Search Results for author:
Found 5 papers, 4 papers with code
Decision Theoretic Foundations for Experiments Evaluating Human Decisions
We argue that to attribute loss in human performance to forms of bias, an experiment must provide participants with the information that a rational agent would need to identify the normative decision.
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Machine learning (ML) interpretability techniques can reveal undesirable patterns in data that models exploit to make predictions--potentially causing harms once deployed.
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Recent strides in interpretable machine learning (ML) research reveal that models exploit undesirable patterns in the data to make predictions, which potentially causes harms in deployment.
Visual Reasoning Strategies for Effect Size Judgments and Decisions
We also see that visualization designs that support the least biased effect size estimation do not support the best decision-making, suggesting that a chart user's sense of effect size may not necessarily be identical when they use the same information for different tasks.
Human-Computer Interaction
Boba: Authoring and Visualizing Multiverse Analyses
Multiverse analysis is an approach to data analysis in which all "reasonable" analytic decisions are evaluated in parallel and interpreted collectively, in order to foster robustness and transparency.
Human-Computer Interaction
