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“Using healthcare databases to learn what works when no randomized trials exist” Making clinical decisions among several courses of action requires knowledge about their causal effects. Randomized trials are the preferred method to quantify those causal effects. When randomized trials are not available, causal effects are often estimated from observational data. Therefore, causal inference from observational data can be...
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Designing Optimal Voting Rules
A central task in voting is to aggregate the ranked preferences of voters over a set of alternatives (candidates) to select a winning alternative. However, despite centuries of research, the natural question of which voting rule is “best” has remained elusive. A recent approach from computer science offers hope. By proposing a natural quantitative measure of the “efficiency” of a voting rule, called distortion, it allows us to define and seek the most efficient voting rule.
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The key thrust behind the fast emerging area of AI for Social Impact (AISI) has been to apply AI research to address societal challenges. AI has great potential to provide tremendous societal benefits, having been successfully deployed in areas spanning public health, environmental sustainability, education, public welfare, among many others. In AI, we have just recently begun to define this topic as its own area of research, and we have just started...
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Can Machine Learning and Mobile Phone Data Improve the Targeting of Humanitarian Assistance?
Targeting is a central challenge in the administration of anti-poverty programs: given available data, how does one rapidly identify the individuals and families with the greatest need? Here we show that non-traditional “big” data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our analysis compares outcomes – including exclusion errors, total social welfare, and measures of fairness – under different targeting regimes. Relative to...
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The myopia of model centrism
AI models seek to intervene in increasingly higher stakes domains, such as cancer detection and microloan allocation. What is the view of the world that guides AI development in high risk areas, and how does this view regard the complexity of the real world? In this talk, I will present results from my multi-year inquiry into how fundamentals of AI systems---data, expertise, and fairness...
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Will algorithms save our planet and will we regret it when they do?
We live in a time of unprecedented global environmental and ecological change: a warming planet, vanishing biodiversity, overfishing and intensifying ecosystem change from fire to draught to invasive species. Not only are these challenges frequently intertwined, all are closely coupled to social, economic, political components which play out in diverse and unequal ways. At the same time, we suddenly have access to ecological and environmental data at a scale we never imagined, thanks to...
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Title: Trustworthy Human-AI Partnerships
Abstract: Recent advances in AI, Machine learning and Robotics have significantly enhanced the capabilities of machines. Machine intelligence is now able to support human decision making, augment human capabilities, and, in some cases, take over control from humans and act fully autonomously. Machines are becoming more tightly embedded into systems alongside humans, interacting and influencing each other in a number of ways. Such human-AI partnerships are a new form of socio-technical system in which the potential...
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Efficient and targeted COVID-19 border testing via reinforcement learning
Throughout the COVID-19 pandemic, countries relied on a variety of ad-hoc border control protocols to allow for non-essential travel while safeguarding public health: from quarantining all travellers to restricting entry from select nations based on population-level epidemiological metrics such as cases, deaths or testing positivity rates....
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Title: How can AI improve the impact of biodiversity conservation
Abstract: Conservation practitioners have to deal with a multitude of different disciplines. These include subjects such as conservation planning which help identify where conservation should be prioritised, to how to most efficiently tackle illegal activities threatening a site, and social aspects such as how to engage local communities in the conservation of a site or to tackle the demand for products. Conservation scientists have to deal with many data inputs which makes it...
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Title: Algorithmic Information Design: Computability, Learnability and Applicability to Societal Challenges
Abstract: The celebrated field of mechanism design studies how a system designer can design agents' incentives, and consequently their actions, in order to steer their joint decisions towards a desirable outcome. This talk also examines the intervention of agents' actions but through a fundamentally different yet equally important "knob" --- i.e., influencing agents' decisions by designing the available information to each agent. This task,...
Also read about the 2020 Rising Stars Workshop, where 60 of the brightest rising stars in AI for Social Impact (AISI), their research and activities all mentored by senior AISI faculty and researchers.
