Pranav A

We demonstrate the ineffectiveness of ReID datasets that are being deployed in surveillance pipelines.

We audited marketing materials of 100 startups in Germany and discussed the perception of exaggerated claims (AI-washing) with experts and the public.

An early account of my doctoral dissertation on critiques of current policymaking related to surveillance-based AI.

Content filtering systems disproportionately remove mentions of marginalised groups while failing to catch genuinely harmful material, creating a form of epistemic erasure in language model training and deployment.

This mixed-methods paper surveys and interviews NLP researchers about ACL's anonymity policy removal and quantitatively analyses how preprint visibility and author recognition bias review scores and quality across institutional tiers.

Having proactive name-change policies leads to more inclusive publishing with better citation quality.

Understanding the impact of AI requires not only technical expertise but also listening to the communities affected by these technologies.

We introduce Glitter, an English-German benchmark featuring extended passages with professional translations implementing three gender-fair alternatives. Our experiments reveal significant limitations in state-of-the-art language models, which default to masculine generics and rarely produce gender-fair translations even when explicitly instructed.

We expand Yann LeCun's 'cake that is intelligence' analogy to the full lifecycle of AI systems, from sourcing data to evaluation and distribution. We describe each step's social ramifications and provide actionable recommendations for increased participation in AI discourse.

Empirical comparisons of character-based models with word-based models on common Chinese semantic benchmarks.

A case study of Queer in AI's community-led participatory design, offering lessons on decentralisation, building community aid, empowering marginalised groups, and critiquing poor participatory practices.

The paper contributes a contextual subword segmentation method along with benchmark datasets that outperform previous Chinese character conversion approaches by 6 points in accuracy, especially for code-mixing and named entities.

The paper contributes information retrieval ranking functions with heuristics like positional tokenisation and graphical error modelling to the problem of cognate detection.