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AI Safety Research
“What we really need to do is make sure that life continues into the future. […] It’s best to try to prevent a negative circumstance from occurring than to wait for it to occur and then be reactive.”
-Elon Musk on keeping AI safe and beneficial
In spring of 2018, FLI launched our second AI Safety Research program, this time focusing on Artificial General Intelligence (AGI) and how to keep it safe and beneficial. By the summer, 10 researchers were awarded over $2 million to tackle the technical and strategic questions related to preparing for AGI, funded by generous donations from Elon Musk and the Berkeley Existential Risk Institute. You can read about their projects in the table below.
This research program comes as a sequel to our AI Safety grants competition in 2015, where generous donations from Elon Musk and the Open Philanthropy Project funded 37 researchers to begin various projects to help ensure that artificial intelligence remains safe and beneficial. Now, three years later, our grant winners have produced over 45 scientific publications and a host of conference events, which you can also read about below.
AGI Safety Researchers 2018
Click on any of the researchers below for more information about their work.
Bai, Aijun and Russell, Stuart. Markovian State and Action Abstractions in Monte Carlo Tree Search. In Proc. IJCAI16, New York, 2016. https://people.eecs.berkeley.edu/~russell/papers/ijcai16-markov.pdf
Boddington, Paula. EPSRC Principles of Robotics: Commentary on safety, robots as products, and responsibility. Ethical Principles of Robotics, special issue, 2016.
Boddington, Paula. The Distinctiveness of AI Ethics, and Implications for Ethical Codes. Presented at IJCAI-16 Workshop 6 Ethics for Artificial Intelligence, New York, July 2016.
Fulton, Nathan and Platzer, André. A logic of proofs for differential dynamic logic: Toward independently checkable proof certificates for dynamic logics. Jeremy Avigad and Adam Chlipala, editors, Proceedings of the 2016 Conference on Certified Programs and Proofs, CPP 2016, St. Petersburg, FL, USA, January 18-19, 2016, pp. 110-121. ACM, 2016. http://nfulton.org/papers/lpdl.pdf
Garrabrant, Scott, et al. Asymptotically Coherent, Well Calibrated, Self-trusting Logical Induction. Working Paper (Berkeley, CA: Machine Intelligence Research Institute). 2016. https://arxiv.org/pdf/1609.03543.pdf
Hsu, L.K., et al. Tight variational bounds via random projections and i-projections. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, pages 1087–1095, 2016. https://arxiv.org/pdf/1510.01308v1.pdf
Khani, F., et al. Unanimous prediction for 100% precision with application to learning semantic mappings. Association for Computational Linguistics (ACL), 2016. http://arxiv.org/pdf/1606.06368v2.pdf
Leike, Jan, et al. A Formal Solution to the Grain of Truth Problem. Uncertainty in Artificial Intelligence: 32nd Conference (UAI 2016), edited by Alexander Ihler and Dominik Janzing, 427–436. Jersey City, New Jersey, USA. 2016. http://www.auai.org/uai2016/proceedings/papers/87.pdf
Pistono, F and Yampolskiy, RV. Unethical research: How to create a malevolent artificial intelligence. 25th International Joint Conference on Artificial Intelligence (IJCAI-16), Ethics for Artificial Intelligence Workshop (AI-Ethics-2016). https://arxiv.org/ftp/arxiv/papers/1605/1605.02817.pdf
➣Kristen Brent Venable, IHMC: Taught a new ad-hoc independent study course entitled “Ethics for Artificial Intelligence” during the spring 2016 semester with the goal of carrying out an in-depth state of the review of models for ethical issues and ethical values in AI.
➣ Owain Evans (AgentModels.org): An interactive online textbook, to communicate the idea of IRL to a broader audience and to give a detailed explanation of our approach to IRL to the existing AI Safety and AI/ML communities.
