8 citations · 8 across the 3 of their papers we have counts for
4 papers
When Should AI Decide, Defer, and Deviate? Responsible Automation for Deceased Donor Kidney Allocation
Madeleine Pollack, Thomas Daillak, Mehmet Toner +3
We ask when an AI trained on historical decisions should be allowed to recommend decisions, defer to human experts, or in other cases, flag concerning patterns in data and deviate…
Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning
Cheol Woo Kim, Jai Moondra, Shresth Verma +4
In many real-world applications of reinforcement learning (RL), deployed policies have varied impacts on different stakeholders, creating challenges in reaching consensus on how to…
The implications of state aggregation in deteriorating Markov Decision Processes with optimal threshold policies
Madeleine Pollack, Lauren N. Steimle
Markov Decision Processes (MDPs) are mathematical models of sequential decision-making under uncertainty that have found applications in healthcare, manufacturing, logistics, and o…
Equitably allocating wildfire resilience investments for power grids: The curse of aggregation and vulnerability indices
Madeleine Pollack, Ryan Piansky, Swati Gupta +1
Social vulnerability indices have increased traction for guiding infrastructure investment decisions to prioritize communities that need these investments most. One such plan is th…