Ethics of Autonomous Vehicles
Adoption of self-driving, Autonomous Vehicles (AVs) promises to dramatically reduce the number of traffic accidents, but some inevitable accidents will require AVs to choose the lesser of two evils, such as running over a pedestrian on the road or the sidewalk.
Defining the algorithms to guide AVs confronted with such moral dilemmas is a challenge, and manufacturers and regulators will need psychologists to apply methods of experimental ethics to these situations.
Selected Media: New York Times, Washington Post (1), Wall Street Journal, Time, Independent (1), Guardian, CBS News, LA Times, Forbes, Newsweek, CBC (Canada), ABC (Australia), Le Monde (FR), El Pais (ES), Science, Scientific American, New Scientist, PBS NOVA Next, Wired, Huffington Post, MIT News, Independent (2), Washington Post (2), Washington Post (3), BBC World Service (live interview), Huffington Post (live interview), New York Magazine, Popular Science, MIT Technology Review
The Moral Machine is a platform for gathering a human perspective on moral decisions made by machine intelligence, such as self-driving cars. We generate moral dilemmas, where a driverless car must choose the lesser of two evils, such as killing two passengers or five pedestrians. As an outside observer, people judge which outcome they think is more acceptable. They can then see how their responses compare with other people. If they are feeling creative, people can also design their own scenarios, for others to view, share, and discuss.
The platform can be visited at http://moralmachine.mit.edu
Selected Media: Guardian, LA Times, CBC (Canada), Le Monde (FR), El Pais (ES), Scientific American, New Scientist, Wired, Huffington Post, Slate, The Next Web
Promoting Cooperation through Peer Pressure
Cooperation in a large society of self-interested individuals is notoriously difficult to achieve when the externality of one individual's action is spread thin and wide on the whole society (e.g. in the case of pollution). We introduce a new approach to achieving global cooperation by localizing externalities to one's peers in a social network, thus leveraging the power of peer-pressure to regulate behavior. Global cooperation becomes more like local cooperation.
Since Alan Turing envisioned Artificial Intelligence (AI), a major driving force behind technical progress has been competition with human cognition (e.g. beating humans in Chess or Jeopardy!). Less attention has been given to developing autonomous machines that learn to cooperate with humans. Cooperation does not require sheer computational power, but relies on intuition, and pre-evolved dispositions toward cooperation, common-sense mechanisms that are difficult to encode in machines. We develop state-of-the-art machine-learning algorithms that cooperate with people and other machines at levels that rival human cooperation in two-player repeated games.
Corruption-Resistant Cooperation: Institutions vs. Crowds
How to best govern society and promote cooperation is a centuries-old debate: is cooperation best maintained by a central authority, or is it better handled by more decentralized forms of governance? Using mathematical models, we show that when some actors can bribe a powerful centralized authority, they can completely undermine cooperation in society. Counterintuitively, a weaker centralized authority is more effective because it allows peer punishment to restore cooperation in the presence of corruption. Our results help explain why citizen participation is a fundamental necessity for policing the commons.
Selected Media: Anti-Corruption Research Network (part of Transparency International)
Cognitive Limits of Social Networks
There is a wide cultural belief in the power of the Web and social media as enablers of collective intelligence. They help us spread information rapidly, and learn useful information from each other. But there are fundamental limits to the capabilities of those networks. Understanding these limits is essential to improving social media and allowing society to make the most of it.
Selected Media: Royal Society, Daily Mail, Live Science, Phys.org
Crowdsourcing Under Attack
The Internet has unleashed the capacity for planetary-scale collective problem solving (also known as crowdsourcing). However, the very openness of crowdsourcing makes it vulnerable to sabotage by rogue or competitive actors. To explore the effect of errors and sabotage on the performance of crowdsourcing, we analyze data from the DARPA Shredder Challenge, a prize competition for exploring methods to reconstruct documents shredded by a variety of paper shredding techniques.
Selected Media: Nautilus, Backchannel, UCSD Press Release
Crowdsourcing a Manhunt
People often say that we live in a small world. In a brilliant experiment, legendary social psychologist Stanley Milgram proved the six degrees of separation hypothesis: that everyone is six or fewer steps away, by way of introduction, from any other person in the world. But how far are we, in time, from anyone on earth? Our team won the Tag Challenge, showing it is possible to find a person, using only his or her mug shot, within 12 hours.
Selected Media: New Scientist, Nature, Scientific American, Economist, New Scientist, Nextgov, MIT Technology Review
The Red Balloon Challenge
In 2009, DARPA launched the Network Challenge, to explore the roles the Internet and social networking play in the timely communication, wide-area team-building, and urgent mobilization required to solve broad-scope, time-critical problems. The challenge was to be the first to locate 10 moored, 8-foot, red weather balloons at 10 random locations in the continental United States. A team from MIT won by locating all balloons in under 9 hours. We helped analyze the factors behind the team's success. We then quantified the limits of this kind of mobilization, and introduced techniques for improving information verification in mass collaboration.