➣ Joshua Greene, Harvard: Spring 2016, graduate seminar “Evolving Morality: From Primordial Soup to Superintelligent Machines.”
➣Andre Platzer: Foundations of Cyber-Physical Systems (Spring 2016)
➣Stuart Russell, Tom Griffiths, Anca Dragan, UC Berkeley: Spring 2016, graduate course on “Human-Compatible AI”
➣ The Control Problem in AI: by the Strategic AI Research Centre
This was an intensive workshop at Oxford, with a large number of participants, and covered, among many other things, goals and principles of AI policy and strategy, value alignment for advanced machine learning, the relative importance of AI v. other x-risk, geopolitical strategy, government involvement, analysis of the strategic landscape, theory and methods of communication and engagement, the prospects of international-space-station-like coordinated AGI development, and an enormous array of technical AI control topics.
➣ Policies for Responsible AI Development: by the Strategic AI Research Centre
This workshop focused on a selection of key areas, such as: classifying risks, international governance, and surveillance. The workshop also engaged in a series of brainstorming and analysis exercises. The brainstorming sessions included “rapid problem attacks” on especially difficult issues, a session drafting various “positive visions” for different AI development scenarios, and a session (done in partnership with Open Philanthropy) which involved brainstorming ideas for major funders interested in x-risk reduction. This workshop even engaged in two separate “red team” exercises in which we sought out vulnerabilities, first in our own approach and research agenda, and then on global security.
➣ Intersections between Moral Psychology and Artificial Intelligence: by Molly Crockett and Walter Sinnott-Armstrong.
This workshop included two panels. The first asked whether artificial intelligence systems could ever provide reliable moral advice on a wide range of issues. Two speakers were skeptical about traditional top-down approaches, but the other two speakers argued that new alternatives are more promising. The second panel focussed on particular applications of artificial intelligence in war. The panelists again vigorously disagreed but left with a much better understanding of each other’s positions. Both panels were very diverse in their disciplinary backgrounds. The audience consisted of approximately 50 professors and students as well as members of the public.
➣ Moral AI Projects: by Vincent Conitzer, Walter Sinnott-Armstrong, Erin Taylor, and others.
Each part of this workshop included an in depth discussion of an innovative model for moral artificial intelligence. The first group of speakers explained and defended the bottom-up approach that they are developing with support from the FLI. The second session was led by a guest speaker who presented a dialogic theory of moral reasoning that has potential to be programmed into artificial intelligence systems. In the end, both groups found that their perspectives were complementary rather than competing. The audience consisted of around 20 students and faculty from a wide variety of fields.
➣ Embedded Machine Learning: by Dragos Margineantu (Boeing), Rich Caruana (Microsoft Research), Thomas Dietterich (Oregon State University)
This workshop took place at the AAAI Fall Symposium, Arlington, VA, November 12-14, 2015 and included issues of Unknown Unknowns in machine learning and more generally touched on issues at the intersection of software engineering and machine learning, including verification and validation.
➣ The Future of Artificial Intelligence: by Jacob Steinhardt, Stanford; Tom Dietterich, OSU; Percy Liang, Stanford; Andrew Critch, MIRI; Jessica Taylor, MIRI; Adrian Weller, Cambridge
The Future of Artificial Intelligence workshop was held at NYU. The first day consisted of two public sessions on the subject of “How AI is Used in Industry, Present and Future”. The first session included talks by Eric Schmidt (Alphabet), Mike Schroepfer (Facebook), Eric Horvitz (MSR), and me. This was followed by a panel including all of us plus Demis Hassabis (Deep Mind) and Bart Selman (Cornell). I talked about AI applications in science (bird migration, automated scientist), law enforcement (fraud detection, insider threat detection), and sustainability (managing invasive species). This session was generally very up-beat about the potential of AI to do great things. The second session had talks by Jen-Hsun Huang (NVIDIA), Amnon Shashua (Mobileye), John Kelly (IBM), and Martial Hebert (CMU). The final session turned toward the present and future of AI with presentations by Bernhard Schölkopf (Max Planck Institute), Demis Hassabis (Google DeepMind), and Yann LeCun (Facebook AI Research & NYU). Bernhard spoke about discovering causal relationships, Demis spoke about artificial general intelligence and his vision of how to achieve it. Yann discussed “differentiable programs” and raised the issue of whether we can differentiate traditional symbolic AI methods or need to adopt continuous representations for them.
