From Social to Prosocial Machines: A New Challenge for AI
Time
March 22, 4:30 PM GMT+2 (Local: )
About
Throughout the past few years, artificial intelligence (AI) has become increasingly more present in our daily lives. A myriad of settings became the stage for AI applications, such as factories, roads, houses, hospitals and even schools. Given these new contexts, AI-powered machines must now place the human at the centre, and be designed to interact with humans in a natural way: AI is becoming social.
But such a diverse use of AI also fosters change, especially in the way we behave and how with cooperate with each other and with machines. It is therefore important to reflect upon the impact that AI may have on humans’ societies, and consider its effects on supporting more collaboration, social action, and prosocial behavior. Prosocial behavior occurs when people and agents perform costly actions that benefit others. Acts such as helping others voluntarily, donating to charity, providing information or sharing resources, are all forms of prosocial behavior. Humans are inherently prosocial, and attributes, such as altruism or empathy, that affect decision-making and cooperation, are essential ingredients to more just and positive societies. However, the view of human decision-making prevalent in the design of AI is based on the homo economicus principle, where utility maximization and selfishness are the backbone for modeling behavior in autonomous behavior.
In this talk I will be challenging this view and explore how to create AI (agents) that places prosocial behavior at the core, and while engaged in human settings, cultivates cooperation and fosters people into contributing for the social good.
I will imagine a future where prosocial machines can be a reality and present three case studies from different areas: prosocial robotics; prosocial games, and social simulation. These simple examples illustrate how AI can play an active role by contributing to a kinder society.
Biography
Ana Paiva is a Professor of Computer Science at Instituto Superior Técnico, University of Lisbon investigating the creation of intelligent interactive systems by designing “social agents” that can interact with humans in a natural and social manner. She is also a fellow at the Radcliffe Institute for Advanced Study at Harvard University. Over the years she has addressed this problem by engineering social agents that exhibit specific capabilities, including emotions, personality, culture, non-verbal behavior, empathy, and collaboration, among others. Her more recent research combines methods from artificial intelligence with social modelling to study hybrid societies of humans and machines. In particular she is investigating how to engineer agents that lead to more prosocial and altruistic societies.
She has published extensively and received best paper awards in several conferences, notably, she won the Blue Sky Awards at the AAAI in 2018. She has further advanced the area of artificial intelligence and social agents worldwide, having served for the Global Agenda Council in Artificial Intelligence and Robotics of the World Economic Forum and as a member of the Scientific Advisory Board of Science Europe. She is an EurAI fellow.
Employing Social Media to Improve Mental Health: Pitfalls, Lessons Learned, and the Next Frontier
Time
March 24, 9:00 PM GMT+2 (Local: )
Streaming
https://www.youtube.com/watch?v=pD0Js0-J43Y&list=PLqhXYFYmZ-Ve1ETtb7om8IVSg1DzZO8aj&index=2About
Social media data is being increasingly used to computationally learn about and infer the mental health states of individuals and populations. Despite being touted as a powerful means to shape interventions and impact mental health recovery, little do we understand about the theoretical, domain, and psychometric validity of this novel information source, or its underlying biases, when appropriated to augment conventionally gathered data, such as surveys and verbal self-reports. This talk presents a critical analytic perspective on the pitfalls of social media signals of mental health, especially when they are derived from “proxy” diagnostic indicators, often removed from the real-world context in which they are likely to be used. Then, to overcome these pitfalls, this talk presents results from two case studies, where machine learning algorithms to glean mental health insights from social media were developed in a context-sensitive and human-centered way, in collaboration with domain experts and stakeholders. The first of these case studies, a collaboration with a health provider, focuses on the individual-perspective, and reveals the ability and implications of using social media data of consented schizophrenia patients to forecast relapse and support clinical decision-making. Scaling up to populations, in collaboration with a federal organization and towards influencing public health policy, the second case study seeks to forecast nationwide rates of suicide fatalities using social media signals, in conjunction with health services data. The talk concludes with discussions of the path forward, emphasizing the need for a collaborative, multi-disciplinary research agenda while realizing the potential of social media data and machine learning in mental health – one that incorporates methodological rigor, ethics, and accountability, all at once.
Biography
Munmun De Choudhury is an Associate Professor of Interactive Computing at Georgia Tech. Dr. De Choudhury is best known for laying the foundation of a line of research that develops computational techniques to, responsibly and ethically, employ social media in understanding and improving our mental health. To do this work, she adopts a highly interdisciplinary approach, combining social computing, machine learning, and natural language analysis with insights and theories from the social, behavioral, and health sciences. Dr. De Choudhury has been recognized with the 2021 ACM-W Rising Star Award, 2019 Complex Systems Society – Junior Scientific Award, over a dozen best paper and honorable mention awards from the ACM and AAAI, and extensive coverage in popular press like the New York Times, the NPR, and the BBC. Earlier, Dr. De Choudhury was a faculty associate with the Berkman Klein Center for Internet and Society at Harvard, a postdoc at Microsoft Research, and obtained her PhD in Computer Science from Arizona State University.
Provably Beneficial Artificial Intelligence
Time
March 25, 7:30 PM GMT+2 (Local: )
Streaming
https://www.youtube.com/watch?v=SYqVKrY8XpA&list=PLqhXYFYmZ-Ve1ETtb7om8IVSg1DzZO8aj&index=5About
As AI advances in capabilities and moves into the real world, its potential to benefit humanity seems limitless. Yet we see serious problems including racial and gender bias, manipulation by social media, and an arms race in lethal autonomous weapons. Looking further ahead, Alan Turing predicted the eventual loss of human control over machines that exceed human capabilities. I will argue that Turing was right to express concern but wrong to think that doom is inevitable. Instead, we need to develop a new kind of AI that is provably beneficial to humans.
Biography
Stuart Russell is a Professor of Computer Science at the University of California at Berkeley, holder of the Smith-Zadeh Chair in Engineering, and Director of the Center for Human-Compatible AI. He is a recipient of the IJCAI Computers and Thought Award and held the Chaire Blaise Pascal in Paris. In 2021 he received the OBE from Her Majesty Queen Elizabeth and gave the Reith Lectures. He is an Honorary Fellow of Wadham College, Oxford, an Andrew Carnegie Fellow, and a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. His book ”Artificial Intelligence: A Modern Approach” (with Peter Norvig) is the standard text in AI, used in 1500 universities in 135 countries. His research covers a wide range of topics in artificial intelligence, with a current emphasis on the long-term future of artificial intelligence and its relation to humanity. He has developed a new global seismic monitoring system for the nuclear-test-ban treaty and is currently working to ban lethal autonomous weapons.