AI Safety, Ethics, and Policy

Jan 17^th Introduction to class

*After class: introduction email due before the weekend

 

Jan 22^nd The past and future of AI

Guest speaker: Hod Lipson

 

Jan 24^th Superintelligence?

Bostrom, Superintelligence chapters 1-5

Allen, Paul, "The singularity isn't near" Technology Review October 12,2011

Felten, Ed. "Why the singularity is not a singularity", series of 4 blog posts starting at http://freedom-to-tinker.com/2018/01/03/why-the-singularity-is-not-a-singularity/

 

Jan 29^th Superintelligence?

Bostrom, Superintelligence chapters 6-10

 

Jan 31^st Superintelligence & resource guide

Bostrom, Superintelligence chapters 6-10 (discussion continued)

Bryson, Joanna. "Tomorrow comes today: How policymakers should approach AI"

AI 100 Report https://ai100.stanford.edu/2016-report

 

Feb 5^th Superintelligence?

Bostrom, Superintelligence chapters 11-15

 

Feb 7^th AI Policy and regulation

National Science and Technology Council (2016) National Artificial Intelligence Research and Development Strategic Plan

Crawford, Kate, and Ryan Calo. "There is a blind spot in AI research." Nature 538 (2016): 311-313.

Optional:

• Ryan Calo. “AI Policy: A Roadmap” (2017)
• Sunstein, C. R. (2003). Beyond the precautionary principle. University of Pennsylvania Law Review, 151(3), 1003-1058.

 

Feb 12^th Regulation of AI

Scherer, Matthew U., (2016) "Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies, and Strategies" Harvard Journal of Law & Technology, Vol. 29, No. 2, Spring 2016

 

Feb 14th Regulation of AI and intro to economic effects of AI

Ford, Martin. Rise of the Robots chapters 2 and 8

Weinberger, David. Optimization over Explanation Medium 1/28/18

Optional:

• Picketty Explained

 

Feb 19^th Economic effects of AI

Brynjolfsson, E., Rock, D., & Syverson, C. (2017). "Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics" (No. w24001). National Bureau of Economic Research. (Available on CourseWorks)

Mokyr, Joel, Chris Vickers, and Nicolas L. Ziebarth (2015). "The history of technological anxiety and the future of economic growth: Is this time different?." The Journal of Economic Perspectives 29.3 (2015): 31-50.

Metz, Cade. As China Marches Forward on AI, the White House is Silent The New York Times, February 12, 2018 (Available on CourseWorks)

Optional:

• Gregory Mankiw "Yes, r > g. So what?" American Economic Review: Papers and Proceedings 2015, 105(5): 43

 

Feb 21^st Economic effects of AI

Frey, Carl Benedikt, and Michael A. Osborne. "The future of employment: how susceptible are jobs to computerisation?." Technological Forecasting and Social Change 114 (2017): 254-280. (Available on CourseWorks)

Hariri, Yuval Noah. “The Meaning of life in a world without work” The Guardian 5/8/17 https://www.theguardian.com/technology/2017/may/08/virtual-reality-religion-robots-sapiens-book

Stacey, Kiran and Anna Nicolaou (2017). Stiched up by robots: the threat to emerging economies The Financial Times, July 18, 2017 (Available on CourseWorks)

Optional:

• John Maynard Keynes, Economic Possibilities for our Grandchildren (1930)
• Frey et al, “Automation Anxiety and the 2016 Presidential Election” Oxford Martin School working paper

 

Feb 26^st Economic effects of AI

Guest lecturer: Professor Joseph Stiglitz

Korinek, A., & Stiglitz, J. (2017). "Artificial Intelligence and its implications for income distribution and unemployment", National Bureau of Economic Research, Inc. http://www.nber.org/papers/w24174 (Availabe on CourseWorks)

 

Feb 28^th Fairness, bias, inequality

Eubanks, Virginia. (2018) Automating Inequality Chapters 1-2

 

March 5^th Fairness, bias, and Inequality

Eubanks, Virginia. (2018) Automating Inequality Chapters 3 - 4

Chouldechova, A. et al (2018) A case study of algorithm-assisted decision-making in child maltreatment hotline screening decisions Conference on Fairness, Accountability, and Transparency

Hurley, Dan. (2018) Can an Algorithm Tell if Kids are in Danger? The New York Times Magazine, January 2, 2018

 

March 7^th Predictive Policing

Eubanks, Virginia. (2018) Automating Inequality chapter 5 and conclusion

Angwin, J. et al (2016) Machine Bias ProPublica, May 23, 2016

Asher, Jeff and Arthur, Rob. (2017) Inside the Algorithm That Tries to Predict Gun Violence in Chicago The New York Times The Upshot, June 13, 2017

Human Rights Watch. (2018) China: Big Data Fuels Crackdown in Minority Region February 26, 2018

Denyer, Simon. (2016) China wants to give all of its citizens a score The Independent, October 22, 2016

Optional:

• Fergason, A. (2017) Policing Predictive Policing Washington University Law Review, Vol. 94, No. 5, 2017
• Fergason, A. and Danaher, J. (2017) John Danaher's podcast interviewing Andrew Fergason about predictive policing February 19, 2017
• Hvistendahl, M. (2017) Inside China's vast new experiment in social ranking Wired, December 14, 2017

 

 

SPRING BREAK

March 19^th Concrete AI Safety

Amodei, D., et al. Concrete problems in AI Safety.

Hadfield-Menell, D.. et al (2016). Cooperative inverse reinforcement learning. Advances in neural information processing systems (pp. 3909-3917).