The second and third days of the workshop were subject to Chatham House Rules. Many topics were discussed including (a) the impact of AI on the future of employment and economic growth, (b) social intelligence and human-robot interaction, (c) the time scales of AI risks: short term, medium term, and very long term, (d) the extent to which mapping the brain will help us understand how the brain works, (e) the future of US Federal funding for AI research and especially for young faculty, (f) the challenges of creating AI systems that understand and exhibit ethical behavior, (g) the extent to which AI should be regulated either by government or by community institutions and standards, and (h) how do we develop appropriate “motivational systems” for AI agents?
➣ Reliable Machine Learning in the Wild: by Jacob Steinhardt, Stanford; Tom Dietterich, OSU; Percy Liang, Stanford; Andrew Critch, MIRI; Jessica Taylor, MIRI; Adrian Weller, Cambridge.
This was an ICML Workshop, NY, June 23, 2016. This workshop discussed a wide range of issues related to engineering reliable AI systems. Among the questions discussed were (a) how to estimate causal effects under various kinds of situations (A/B tests, domain adaptation, observational medical data), (b) how to train classifiers to be robust in the face of adversarial attacks (on both training and test data), (c) how to train reinforcement learning systems with risk-sensitive objectives, especially when the model class may be misspecified and the observations are incomplete, and (d) how to guarantee that a learned policy for an MDP satisfies specified temporal logic properties. Several important engineering practices were also discussed, especially engaging a Red Team to perturb/poison data and making sure we are measuring the right data. My assessment is that a research community is coalescing nicely around these questions, and the quality of the work is excellent.
Over 50 people attended the colloquium series from 25 different institutions, including Stuart Russell (UC Berkeley), Bart Selman (Cornell), Francesca Rossi (IBM Research), and Tom Dietterich (Oregon State). MIRI also ran four research retreats, internal workshops exclusive to MIRI researchers
Workshop #1: Self-Reference, Type Theory, and Formal Verification. April 1-3.
Participants worked on questions of self-reference in type theory and automated theorem provers, with the goal of studying systems that model themselves. Participants: Benya Fallenstein (MIRI), Daniel Selsam (Stanford), Jack Gallagher (Gallabytes), Jason Gross (MIT), Miëtek Bak (Least Fixed), Nathaniel Thomas (Stanford), Patrick LaVictoire (MIRI), Ramana Kumar (Cambridge)
Workshop #2: Transparency. May 28-29.
In many cases, it can be prohibitively difficult for humans to understand AI systems’ internal states and reasoning. This makes it more difficult to anticipate such systems’ behavior and correct errors. On the other hand, there have been striking advances in communicating the internals of some machine learning systems, and in formally verifying certain features of algorithms. We would like to see how far we can push the transparency of AI systems while maintaining their capabilities.
Slides are up for Tom Dietterich’s overview talk at this workshop, “Issues Concerning AI Transparency” (https://intelligence.org/files/csrbai/dietterich-slides.pdf). Participants: Nate Soares (MIRI), Andrew Critch (MIRI), Patrick LaVictoire (MIRI), Jessica Taylor (MIRI), Scott Garrabrant (MIRI), Alan Fern (Oregon State University), Daniel Filan (Australian National University), Devi Borg (Future of Humanity Institute), Francesca Rossi (IBM Research), Jack Gallagher (Gallabytes), János Kramár (Montreal Institute for Learning Algorithms), Jim Babcock (unaffiliated), Marcello Herreshoff (Google), Moshe Looks (Google), Nathaniel Thomas (Stanford), Nisan Stiennon (Google), Sune Jakobsen (University College Longdon), Tom Dietterich (Oregon State University), Tsvi Benson-Tilsen (UC Berkeley), Victoria Krakovna (Future of Life Institute)
Workshop #3: Robustness and Error-Tolerance. June 4-5.