Selected Media: MSNBC, Popular Mechanics, Science News, NBC News, ABC, La Repubblica, ACM, Red Orbit, Science 2.0, Science Daily, MIT News
Mapping Social Influence: Beyond Contagion
Social networks shape our mood, emotions, and behavior. New unobtrusive sensing techniques (wearable sensors, mobile phones, social media), allow us to study these social dynamics in the real world with unprecedented detail, complementing old methods like lab studies and surveys. We discovered that while some behaviors spread on social networks like a virus (you catch the blues from your friends), other behaviors work in the opposite direction, where our behavior changes to complement the behavior of others. Moreover, our individual personality traits affect how these processes work. These findings inform potential interventions designed to improve societal well-being.
J. F. Bonnefon, A. Shariff, I. Rahwan (2016). The Social Dilemma of Autonomous Vehicles. Science. 352(6293):1573-1576.
[Paper] [Selected Media: New York Times, Wall Street Journal, Washington Post, Scientific American, Time, CNN, The Guardian, Gizmag, Wired, LA Times, IEEE Spectrum, Forbes, Washington Post, BBC Radio, Huffington Post (live interview), New York Magazine, Independent, MIT Technology Review, Popular Science]
A. Alshamsi, F. Pianesi, B. Lepri, A. Pentland, I. Rahwan (2015). Beyond contagion: Reality mining reveals complex patterns of social influence. PLOS ONE. 10(8): e0135740.
[Published Version (open access)]
A. Alshamsi, E. Awad, M. Almehrezi, V. Babushkin, P.-J. Chang, Z. Shoroye, A.-P. Toth, I. Rahwan (2015). Misery Loves Company: Happiness and Communication in the City. EPJ Data Science. 4:7.
[Published Version (open access)]
N. Stefanovitch, A. Alshamsi, M. Cebrian, I. Rahwan (2014). Error and attack tolerance of collective problem solving: The DARPA Shredder Challenge. EPJ Data Science. vol 3, no 13, pages 1-27.
[Paper] [Videos: Puzzle2, Puzzle4] [Media: Nautilus, UCSD Press Release, Backchannel]
I. Rahwan, D. Krasnoshtan, A. Shariff, J. F. Bonnefon (2014). Analytical reasoning task reveals limits of social learning in networks. Journal of the Royal Society Interface. 11(93).
[Published Version] [Selected Media: Royal Society, Daily Mail, Live Science, Phys.org, Interview with Azim]
S. Abdallah, R. Sayed, I. Rahwan, B. LeVeck, M. Cebrian, A. Rutherford, J. Fowler (2014). Corruption Drives the Emergence of Civil Society. Journal of the Royal Society Interface. 11(93).
[arXiv preprint] [Published Version] [Media: Anti-Corruption Research Network (part of Transparency International)]
A. Rutherford, M. Cebrian, I. Rahwan, S. Dsouza, J. McInerney, V. Naroditskiy, M. Venanzi, N. R. Jennings, J.R. deLara, E. Wahlstedt, S. U. Miller (2013). Targeted social mobilization in a global manhunt. PLOS ONE 8(9): e74628
[Paper] [Selected Media: MIT Technology Review, New Scientist, Nature, Scientific American]
A. Rutherford, M. Cebrian , S. Dsouza, E. Moro, A. Pentland, and I. Rahwan (2013). Limits of Social Mobilization. Proceedings of the National Academy of Sciences, vol. 110 no. 16 pp. 6281-6286
[Paper] [Selected Media: NBC News, ABC, La Repubblica, Arabian Gazette, Gulf Today, The National]
I. Rahwan, S. Dsouza, A. Rutherford, V. Naroditskiy, J. McInerney, M. Venanzi, N. R. Jennings, M. Cebrian (2013). Global Manhunt Pushes the Limits of Social Mobilization. Computer, vol. 46, no. 4, pp. 68-75.
[PDF Preprint] [Selected Media: The Economist, Nextgov, New Scientist, Popular Science, The National, Vision (cover story)]
S. Dsouza, Y. Gal, P. Pasquier, S. Abdallah, and I. Rahwan (2013). Reasoning about Goal Revelation in Human Negotiation. IEEE Intelligent Systems. vol. 28, no. 2, pp. 74-80.
V. Naroditskiy, I. Rahwan, M. Cebrian, N. R. Jennings (2012). Verification in Referral-Based Crowdsourcing. PLOS ONE 7(10): e45924.
[Paper] [Selected Media: ACM, Red Orbit, Science 2.0, Science Daily]
G. Pickard, W. Pan, I. Rahwan, M. Cebrian, R. Crane, A. Madan,
A. Pentland (2011). Time-Critical Social Mobilization. Science. Vol. 334 no. 6055 pp. 509-512.
[Paper] [Selected Media: MSNBC, Popular Mechanics, MIT News, Science News]
Scalable Cooperation, MIT Media Lab
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