3.2k citations · 5.1k across the 137 of their papers we have counts for
145 papers
Referential Uncertainty in Human--AI Collaboration
Christian Poelitz, Finale Doshi-Velez, Siân Lindley
Effective human-AI collaboration requires partners to establish references through interaction, which becomes fragile when descriptions are ambiguous, similar referents compete, or…
"ChatGPT, what am I missing?": Designing AI Workflows around Professional Task Structure to Shape Analytic AI Use
Zilin Ma, Suzi Jazmati, Marco Chimenton +4
General-purpose AI lets users choose what support to request, but leaves them to structure the support a professional task requires. We examine how interactive workflows can embed…
Algorithmic Impact Reveals the Hidden Social Choice Structure of Alignment
Zachary Wojtowicz, Michelle Si, Finale Doshi-Velez +1
When an AI algorithm makes decisions that affect more than one person, aligning it becomes a problem of social choice: how should people's divergent preferences about system behavi…
From Wearable Data to Personalized and Actionable Health Insights
Esther Brown, Karis Moon, Victoria Dean +1
Commercial wearable devices continuously capture rich physiological data (e.g., heart rate, respiration), opening new possibilities for monitoring health conditions, notably around…
Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health
Donna Tjandra, Trenton Chang, Sonali Parbhoo +8
Objective: The growing availability of large-scale observational clinical datasets and challenges in conducting randomized controlled trials have spurred enthusiasm in using causal…
Quantifying Potential Observation Missingness in Inverse Reinforcement Learning
Leo Benac, Abhishek Sharma, Alihan Huyuk +1
Inverse reinforcement learning (IRL), which infers reward functions from demonstrations, is a valuable tool for modeling and understanding decision-making behavior. Many variants o…
Personalized and Context-Aware Transformer Models for Predicting Post-Intervention Physiological Responses from Wearable Sensor Data
Esther Brown, Victoria Dean, Finale Doshi-Velez
Consumer wearables enable continuous measurement of physiological data related to stress and recovery, but turning these streams into actionable, personalized stress-management rec…
A Benchmark for Multi-Party Negotiation Games from Real Negotiation Data
Leo Benac, Jonas Raedler, Zilin Ma +1
Many real-world multi-party negotiations unfold as sequences of binding, action-level commitments rather than a single final outcome, yet this regime remains under-studied in exist…
A Benchmark to Assess Common Ground in Human-AI Collaboration
Christian Poelitz, Finale Doshi-Velez, Siân Lindley
AI is becoming increasingly integrated into everyday life, both in professional work environments and in leisure and entertainment contexts. This integration requires AI to move be…
Federated ADMM from Bayesian Duality
Thomas Möllenhoff, Siddharth Swaroop, Finale Doshi-Velez +1
We propose a new Bayesian approach to generalize the federated Alternating Direction Method of Multipliers (ADMM). We show that the solutions of variational-Bayesian (VB) objective…
Strategically Linked Decisions in Long-Term Planning and Reinforcement Learning
Alihan Hüyük, Finale Doshi-Velez
Long-term planning, as in reinforcement learning (RL), involves finding strategies: actions that collectively work toward a goal rather than individually optimizing their immediate…
Connecting Federated ADMM to Bayes
Siddharth Swaroop, Mohammad Emtiyaz Khan, Finale Doshi-Velez
We provide new connections between two distinct federated learning approaches based on (i) ADMM and (ii) Variational Bayes (VB), and propose new variants by combining their complem…
Diverse Concept Proposals for Concept Bottleneck Models
Katrina Brown, Marton Havasi, Finale Doshi-Velez
Concept bottleneck models are interpretable predictive models that are often used in domains where model trust is a key priority, such as healthcare. They identify a small number o…
Shaping AI's Impact on Billions of Lives
Mariano-Florentino Cuéllar, Jeff Dean, Finale Doshi-Velez +6
Artificial Intelligence (AI), like any transformative technology, has the potential to be a double-edged sword, leading either toward significant advancements or detrimental outcom…
Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning
Inko Bovenzi, Adi Carmel, Michael Hu +5
In aims to uncover insights into medical decision-making embedded within observational data from clinical settings, we present a novel application of Inverse Reinforcement Learning…
Bayesian Inverse Transition Learning: Learning Dynamics From Near-Optimal Trajectories
Leo Benac, Abhishek Sharma, Sonali Parbhoo +1
We consider the problem of estimating the transition dynamics from near-optimal expert trajectories in the context of offline model-based reinforcement learning. We develop a…
Decision-Point Guided Safe Policy Improvement
Abhishek Sharma, Leo Benac, Sonali Parbhoo +1
Within batch reinforcement learning, safe policy improvement (SPI) seeks to ensure that the learnt policy performs at least as well as the behavior policy that generated the datase…
Transparent Trade-offs between Properties of Explanations
Hiwot Belay Tadesse, Alihan Hüyük, Yaniv Yacoby +2
When explaining black-box machine learning models, it's often important for explanations to have certain desirable properties. Most existing methods `encourage' desirable propertie…
Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills
Zana Buçinca, Siddharth Swaroop, Amanda E. Paluch +2
People's decision-making abilities often fail to improve or may even erode when they rely on AI for decision-support, even when the AI provides informative explanations. We argue t…
Inverse Reinforcement Learning with Multiple Planning Horizons
Jiayu Yao, Weiwei Pan, Finale Doshi-Velez +1
In this work, we study an inverse reinforcement learning (IRL) problem where the experts are planning under a shared reward function but with different, unknown planning horizons.…
A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial
Anna L. Trella, Kelly W. Zhang, Hinal Jajal +4
Dental disease is a prevalent chronic condition associated with substantial financial burden, personal suffering, and increased risk of systemic diseases. Despite widespread recomm…
Effective Monitoring of Online Decision-Making Algorithms in Digital Intervention Implementation
Anna L. Trella, Susobhan Ghosh, Erin E. Bonar +10
Online AI decision-making algorithms are increasingly used by digital interventions to dynamically personalize treatment to individuals. These algorithms determine, in real-time, t…
Prompting the Unknown: Understanding Response Uncertainty in Large Language Models
Ze Yu Zhang, Arun Verma, Finale Doshi-Velez +1
Large language models (LLMs) are widely used in decision-making across diverse domains. Ensuring the generation of safe and reliable responses is critical for the effective deploym…
Towards Integrating Personal Knowledge into Test-Time Predictions
Isaac Lage, Sonali Parbhoo, Finale Doshi-Velez
Machine learning (ML) models can make decisions based on large amounts of data, but they can be missing personal knowledge available to human users about whom predictions are made.…