How can we ensure that when AI system fail, they fail gracefully and detectably? This is difficult for systems that must adapt to new or changing environments; standard PAC guarantees for machine learning systems fail to hold when the distribution of test data does not match the distribution of training data. Moreover, systems capable of means-end reasoning may have incentives to conceal failures that would result in their being shut down. We would much prefer to have methods of developing and validating AI systems such that any mistakes can be quickly noticed and corrected. Participants: Andrew Critch (MIRI), Patrick LaVictoire (MIRI), Jessica Taylor (MIRI), Scott Garrabrant (MIRI), Abram Demski (USC Institute for Creative Technologies), Bart Selman (Cornell), Bas Steunebrink (IDSIA), Daniel Filan (Australian National University), Devi Borg (Future of Humanity Institute), Jack Gallagher (Gallabytes), Jim Babcock, Nisan Stiennon (Google), Ryan Carey (Centre for the Study of Existential Risk), Sune Jakobsen (University College Longdon)
Workshop #4: Preference Specification. June 11-12.
The perennial problem of wanting code to “do what I mean, not what I said” becomes increasingly challenging when systems may find unexpected ways to pursue a given goal. Highly capable AI systems thereby increase the difficulty of specifying safe and useful goals, or specifying safe and useful methods for learning human preferences. Participants: Patrick LaVictoire (MIRI), Jessica Taylor (MIRI), Abram Demski (USC Institute for Creative Technologies), Bas Steunebrink (IDSIA), Daniel Filan (Australian National University), David Abel (Brown University), David Krueger (Montreal Institute for Learning Algorithms), Devi Borg (Future of Humanity Institute), Jan Leike (Future of Humanity Institute), Jim Babcock (unaffiliated), Lucas Hansen (unaffiliated), Owain Evans (Future of Humanity Institute), Rafael Cosman (unaffiliated), Ryan Carey (Centre for the Study of Existential Risk), Stuart Armstrong (Future of Humanity Institute), Sune Jakobsen (University College Longdon), Tom Everitt (Australian National University), Tsvi Benson-Tilsen (UC Berkeley), Vadim Kosoy (Epicycle)
Workshop #5: Agent Models and Multi-Agent Dilemmas. June 17.
When designing an agent to behave well in its environment, it is risky to ignore the effects of the agent’s own actions on the environment or on other agents within the environment. For example, a spam classifier in wide use may cause changes in the distribution of data it receives, as adversarial spammers attempt to bypass the classifier. Considerations from game theory, decision theory, and economics become increasingly useful in such cases. Participants: Andrew Critch (MIRI), Patrick LaVictoire (MIRI), Abram Demski (USC Institute for Creative Technologies), Andrew MacFie (Carleton University), Daniel Filan (Australian National University), Devi Borg (Future of Humanity Institute), Jaan Altosaar (Google Brain), Jan Leike (Future of Humanity Institute), Jim Babcock (unaffiliated), Matthew Johnson (Harvard), Rafael Cosman (unaffiliated), Stefano Albrecht (UT Austin), Stuart Armstrong (Future of Humanity Institute), Sune Jakobsen (University College Longdon), Tom Everitt (Australian National University), Tsvi Benson-Tilsen (UC Berkeley), Vadim Kosoy (Epicycle)
Workshop #6: Logic, Probability, and Reflection. August 12-14.
