11 citations · 19 across the 8 of their papers we have counts for
8 papers
Enduring Disparities in the Workplace: A Pilot Study in the AI Community
Yunusa Simpa Abdulsalam, Siobhan Mackenzie Hall, Ana Quintero-Ossa +6
In efforts toward achieving responsible artificial intelligence (AI), fostering a culture of workplace transparency, diversity, and inclusion can breed innovation, trust, and emplo…
SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence era
Elizaveta Semenova, Alisa Sheinkman, Timothy James Hitge +2
Surrogate models are widely used to approximate complex systems across science and engineering to reduce computational costs. Despite their widespread adoption, the field lacks sta…
The Human Labour of Data Work: Capturing Cultural Diversity through World Wide Dishes
Siobhan Mackenzie Hall, Samantha Dalal, Raesetje Sefala +8
This paper provides guidance for building and maintaining infrastructure for participatory AI efforts by sharing reflections on building World Wide Dishes (WWD), a bottom-up, commu…
Provocation: Who benefits from "inclusion" in Generative AI?
Samantha Dalal, Siobhan Mackenzie Hall, Nari Johnson
The demands for accurate and representative generative AI systems means there is an increased demand on participatory evaluation structures. While these participatory structures ar…
The World Wide recipe: A community-centred framework for fine-grained data collection and regional bias operationalisation
Jabez Magomere, Shu Ishida, Tejumade Afonja +11
We introduce the World Wide recipe, which sets forth a framework for culturally aware and participatory data collection, and the resultant regionally diverse World Wide Dishes eval…
VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution
Siobhan Mackenzie Hall, Fernanda Gonçalves Abrantes, Hanwen Zhu +3
We introduce VisoGender, a novel dataset for benchmarking gender bias in vision-language models. We focus on occupation-related biases within a hegemonic system of binary gender, i…
Balancing the Picture: Debiasing Vision-Language Datasets with Synthetic Contrast Sets
Brandon Smith, Miguel Farinha, Siobhan Mackenzie Hall +3
Vision-language models are growing in popularity and public visibility to generate, edit, and caption images at scale; but their outputs can perpetuate and amplify societal biases…
A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning
Hugo Berg, Siobhan Mackenzie Hall, Yash Bhalgat +4
Vision-language models can encode societal biases and stereotypes, but there are challenges to measuring and mitigating these multimodal harms due to lacking measurement robustness…