Search Results for author:
Found 8 papers, 1 papers with code
Power-up! What Can Generative Models Do for Human Computation Workflows?
We identify junctures in typical crowdsourcing workflows at which the introduction of LLMs can play a beneficial role and propose means to augment existing design patterns for crowd work.
Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems
This paper addresses an under-explored problem of whether the Dunning-Kruger Effect (DKE) among people can hinder their appropriate reliance on AI systems.
Using Conversational Artificial Intelligence to Support Children's Search in the Classroom
Further, we discuss preliminary ideas on strategies for evaluation.
Towards Benchmarking the Utility of Explanations for Model Debugging
Post-hoc explanation methods are an important class of approaches that help understand the rationale underlying a trained model's decision.
This Is Not What We Ordered: Exploring Why Biased Search Result Rankings Affect User Attitudes on Debated Topics
To better understand the mechanisms underlying SEME, we present a pre-registered, 5 × 3 factorial user study investigating whether order effects (i. e., users adopting the viewpoint pertaining to higher-ranked documents) can cause SEME.
Dissonance Between Human and Machine Understanding
Are humans consistently better at selecting features that make image recognition more accurate?
Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics
The way pages are ranked in search results influences whether the users of search engines are exposed to more homogeneous, or rather to more diverse viewpoints.
Balancing Novelty and Salience: Adaptive Learning to Rank Entities for Timeline Summarization of High-impact Events
In this work, we present a novel approach for timeline summarization of high-impact events, which uses entities instead of sentences for summarizing the event at each individual point in time.
