Search Results for author: Anubrata Das

Found 11 papers, 4 papers with code

Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI

no code implementations14 Aug 2023 Houjiang Liu, Anubrata Das, Alexander Boltz, Didi Zhou, Daisy Pinaroc, Matthew Lease, Min Kyung Lee

While many Natural Language Processing (NLP) techniques have been proposed for fact-checking, both academic research and fact-checking organizations report limited adoption of such NLP work due to poor alignment with fact-checker practices, values, and needs.

Fact Checking Misinformation

The State of Human-centered NLP Technology for Fact-checking

no code implementations8 Jan 2023 Anubrata Das, Houjiang Liu, Venelin Kovatchev, Matthew Lease

We recommend that future research include collaboration with fact-checker stakeholders early on in NLP research, as well as incorporation of human-centered design practices in model development, in order to further guide technology development for human use and practical adoption.

Explainable Models Fact Checking +1

Fairly Accurate: Learning Optimal Accuracy vs. Fairness Tradeoffs for Hate Speech Detection

no code implementations15 Apr 2022 Venelin Kovatchev, Soumyajit Gupta, Anubrata Das, Matthew Lease

In this work, we first introduce a differentiable measure that enables direct optimization of group fairness (specifically, balancing accuracy across groups) in model training.

Fairness Hate Speech Detection

The Effects of Interactive AI Design on User Behavior: An Eye-tracking Study of Fact-checking COVID-19 Claims

1 code implementation17 Feb 2022 Li Shi, Nilavra Bhattacharya, Anubrata Das, Matthew Lease, Jacek Gwidzka

We conducted a lab-based eye-tracking study to investigate how the interactivity of an AI-powered fact-checking system affects user interactions, such as dwell time, attention, and mental resources involved in using the system.

Fact Checking

The Case for Claim Difficulty Assessment in Automatic Fact Checking

no code implementations20 Sep 2021 Prakhar Singh, Anubrata Das, Junyi Jessy Li, Matthew Lease

Fact-checking is the process of evaluating the veracity of claims (i. e., purported facts).

Fact Checking

Fairness in Information Access Systems

no code implementations12 May 2021 Michael D. Ekstrand, Anubrata Das, Robin Burke, Fernando Diaz

Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems.

Fairness Information Retrieval +1

A Conceptual Framework for Evaluating Fairness in Search

1 code implementation22 Jul 2019 Anubrata Das, Matthew Lease

While search efficacy has been evaluated traditionally on the basis of result relevance, fairness of search has attracted recent attention.

Fairness

CobWeb: A Research Prototype for Exploring User Bias in Political Fact-Checking

1 code implementation8 Jul 2019 Anubrata Das, Kunjan Mehta, Matthew Lease

The effect of user bias in fact-checking has not been explored extensively from a user-experience perspective.

Fact Checking

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