Participants at this workshop, consisting of MIRI staff and regular collaborators, worked on a variety of problems related to MIRI’s Agent Foundations technical agenda, with a focus on decision theory and the formal construction of logical counterfactuals. Participants: Andrew Critch (MIRI), Benya Fallenstein (MIRI), Eliezer Yudkowsky (MIRI), Jessica Taylor (MIRI), Nate Soares (MIRI), Patrick LaVictoire (MIRI), Sam Eisenstat (UC Berkeley), Scott Garrabrant (MIRI), Tsvi Benson-Tilsen (UC Berkeley)
➣ Control and Responsible Innovation in the Development of Autonomous Systems Workshop: by The Hastings Center
The four co-chairs (Gary Marchant, Stuart Russell, Bart Selman, and Wendell Wallach) and The Hastings Center staff (particularly Mildred Solomon and Greg Kaebnick) designed this first workshop. This workshop was focused on exposing participants to relevant research progressing in an array of fields, stimulating extended reflection upon key issues and beginning a process of dismantling intellectual silos and loosely knitting the represented disciplines into a transdisciplinary community. Twenty-five participants gathered at The Hastings Center in Garrison, NY from April 24th – 26th, 2016. The workshop included representatives from key institutions that have entered this space, including IEEE, the Office of Naval Research, the World Economic Forum, and of course AAAI. They are planning a second workshop, scheduled for October 30-November 1, 2016. The invitees for the second workshop are primarily scientists, but also include social theorists, legal scholars, philosophers, and ethicists. The expertise of the social scientists will be drawn upon in clarifying the application of research in cognitive science and legal and ethical theory to the development of autonomous systems. Not all of the invitees to the second workshop have considered the challenge of developing beneficial trustworthy artificial agents. However, we believe we are bringing together brilliant and creative minds to collectively address this challenge. We hope that scientific and intellectual leaders, new to the challenge and participating in the second workshop, will take on the development of beneficial, robust, safe, and controllable AI as a serious research agenda.
➣ A Day of Ethical AI at Oxford: by Michael Wooldridge, Peter Millican, and Paula Boddington
This workshop was held at the Oxford Martin School on June 8th, 2016. The goal of the workshop was collaborative discussion between those working in AI and ethics and related areas, between geographically close and linked centres. Participants were invited from the Oxford Martin School, The Future of Humanity Institute, the Cambridge Centre for the Study of Existential Risk, and the Leverhulme Centre for the Future of Intelligence, plus others. Participants included FLI grantholders. This workshop included participants from diverse disciplines, including computing,philosophy and psychology, to facilitate cross disciplinary conversation and understanding.
➣ Ethics for Artificial Intelligence: by Brian Ziebart
This workshop took place at IJCAI-’16, July 9th, 2016, in New York. This workshop focussed on selecting papers which speak to the themes of law and autonomous vehicles, ethics of autonomous systems, and superintelligence.
➣ Asaro, P. (2016) “AI Now: The Social and Economic Implications of Artificial Intelligence,” Whitehouse Workshop on AI, New York University, New York, NY, July 7, 2016. https://artificialintelligencenow.com/
➣ Asaro, P. (2016). “Autonomous Weapons,” Computers Gone Wild Workshop, Berkman Center for Internet and Society, Harvard University, Cambridge, MA, February 19, 2016. https://cyber.law.harvard.edu/node/99484
➣ Asaro, P. (2016). “Regulating Autonomous Agents: The Scope and Limits of Liability,” 4thAnnual Conference on Governance of Emerging Technologies: Law, Policy & Ethics, Arizona State University, Tempe, AZ, May 24-26, 2016. http://conferences.asucollegeoflaw.com/get2016/
➣Walter Sinnott-Armstrong: co-organized and spoke at a workshop on “Moral Issues in Artificial Intelligence”at the Oxford Martin School of Oxford University.
