Mathematical Foundations for Social Computing

Communications of the ACM

Yiling Chen (yiling@seas.harvard.edu) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (arpitaghosh@cornell.edu) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (mkearns@cis.upenn.edu) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (tim@cs.stanford.edu) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (jenn@microsoft.com) is a senior researcher at Microsoft Research, New York, NY.


Why AI Needs To Reflect Society

#artificialintelligence

While artificial intelligence (AI) has the potential to solve an incredible spectrum of problems and challenges in our lives, our work and our world, there is a widening disconnect between the people who are introducing and deploying AI-based solutions and those who set policies for when and how these solutions are used. Much has been written about one consequence of this disconnect--algorithmic bias in AI systems, in which machine learning algorithms trained on data that reflects historical discrimination replicate and even magnify it. But there's another pressing issue: There are many missed opportunities to use AI for the good of many. Just as AI systems susceptible to bias are a problem, so too is inadequate focus on contributions that improve the lives of marginalized communities, such as Black and brown individuals, economically vulnerable populations and many other groups whose interests are underserved in society. If teams that set research directions, write algorithms or deploy them are made up of individuals with similar backgrounds and experiences, then we will end up with research that is to the benefit of a similarly narrow and already privileged subset of society.


Mathematical Foundations for Social Computing

#artificialintelligence

Yiling Chen (yiling@seas.harvard.edu) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (arpitaghosh@cornell.edu) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (mkearns@cis.upenn.edu) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (tim@cs.stanford.edu) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (jenn@microsoft.com) is a senior researcher at Microsoft Research, New York, NY.


Computational Sustainability and Artificial Intelligence in the Developing World

AI Magazine

Despite some difficult problems in such places, a period of enormous technologydriven change has created new opportunities to address poor management of resources and improve human wellbeing. As just one example of the possibilities, however, take road traffic in cities. The chaotic and spectacular road congestion that is characteristic of developing-world cities is a microcosm of opportunities for applying AI methods. The problems are mainly caused by inadequate infrastructure (for example, road layouts that have not changed significantly despite decades of economic growth, unsealed or pothole-strewn roads), and a lack of resources to monitor or control traffic (for example, scarce and possibly corrupt traffic police, rolling blackouts affecting traffic lights). Any such solution must take into account the unique nature of traffic in these places, where the assumptions made in developed-world intelligent transport systems -- for example, that drivers travel in the correct direction, and only on the road -- might not be valid.


Computational Sustainability and Artificial Intelligence in the Developing World

AI Magazine

The developing regions of the world contain most of the human population and the planet's natural resources, and hence are particularly important to the study of sustainability. Despite some difficult problems in such places, a period of enormous technology-driven change has created new opportunities to address poor management of resources and improve human well-being.