research
∙
07/28/2023
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines
Even though machine learning (ML) pipelines affect an increasing array o...
share
research
∙
06/14/2023
Selective Concept Models: Permitting Stakeholder Customisation at Test-Time
Concept-based models perform prediction using a set of concepts that are...
research
∙
06/02/2023
Evaluating Language Models for Mathematics through Interactions
The standard methodology of evaluating large language models (LLMs) base...
research
∙
04/13/2023
Learning Personalized Decision Support Policies
Individual human decision-makers may benefit from different forms of sup...
research
∙
03/22/2023
Human Uncertainty in Concept-Based AI Systems
Placing a human in the loop may abate the risks of deploying AI systems ...
research
∙
02/20/2023
Harms from Increasingly Agentic Algorithmic Systems
Research in Fairness, Accountability, Transparency, and Ethics (FATE) ha...
research
∙
01/25/2023
Towards Robust Metrics for Concept Representation Evaluation
Recent work on interpretability has focused on concept-based explanation...
research
∙
11/02/2022
Web-based Elicitation of Human Perception on mixup Data
Synthetic data is proliferating on the web and powering many advances in...
research
∙
10/31/2022
Iterative Teaching by Data Hallucination
We consider the problem of iterative machine teaching, where a teacher s...
research
∙
07/06/2022
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
When using medical images for diagnosis, either by clinicians or artific...
research
∙
07/02/2022
Eliciting and Learning with Soft Labels from Every Annotator
The labels used to train machine learning (ML) models are of paramount i...
research
∙
05/13/2022
Perspectives on Incorporating Expert Feedback into Model Updates
Machine learning (ML) practitioners are increasingly tasked with develop...
research
∙
05/03/2022
On the Utility of Prediction Sets in Human-AI Teams
Research on human-AI teams usually provides experts with a single label,...
research
∙
02/02/2022
Approximating Full Conformal Prediction at Scale via Influence Functions
Conformal prediction (CP) is a wrapper around traditional machine learni...
research
∙
12/05/2021
Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates
To interpret uncertainty estimates from differentiable probabilistic mod...
research
∙
07/13/2021
DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement
As the complexity of machine learning (ML) models increases, resulting i...
research
∙
05/10/2021
Do Concept Bottleneck Models Learn as Intended?
Concept bottleneck models map from raw inputs to concepts, and then from...
research
∙
04/13/2021
δ-CLUE: Diverse Sets of Explanations for Uncertainty Estimates
To interpret uncertainty estimates from differentiable probabilistic mod...
research
∙
03/27/2021
A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms
Given that there are a variety of stakeholders involved in, and affected...
research
∙
11/15/2020
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Transparency of algorithmic systems entails exposing system properties t...
research
∙
10/13/2020
On the Fairness of Causal Algorithmic Recourse
While many recent works have studied the problem of algorithmic fairness...
research
∙
07/10/2020
Machine Learning Explainability for External Stakeholders
As machine learning is increasingly deployed in high-stakes contexts aff...
research
∙
06/11/2020
Getting a CLUE: A Method for Explaining Uncertainty Estimates
Both uncertainty estimation and interpretability are important factors f...
research
∙
05/01/2020
Evaluating and Aggregating Feature-based Model Explanations
A feature-based model explanation denotes how much each input feature co...
research
∙
10/24/2019
A Robot's Expressive Language Affects Human Strategy and Perceptions in a Competitive Game
As robots are increasingly endowed with social and communicative capabil...
research
∙
09/13/2019
Explainable Machine Learning in Deployment
Explainable machine learning seeks to provide various stakeholders with ...
research
∙
01/20/2019
NIF: A Framework for Quantifying Neural Information Flow in Deep Networks
In this paper, we present a new approach to interpreting deep learning m...
research
∙
01/20/2019
Towards Aggregating Weighted Feature Attributions
Current approaches for explaining machine learning models fall into two ...
research
∙
06/10/2018
The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting
With the rapid development of robot and other intelligent and autonomous...
research
∙
11/15/2017
Maintaining The Humanity of Our Models
Artificial intelligence and machine learning have been major research in...
research
∙
10/06/2017