➣Seth Baum, Anthony Barrett, and Roman Yampolskiy presented their research at the 2015 Society for Risk Analysis Annual Meeting
➣ Seth Baum organized several informal meetings on AI safety with attendees from (among other places) CSER, FHI, MIRI, Yale, and the United Nations at the International Joint Conference on Artificial Intelligence
➣ Vincent Conitzer: participated in the ethics workshop at AAAI, describing our work on this project in a session and also serving on a panel on research directions for keeping AI beneficial.
➣Owen Cotton-Barratt: presented on new ideas at a one-day workshop on “Ethical AI” in Oxford on June 8, 2016. He has further developed informal models of likely crucial parameters to include in the models, and he now believes that the model should additionally include a division between scenarios where a single AI-enabled actor gains a decisive strategic advantage, and ones where this does not occur.
➣ Dietterich, T. G. (2015). Toward Beneficial Artificial Intelligence. Blouin Creative Leadership Summit, NY, NY, September 21, 2015.
➣ Dietterich, T. G. (2015). Artificial Intelligence: Progress and Challenges. Technical and Business Perspectives on the Current and Future Impact of Machine Learning. Valencia, Spain, October 20, 2015. Press coverage in El Mundo.
➣ Dietterich, T. G. (2015). Algorithms Among Us: The Societal Impacts of Machine Learning(opening remarks). NIPS Symposium. Montreal, Canada, December 10, 2015.
➣ Dietterich, T. G. (2016). AI in Science, Law Enforcement, and Sustainability. The Future of Artificial Intelligence. NYU, January 11, 2016.I also participated in a side meeting with Henry Kissinger on January 13 along with Max Tegmark and several other key people.
➣ Dietterich, T. G. (2016). Steps Toward Robust Artificial Intelligence(AAAI President’s Address). AAAI Conference on Artificial Intelligence, Phoenix, AZ. February 14, 2016.
➣ Dietterich, T. G. (2016). Testing, Verification & Validation, Monitoring. Control and Responsible Innovation in the Development of Autonomous Machines. Hastings Center, Garrison, NY, April 25, 2016.
➣ Dietterich, T. G. (2016). Steps Toward Robust Artificial Intelligence(short version). Huawei STW Workshop, Shenzhen, China, May 17, 2016.
➣ Dietterich, T. G. (2016). Steps Toward Robust Artificial Intelligence. Distinguished Seminar, National Key Laboratory for Novel Software Technology, University of Nanjing, Nanjing, China, May 19, 2016.
➣ Dietterich, T. G. (2016). Understanding and Managing Ecosystems through Artificial Intelligence. AI For Social Good. White House OSTP Workshop. Washington, DC, June 6-7, 2016.
➣ Dietterich, T. G., Fern, A., Wong, W-K., Emmott, A., Das, S., Siddiqui, M. A., Zemicheal, T.(2016). Anomaly Detection: Principles, Benchmarking, Explanation, and Theory. ICML Workshop on Anomaly Detection Keynote Speech.NY. June, 24, 2016.
➣ Dietterich, T. G. (2016). Making artificial intelligence systems robust. Safe Artificial Intelligence. White House OSTP Workshop, Pittsburgh, PA, June 28, 2016.
➣ Fern, A., Dietterich, T. G. (2016). Toward Explainable Uncertainty. MIRI Colloquium Series on Robust and Beneficial Artificial Intelligence.Alan and I also participated inthe two-day workshop on Transparency.MIRI, Berkeley, CA. May 27-29, 2016.
➣ Nathan Fulton:
Presented A Logic of Proofs for Differential Dynamic Logic: Toward Independently Checkable Proof Certificates for Dynamic Logics at The 5th ACM SIGPLAN Conference on Certified Programs and Proofs.
Nathan Fulton, Stefan Mitsch, and André Platzer presented a tutorial on KeYmaera X and hybrid systems verification at CPSWeek 2016, and a similar tutorial has been accepted at FM 2016.
