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 | 143* | 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, ... | 46* | 2019 |
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 | 35 | 2023 |
Fairness in Recommender Systems F Ekstrand, M.D., Das, A., Burke, R., Diaz Recommender Systems Handbook, 679–707, 2022 | 30* | 2022 |
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 | 27 | 2020 |
ProtoTEx: Explaining Model Decisions with Prototype Tensors A Das, C Gupta, V Kovatchev, M Lease, JJ Li ACL 2022, 2022 | 16 | 2022 |
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 | 13 | 2016 |
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 | 13 | 2015 |
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 | 9 | 2019 |
A Conceptual Framework for Evaluating Fairness in Search A Das, M Lease arXiv preprint arXiv:1907.09328, 2019 | 7 | 2019 |
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 | 6 | 2022 |
The Case for Claim Difficulty Assessment in Automatic Fact Checking P Singh, A Das, JJ Li, M Lease arXiv preprint arXiv:2109.09689, 2021 | 4 | 2021 |
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 | 3 | 2022 |
CobWeb: A Research Prototype for Exploring User Bias in Political Fact-Checking A Das, K Mehta, M Lease arXiv preprint arXiv:1907.03718, 2019 | 3 | 2019 |
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 | 2 | 2023 |
Fairly accurate: Learning optimal accuracy vs. fairness tradeoffs for hate speech detection. V Kovatchev, S Gupta, A Das, M Lease arXiv preprint, 2022 | 2 | 2022 |
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 |
The Case for Assessing Claim Difficulty in Automatic Fact-Checking P Singh, A Das, JJ Li, M Lease | | |