Search Results for author: Charvi Rastogi

Found 8 papers, 0 papers with code

Supporting Human-AI Collaboration in Auditing LLMs with LLMs

no code implementations19 Apr 2023 Charvi Rastogi, Marco Tulio Ribeiro, Nicholas King, Harsha Nori, Saleema Amershi

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.

Language Modelling Large Language Model +1

How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?

no code implementations22 Nov 2022 Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, Zhenyu Xue, Hal Daumé III, Emma Pierson, Nihar B. Shah

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.

A Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML Complementarity

no code implementations22 Apr 2022 Charvi Rastogi, Liu Leqi, Kenneth Holstein, Hoda Heidari

To illustrate how our taxonomy can be used to investigate complementarity, we provide a mathematical aggregation framework to examine enabling conditions for complementarity.

Decision Making

A Large Scale Randomized Controlled Trial on Herding in Peer-Review Discussions

no code implementations30 Nov 2020 Ivan Stelmakh, Charvi Rastogi, Nihar B. Shah, Aarti Singh, Hal Daumé III

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.

Decision Making

Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

no code implementations15 Oct 2020 Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, Richard Tomsett

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.

Decision Making

Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions

no code implementations21 Jun 2020 Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah, Aarti Singh

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.

Test Two-sample testing

A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms

no code implementations16 Dec 2017 Bhavya Kailkhura, Jayaraman J. Thiagarajan, Charvi Rastogi, Pramod K. Varshney, Peer-Timo Bremer

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.

Image Reconstruction

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