artificial Intelligence, Springer, 2017
- Derek Leben, Ethics for Robots: How to Design a Moral Algorithm, Routledge, 2019
- Marco Norskov,Social Robots: Boundaries, Potential, Challenges, Ashgate Publishing Limited, 2016
- Aimee Van Wynsberghe, Healthcare Robots: Ethics, Designand Implementation, Routledge, 2016
Other resources
- Anna Jobin, Marcello Icenca, Effy Vayena, Artificial Intelligence: the global landscape of ethics guidelines, 2018
- Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (3rd Edition)
- Chris Smith, and Brian McGuire, and Ting Huang, The History of Artificial Intelligence, University of Washington, 2016, link
- Google, Accelerating social good with artificial intelligence: insights from the Google AI Impact Challenge, 2019, link
- Jim Torresen, A Review of Future and Ethical Perspectives of Robotics and AI, 2018, link
- CJ Haughey, AI for Social Good: 7 inspiring examples, 2019, link
- Saurabh Mishra, and Raymond Perrault, and Yoav Shoham, and Erik Brynjolfsson, and Jack Clark, and John Etchemendy, and Barbara Grosz, and Terah Lyons, and James Manyika, and Juan Carlos Niebles, Artificial Intelligence Index, 2019 Annual Report, link
- A Reading list for AI Ethics, link
- An Nguyen, Understanding Differential Privacy, Towards Data Science, 2019
- Brent Daniel Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter, and Luciano Floridi, The ethics of algorithms: Mapping the debate, Big Data & Society, 2016
- Cassie Kozyrkov, Whose fault is it when AI makes mistakes? TowardsDataScience, 2018
- Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard Zemel, Fairness through Awareness, 2011
- David C. Vladeck, Machines without principles: liability rules and artificial intelligence, Washington Law Review, 2014
- Edmond Awad, Sohan Dsouza, Richard Kim, Jonathan Schulz, Joseph Henrich, Azim Shariff, Jean-Francois Bonnefon, and Yyad Rahwan, The Moral Machine Experiment, Nature, Vol 563, 2018.
- Jean-Francois Bonnefon, Azim Shariff, Iyad Rahwan, The social dilemma of autonomous vehicles, Science, vol. 352, pp. 1573-1576, 2016
- Gray Matter, Whose Life should your car save? The new York Times, 2019
- Karen Hao, The Biggest Threat of Deepfakes isn’t the deepfakes themselves, MIT Technology Review, 2019
- Karen. Hao, Giving Algorithms a Sense of Uncertainty Could Make Them more Ethical, MIT Technology Review, 2019
- Matt Barlett, The AI Arms Race in 2019, Towards Data Science, 2019
- Miles Brundage et. al, The Malicious Use of Artificial Intelligence – Forecasting, Prevention and Mitigation, 2018
- Princeton Dialogues on AI and Ethics: case studies, link
- Racial Bias and Gender Bias Examples in AI systems, Medium, 2018
- Siddhartha Mukherjee, AI vs MD: what happens when diagnosis is automated? The new Yorker, 2017
- Tomas Burrie, Machine Learning and the Law: Five Theses
- The IEEE Global Initiative for Ethical Considerations for Artificial Intelligence and Autonomous Systems
- Wendell Wallach, Colin Allen, and Iva Smit, Machine Morality: Bottom Up and Top-down approaches for Modeling human moral faculties