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Found 11 papers, 2 papers with code
A Statistical Framework for Measuring AI Reliance
We argue that the current definition of appropriate reliance used in such research lacks formal statistical grounding and can lead to contradictions.
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.
Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling
As deep neural networks are more commonly deployed in high-stakes domains, their black-box nature makes uncertainty quantification challenging.
Pre-registration for Predictive Modeling
Amid rising concerns of reproducibility and generalizability in predictive modeling, we explore the possibility and potential benefits of introducing pre-registration to the field.
Artificial Intelligence and Aesthetic Judgment
In this essay, we focus on an unresolved tension when we bring this dilemma to bear in the context of generative AI: are we looking for proof that generated media reflects something about the conditions that created it or some eternal human essence?
REFORMS: Reporting Standards for Machine Learning Based Science
Machine learning (ML) methods are proliferating in scientific research.
Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research
Visualization for machine learning (VIS4ML) research aims to help experts apply their prior knowledge to develop, understand, and improve the performance of machine learning models.
The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning
We conclude by discussing risks that arise when sources of errors are misdiagnosed and the need to acknowledge the role of human inductive biases in learning and reform.
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
Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs
As the use of machine learning (ML) models in product development and data-driven decision-making processes became pervasive in many domains, people's focus on building a well-performing model has increasingly shifted to understanding how their model works.