Optional:

• Brundage, M (2018). The Malicious Use of AI
• Leike, J. et al (2017). AI Safety Gridworlds.

 

March 21^st Snow day

 

March 26^th Fairness

Short paper due!

Guest lecturer: Ana-Andreea Stoica

Dwork, C. et al (2011) Fairness Through Awareness.

Gabriel, I. (2018) The case for fairer algorithms.

Narayanan, A. (2018) Tutorial on Fairness Definitions at FAT*.

 

March 28^th Rights and moral consideration for AI and robots?

Guest lecturer: Professor Joanna Bryson

Bryson, J. (2018) Patiency is not a virtue: the design of intelligent systems and systems of ethics.

Bryson, J. (2018) "Tomorrow comes today: How policymakers should approach AI"

Optional:

• Heller, Nathan. (2016) “If animals have rights, should robots?” The New Yorker 11/28/2016 https://www.newyorker.com/magazine/2016/11/28/if-animals-have-rights-should-robots
• Solum, Lawrence B. "Legal personhood for artificial intelligences." NCL Rev. 70 (1991): 1231.

 

April 2^nd Ethics and AI: teaching machines to be moral?

Wallach, W., & Allen, C. (2008). Moral machines: ceaching robots right from wrong. Oxford University Press. Introduction - Chapter 7

 

April 4^th No class

 

April 9^th AI Safety

Guest lecturer: Professor Jeannette Wing

Clarke, E. M., & Wing, J. M. (1996). Formal methods: State of the art and future directions. ACM Computing Surveys (CSUR), 28(4), 626-643.

Seshia, S. A., Sadigh, D., & Sastry, S. S. (2016). Towards verified artificial intelligence. arXiv preprint arXiv:1606.08514.

 

April 11^th Ethics and AI: teaching machines to be moral?

Wallach, W., & Allen, C. (2008). Moral machines: Teaching robots right from wrong. Oxford University Press. Chapter 8 - Epilogue

 

April 16^th Moral Machines and the EU's GDPR

Finishing discussion of Wallach and Allen, Moral Machines

"Moral Machines" chapter in Artificial Intelligence: A National Strategic Initiative for Artificial Intelligence by Tencent and the China Academy of Information and Communications Technology, translated by Jeffrey Ding

Selbst, A. and Powles, J. (2017) Meaningful Information and the Right to an Explanation International Data Privacy Law, vol. 7(4), 233-242 (also available on CourseWorks)

Burgess, M. (2018) What is GDPR? The need to know guide Wired, April 5th, 2018

 

April 18^th AI and National Security

Allen, G. and Chan, T. (2017) Artificial Intelligence and National Security Harvard Kennedy School Belfer Center study

Kania, E. (2017) Artificial Intelligence and Chinese Power Foreign Affairs website, December 5, 2017

 

April 23^rd Autonomous Weapons

Asaro, P. (2012). On banning autonomous weapon systems: human rights, automation, and the dehumanization of lethal decision-making, International Review of the Red Cross, 94, 687-709.

Lewis, J. (2017) The Case for Regulating Fully Autonomous Weapons Systems The Yale Law Journal 124(4): 1309-1325

Scharre, P. (2017) Why you shouldn't fear slaughterbots IEEE Spectrum

Russell, S. et al (2017) Why you should fear slaughterbots: a response IEEE Spectrum

 

Optional:

• Kanwar, Vik. "The Post-Human Humanitarian Law: The Law of War in the Age of Robotic Weapons." Harv. Nat'l Sec. J. 2 (2011): 616.
• Crootof, Rebecca. (2016). “The Meaning of ‘Meaningful Human Control”. Temple International & Comparative Law Journal, Vol. 30. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2705560
• Human Rights Watch (2012) ‘Losing Humanity: The Case against Killer Robots’. https://www.hrw.org/report/2012/11/19/losing-humanity/case-against-killer-robots
• International Committee of the Red Cross, “International Humanitarian Law: your questions answered”
• Morrow, J. (2007). When Do States Follow the Laws of War? American Political Science Review, 101(3), 559-572.

 

April 25th ^th Explainability

Lipton, Z.C. (2016) The Mythos of Model Interpretability

Olah, C. et al (2018) Building blocks of interpretibility Distill

 

Optional:

• Kroll, J. A., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2016). Accountable algorithms. U. Pa. L. Rev., 165, 633.>

 

 

April 29^th TAKE HOME FINAL EXAM DUE BEFORE MIDNIGHT

 

April 30^th AI and the good life

Nussbaum, Martha (2000). Women and Human Development: The Capabilities Approach. Pages 70 - 86

 

May 2^nd Special Class Session

Guest lecturer: Professor Alondra Nelson

White House National Science and Technology Council (2016). Preparing for the future of AI

Buolamwini, J. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Conference on Fairness, Accountability and Transparency

 

May 7^th FINAL PAPER / PROJECT DUE BEFORE MIDNIGHT

 

May 8^th FINAL PAPER / PROJECT PRESENTATION SLIDES DUE BEFORE 5PM

 

May 9th Final student presentations

Students will discuss their final papers / projects in 5 minute presentations

9 - 12 in the regular classroom

 

 

Additional Resources on adversarial attacks on deep learning systems

Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014). Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572.

Rauber, J., Brendel, W., & Bethge, M. (2017). Foolbox v0. 8.0: A Python toolbox to benchmark the robustness of machine learning models. arXiv preprint arXiv:1707.04131.

Robust Vision Benchmark https://robust.vision/benchmark/leaderboard/