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Found 10 papers, 1 papers with code
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.
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.
Supporting Human-AI Collaboration in Auditing LLMs with LLMs
Through the design process we highlight the importance of sensemaking and human-AI communication to leverage complementary strengths of humans and generative models in collaborative auditing.
How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?
In a top-tier computer science conference (NeurIPS 2021) with more than 23, 000 submitting authors and 9, 000 submitted papers, we survey the authors on three questions: (i) their predicted probability of acceptance for each of their papers, (ii) their perceived ranking of their own papers based on scientific contribution, and (iii) the change in their perception about their own papers after seeing the reviews.
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.
A Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML Complementarity
To illustrate how our taxonomy can be used to investigate complementarity, we provide a mathematical aggregation framework to examine enabling conditions for complementarity.
A Large Scale Randomized Controlled Trial on Herding in Peer-Review Discussions
Peer review is the backbone of academia and humans constitute a cornerstone of this process, being responsible for reviewing papers and making the final acceptance/rejection decisions.
Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making
We, then, conduct a second user experiment which shows that our time allocation strategy with explanation can effectively de-anchor the human and improve collaborative performance when the AI model has low confidence and is incorrect.
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions
We also provide testing algorithms and associated sample complexity bounds for the problem of two-sample testing with partial (or total) ranking data. Furthermore, we empirically evaluate our results via extensive simulations as well as two real-world datasets consisting of pairwise comparisons.
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms
Third, we propose an efficient estimator to evaluate the space-filling properties of sample designs in arbitrary dimensions and use it to develop an optimization framework to generate high quality space-filling designs.
