35 citations · 68 across the 26 of their papers we have counts for
9 papers · 1 filter
AI for Mathematics: A Cognitive Science Perspective
Cedegao E. Zhang, Katherine M. Collins, Adrian Weller +1
Mathematics is one of the most powerful conceptual systems developed and used by the human species. Dreams of automated mathematicians have a storied history in artificial intellig…
Getting aligned on representational alignment
Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller +30
Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we m…
Learning to Receive Help: Intervention-Aware Concept Embedding Models
Mateo Espinosa Zarlenga, Katherine M. Collins, Krishnamurthy Dvijotham +3
Concept Bottleneck Models (CBMs) tackle the opacity of neural architectures by constructing and explaining their predictions using a set of high-level concepts. A special property…
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines
Matthew Barker, Emma Kallina, Dhananjay Ashok +6
Even though machine learning (ML) pipelines affect an increasing array of stakeholders, there is little work on how input from stakeholders is recorded and incorporated. We propose…
The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs
Lance Ying, Katherine M. Collins, Megan Wei +5
Human beings are social creatures. We routinely reason about other agents, and a crucial component of this social reasoning is inferring people's goals as we learn about their acti…
Selective Concept Models: Permitting Stakeholder Customisation at Test-Time
Matthew Barker, Katherine M. Collins, Krishnamurthy Dvijotham +2
Concept-based models perform prediction using a set of concepts that are interpretable to stakeholders. However, such models often involve a fixed, large number of concepts, which…
Evaluating Language Models for Mathematics through Interactions
Katherine M. Collins, Albert Q. Jiang, Simon Frieder +11
There is much excitement about the opportunity to harness the power of large language models (LLMs) when building problem-solving assistants. However, the standard methodology of e…
Learning Personalized Decision Support Policies
Umang Bhatt, Valerie Chen, Katherine M. Collins +4
Individual human decision-makers may benefit from different forms of support to improve decision outcomes, but when each form of support will yield better outcomes? In this work, w…
Human Uncertainty in Concept-Based AI Systems
Katherine M. Collins, Matthew Barker, Mateo Espinosa Zarlenga +6
Placing a human in the loop may abate the risks of deploying AI systems in safety-critical settings (e.g., a clinician working with a medical AI system). However, mitigating risks…