Search Results for author:
Found 15 papers, 8 papers with code
Whom do Explanations Serve? A Systematic Literature Survey of User Characteristics in Explainable Recommender Systems Evaluation
This paper addresses this gap by surveying 124 papers in which recommender systems explanations were evaluated in user studies.
Aligning Object Detector Bounding Boxes with Human Preference
Our evaluation study shows that object detectors fine-tuned with the asymmetric loss are better aligned with human preference and are preferred over fixed scaling factors.
DWARF: Disease-weighted network for attention map refinement
The interpretability of deep learning is crucial for evaluating the reliability of medical imaging models and reducing the risks of inaccurate patient recommendations.
Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation
By focusing on ``implicitly adversarial'' prompts (those that trigger T2I models to generate unsafe images for non-obvious reasons), we isolate a set of difficult safety issues that human creativity is well-suited to uncover.
Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection
We argue that data collection for AI should be performed in a responsible manner where the quality of the data is thoroughly scrutinized and measured through a systematic set of appropriate metrics.
Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models
To address this need, we introduce the Adversarial Nibbler challenge.
Humans disagree with the IoU for measuring object detector localization error
Our work is the first to evaluate IoU with humans and makes it clear that relying on IoU scores alone to evaluate localization errors might not be sufficient.
DataPerf: Benchmarks for Data-Centric AI Development
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems.
Operationalizing Framing to Support Multiperspective Recommendations of Opinion Pieces
We apply this notion to a re-ranking of topic-relevant recommended lists, to form the basis of a novel viewpoint diversification method.
A Survey of Crowdsourcing in Medical Image Analysis
Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis.
CrowdTruth 2.0: Quality Metrics for Crowdsourcing with Disagreement
However, in many domains, there is ambiguity in the data, as well as a multitude of perspectives of the information examples.
Human-Computer Interaction Social and Information Networks
Crowdsourcing StoryLines: Harnessing the Crowd for Causal Relation Annotation
This paper describes a crowdsourcing experiment on the annotation of plot-like structures in English news articles.
Temporal Information Annotation: Crowd vs. Experts
This paper describes two sets of crowdsourcing experiments on temporal information annotation conducted on two languages, i. e., English and Italian.
Crowdsourcing Salient Information from News and Tweets
On the other hand, machines need to understand the information that is published in online data streams and generate concise and meaningful overviews.
