Alicia Parrish

Orcid: 0000-0002-1054-0516

According to our database1, Alicia Parrish authored at least 39 papers between 2019 and 2026.

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Bibliography

2026
Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions.
CoRR, February, 2026

Decoding Safety Feedback from Diverse Raters: A Data-driven Lens on Responsiveness to Severity.
Trans. Mach. Learn. Res., 2026

Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South.
Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency, 2026

Bridging the Scale Gap: Augmenting Human Red-Teaming to Uncover Latent Risks in T2I Models.
Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency, 2026

2025
Risk Management for Mitigating Benchmark Failure Modes: BenchRisk.
CoRR, October, 2025

From Seed to Harvest: Augmenting Human Creativity with AI for Red-teaming Text-to-Image Models.
CoRR, July, 2025

"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives.
CoRR, July, 2025

AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons.
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CoRR, March, 2025

Nuanced Safety for Generative AI: How Demographics Shape Responsiveness to Severity.
CoRR, March, 2025

MSTS: A Multimodal Safety Test Suite for Vision-Language Models.
CoRR, January, 2025

Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Risk Management for Mitigating Benchmark Failure Modes: BenchRisk.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

2024
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs.
Trans. Mach. Learn. Res., 2024

DMLR: Data-centric Machine Learning Research - Past, Present and Future.
J. Data-centric Mach. Learn. Res., 2024

Insights on Disagreement Patterns in Multimodal Safety Perception across Diverse Rater Groups.
CoRR, 2024

Introducing v0.5 of the AI Safety Benchmark from MLCommons.
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CoRR, 2024

A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models.
CoRR, 2024

Is a picture of a bird a bird? A mixed-methods approach to understanding diverse human perspectives and ambiguity in machine vision models.
Proceedings of the 3rd Workshop on Perspectivist Approaches to NLP, 2024

Intersectionality in AI Safety: Using Multilevel Models to Understand Diverse Perceptions of Safety in Conversational AI.
Proceedings of the 3rd Workshop on Perspectivist Approaches to NLP, 2024

GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives.
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation.
Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

2023
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.
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Trans. Mach. Learn. Res., 2023

Inverse Scaling: When Bigger Isn't Better.
Trans. Mach. Learn. Res., 2023

A Framework to Assess (Dis)agreement Among Diverse Rater Groups.
CoRR, 2023

"Is a picture of a bird a bird": Policy recommendations for dealing with ambiguity in machine vision models.
CoRR, 2023

Intersectionality in Conversational AI Safety: How Bayesian Multilevel Models Help Understand Diverse Perceptions of Safety.
CoRR, 2023

Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models.
CoRR, 2023


DICES Dataset: Diversity in Conversational AI Evaluation for Safety.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

What Do NLP Researchers Believe? Results of the NLP Community Metasurvey.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions.
CoRR, 2022

Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions.
CoRR, 2022

QuALITY: Question Answering with Long Input Texts, Yes!
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

BBQ: A hand-built bias benchmark for question answering.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, 2022

2021
Does Putting a Linguist in the Loop Improve NLU Data Collection?
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

NOPE: A Corpus of Naturally-Occurring Presuppositions in English.
Proceedings of the 25th Conference on Computational Natural Language Learning, 2021

2020
Erratum: "BLiMP: The Benchmark of Linguistic Minimal Pairs for English".
Trans. Assoc. Comput. Linguistics, 2020

BLiMP: The Benchmark of Linguistic Minimal Pairs for English.
Trans. Assoc. Comput. Linguistics, 2020

2019
Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs.
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019


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