F. Khani and M. Rinard and P. Liang. Unanimous prediction for 100% precision with application to learning semantic mappings. Association for Computational Linguistics (ACL), 2016. http://arxiv.org/pdf/1606.06368v2.pdf
➣ Francesca Rossi:
German conference on AI (KI 2015) in September 2015, titled “Safety constraints and ethical principles in collective decision making systems”
Ethics of AI — Two TEDx talks: TEDx Lake Como in November 2015, TEDx Ghent in June 2015, TEDx Osnabruck in April 2015
➣ Stuart Russell
“The long-term future of (artificial) intelligence”, invited lecture, Software Alliance Annual Meeting, Napa, Nov 13, 2015
“The Future of AI and the Human Race”, TedX talk, Berkeley, Nov 8, 2015
“Value Alignment”, invited lecture, Workshop on Algorithms for Human-Robot Interaction, Nov 18, 2015
“Killer Robots, the End of Humanity, and All That”, Award Lecture, World Technology Awards, New York, Nov 2015
“Should we Fear or Welcome the Singularity?”, panel presentation, Nobel Week Dialogue, December 2015
“The Future of Human-Computer Interaction”, panel presentation (chair), Nobel Week Dialogue, December 2015
“The Future Development of AI”, panel presentation, Nobel Week Dialogue, December 2015
“Some thoughts on the future”, invited lecture, NYU AI Symposium, January 2016
“The State of AI”, televised panel presentation, World Economic Forum, Davos, January 2016
“AI: Friend or Foe?” panel presentation, World Economic Forum, Davos, January 2016
“The long-term future of (artificial) intelligence”, CERN Colloquium, Geneva, Jan 16,2016
“Some thoughts on the future”, invited presentation, National Intelligence Council,Berkeley, Jan 28, 2016
“The long-term future of (artificial) intelligence”, Herbst Lecture, University of Colorado, Boulder, March 11 2016
“The Future of AI”, Keynote Lecture, Annual Ethics Forum, California State University Monterey Bay, March 16, 2016
“The long-term future of (artificial) intelligence”, IARPA Colloquium, Washington DC,March 21 2016
“AI: Friend or Foe?”, panel presentation, Milken Global Institute, Los Angeles, May 2,2016
“Will Superintelligent Robots Make Us Better People?”, Keynote Lecture (televised),Seoul Digital Forum, South Korea, May 19, 2016
“The long-term future of (artificial) intelligence”, Keynote Lecture, Strata Big Data Conference, London, June 2, 2016
“Moral Economy of Technology”, panel presentation, Annual Meeting of the Society for the Advancement of Socio-Economics, Berkeley, June 2016
➣ Michael Wooldridge and Paula Boddington:
EPSRC Systems-Net Grand Challenge Workshop, “Ethics in Autonomous Systems”, Sheffield University, November 25, 2015.
AISB workshop on Principles of Robotics, Sheffield University, 4 Apr 2016
Workshop examined the EPSRC (Engineering and Physical Sciences Research Council) Principles of Robotics. Boddington presented a paper, “Commentary on responsibility, product design and notions of safety”, and contributed to discussion.
Outcome of workshop: Paper for Special Issue of Connection Science on Ethical Principles of Robotics, ‘EPSRC principles of robotics: Commentary on Safety, Robots as Products, and Responsibility”–Paula Boddington
➣ Bas Steunebrink:
AAAI-16 conference in Phoenix.
Colloquium Series on Robust and Beneficial AI (CSRBAI), hosted by the Machine Intelligence Research Institute in Berkeley, in collaboration with the Future of Humanity Institute at Oxford.
AGI-16 conference in New York.
IEEE Symposium on Ethics of Autonomous Systems (SEAS Europe).
ECAI-16 conference in The Hague.
➣ Manuela Veloso
OSTP/NYU Workshop on The Social and Economic Implications of Artificial Intelligence Technologies in the Near-Term, NYC, July 2016.
Intelligent Autonomous Vehicles Conference, Leipzig, July 2016.