Sledovat
Anubrata Das
Anubrata Das
PhD Student, University of Texas at Austin
E-mailová adresa ověřena na: utexas.edu - Domovská stránka
Název
Citace
Citace
Rok
Fairness and Discrimination in Information Access Systems
MD Ekstrand, A Das, R Burke, F Diaz
(FnTIR) Foundations and Trends® in Information Retrieval, 2022 16 ((1-2)), 1-177, 2021
98*2021
FACTS-IR: Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval
A Roegiest, A Lipani, A Beutel, A Olteanu, A Lucic, A Stoica, A Das, ...
35*2019
Fast, accurate, and healthier: Interactive blurring helps moderators reduce exposure to harmful content
A Das, B Dang, M Lease
AAAI HCOMP 2020 8, 33-42, 2020
202020
Fairness in Recommender Systems
F Ekstrand, M.D., Das, A., Burke, R., Diaz
Recommender Systems Handbook, 679–707, 2022
15*2022
The state of human-centered NLP technology for fact-checking
A Das, H Liu, V Kovatchev, M Lease
Information Processing & Management 60 (2), 103219, 2023
132023
ProtoTEx: Explaining Model Decisions with Prototype Tensors
A Das, C Gupta, V Kovatchev, M Lease, JJ Li
ACL 2022, 2022
132022
Interactive information crowdsourcing for disaster management using SMS and Twitter: A research prototype
A Das, N Mallik, S Bandyopadhyay, SD Bit, J Basak
(PerCom Workshops) 2016 IEEE International Conference on Pervasive Computing …, 2016
132016
Predicting trends in the twitter social network: a machine learning approach
A Das, M Roy, S Dutta, S Ghosh, AK Das
Swarm, Evolutionary, and Memetic Computing: 5th International Conference …, 2015
132015
Dataset bias: A case study for visual question answering
A Das, S Anjum, D Gurari
(ASIS&T 2019) Proceedings of the Association for Information Science and …, 2019
92019
A Conceptual Framework for Evaluating Fairness in Search
A Das, M Lease
arXiv preprint arXiv:1907.09328, 2019
62019
The Effects of Interactive AI Design on User Behavior: An Eye-tracking Study of Fact-checking COVID-19 Claims
L Shi, N Bhattacharya, A Das, M Lease, J Gwizdka
CHIIR 2022, 315-320, 2022
52022
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks.
V Kovatchev, T Chatterjee, VS Govindarajan, J Chen, E Choi, G Chronis, ...
(DADC Workshop 2022) Proceedings of the First Workshop on Dynamic …, 2022
32022
The Case for Claim Difficulty Assessment in Automatic Fact Checking
P Singh, A Das, JJ Li, M Lease
arXiv preprint arXiv:2109.09689, 2021
32021
Fairly accurate: Learning optimal accuracy vs. fairness tradeoffs for hate speech detection.
V Kovatchev, S Gupta, A Das, M Lease
arXiv preprint, 2022
22022
CobWeb: A Research Prototype for Exploring User Bias in Political Fact-Checking
A Das, K Mehta, M Lease
arXiv preprint arXiv:1907.03718, 2019
22019
True or false? Cognitive load when reading COVID-19 news headlines: an eye-tracking study
L Shi, N Bhattacharya, A Das, J Gwizdka
CHIIR 2023, 2023
12023
Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI
H Liu, A Das, A Boltz, D Zhou, D Pinaroc, M Lease, MK Lee
arXiv preprint arXiv:2308.07213, 2023
2023
Pareto Solutions vs Dataset Optima: Concepts and Methods for Optimizing Competing Objectives with Constraints in Retrieval.
S Gupta, G Singh, A Das, M Lease
ACM SIGIR ICTIR 2021, 2021
2021
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Články 1–18