35 citations · 68 across the 26 of their papers we have counts for
5 papers · 1 filter
Building Machines that Learn and Think with People
Katherine M. Collins, Ilia Sucholutsky, Umang Bhatt +10
What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and t…
Modulating Language Model Experiences through Frictions
Katherine M. Collins, Valerie Chen, Ilia Sucholutsky +6
Language models are transforming the ways that their users engage with the world. Despite impressive capabilities, over-consumption of language model outputs risks propagating unch…
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…
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…
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…