Portrait of Negar Rostamzadeh

Negar Rostamzadeh

Associate Industry Member
Senior Research Scientist, Google Brain Ethical AI Team

Biography

Negar Rostamzadeh is a Senior Research Scientist at Google Responsible AI team and an Associate Industrial member at Mila - Quebec Artificial Intelligence Institute. Her research primarily focuses on understanding the social implications of machine learning and evaluation systems, as well as developing equitable and fair ML systems.

Negar holds a deep interest in the creative applications of computer vision and their impact on society and artists. She is the founder and program chair of the workshop series, "Computer Vision for Fashion, Art, and Design," as well as "Ethical Considerations in Creative Applications," featured at Computer Vision venues from ECCV 2018 to CVPR 2023.

Before joining Google, Negar worked as a research scientist at Element AI (Service Now), where she specialized in efficient learning from limited data in computer vision and multi-modal problems.

She completed her PhD in 2017 at the University of Trento under the supervision of Prof. Nicu Sebe, focusing on Video Understanding problems. She also spent two years at MILA (2015-2017), working on attention mechanisms in videos, generative models, and video captioning under the guidance of Prof. Aaron Courville. In 2016, she had the opportunity to intern with Google's Machine Intelligence team.

Negar actively contributes to various community engagements within the AI community. She has served as the program chair for the workshop series, "Science meets Engineering of Deep Learning," at ICLR, FAccT, and NeurIPS. Since 2020, she has been a board member of the Montreal AI Symposium, and in 2019, she held the position of Senior Program Chair. Negar is also an Area Chair for Vision Conferences such as CVPR and ICCV, and gave multiple keynotes in various workshops and conferences.

Publications

A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Stephen R. Pfohl
Heather Cole-Lewis
Rory A Sayres
Darlene Neal
Mercy Nyamewaa Asiedu
Awa Dieng
Nenad Tomašev
Qazi Mamunur Rashid
Shekoofeh Azizi
Negar Rostamzadeh
Liam G. McCoy
L. A. Celi
Yun Liu
Mike Schaekermann
Alanna Walton
Alicia Parrish
Chirag Nagpal
Preeti Singh
Akeiylah Dewitt
P. A. Mansfield … (see 10 more)
Sushant Prakash
Katherine Heller
Alan Karthikesalingam
Christopher Semturs
Joelle Barral
Greg C. Corrado
Yossi Matias
Jamila Smith-Loud
Ivor Horn
Karan Singhal
2024-03-18
ArXiv (preprint)
doi.org
arxiv.org
The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa
Mercy Nyamewaa Asiedu
Awa Dieng
Alexander Haykel
Negar Rostamzadeh
Stephen R. Pfohl
Chirag Nagpal
Maria Nagawa
Abigail Oppong
Sanmi Koyejo
Katherine Heller
With growing application of machine learning (ML) technologies in healthcare, there have been calls for developing techniques to understand … (see more)and mitigate biases these systems may exhibit. Fair-ness considerations in the development of ML-based solutions for health have particular implications for Africa, which already faces inequitable power imbalances between the Global North and South.This paper seeks to explore fairness for global health, with Africa as a case study. We conduct a scoping review to propose axes of disparities for fairness consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. We then conduct qualitative research studies with 672 general population study participants and 28 experts inML, health, and policy focused on Africa to obtain corroborative evidence on the proposed axes of disparities. Our analysis focuses on colonialism as the attribute of interest and examines the interplay between artificial intelligence (AI), health, and colonialism. Among the pre-identified attributes, we found that colonial history, country of origin, and national income level were specific axes of disparities that participants believed would cause an AI system to be biased.However, there was also divergence of opinion between experts and general population participants. Whereas experts generally expressed a shared view about the relevance of colonial history for the development and implementation of AI technologies in Africa, the majority of the general population participants surveyed did not think there was a direct link between AI and colonialism. Based on these findings, we provide practical recommendations for developing fairness-aware ML solutions for health in Africa.
2024-03-05
ArXiv (preprint)
doi.org
arxiv.org
The value of standards for health datasets in artificial intelligence-based applications
Anmol Arora
Joseph E. Alderman
Joanne Palmer
Shaswath Ganapathi
Elinor Laws
Melissa D. McCradden
Lauren Oakden-Rayner
Stephen R. Pfohl
Marzyeh Ghassemi
Francis McKay
Darren Treanor
Negar Rostamzadeh
Bilal Mateen
Jacqui Gath
Adewole O. Adebajo
Stephanie Kuku
Rubeta Matin
Katherine Heller
Elizabeth Sapey
Neil J. Sebire … (see 4 more)
Heather Cole-Lewis
Melanie Calvert
Alastair Denniston
Xiaoxuan Liu
2023-10-26
Nature Medicine (published)
doi.org
Breaking Barriers to Creative Expression: Co-Designing and Implementing an Accessible Text-to-Image Interface
Atieh Taheri
Mohammad Izadi
Gururaj Shriram
Negar Rostamzadeh
Shaun Kane
Text-to-image generation models have grown in popularity due to their ability to produce high-quality images from a text prompt. One use for… (see more) this technology is to enable the creation of more accessible art creation software. In this paper, we document the development of an alternative user interface that reduces the typing effort needed to enter image prompts by providing suggestions from a large language model, developed through iterative design and testing within the project team. The results of this testing demonstrate how generative text models can support the accessibility of text-to-image models, enabling users with a range of abilities to create visual art.
2023-09-05
ArXiv (preprint)
doi.org
arxiv.org
Beyond the ML Model: Applying Safety Engineering Frameworks to Text-to-Image Development
Shalaleh Rismani
Renee Shelby
Andrew J Smart
Renelito Delos Santos
AJung Moon
Negar Rostamzadeh
Identifying potential social and ethical risks in emerging machine learning (ML) models and their applications remains challenging. In this … (see more)work, we applied two well-established safety engineering frameworks (FMEA, STPA) to a case study involving text-to-image models at three stages of the ML product development pipeline: data processing, integration of a T2I model with other models, and use. Results of our analysis demonstrate the safety frameworks – both of which are not designed explicitly examine social and ethical risks – can uncover failure and hazards that pose social and ethical risks. We discovered a broad range of failures and hazards (i.e., functional, social, and ethical) by analyzing interactions (i.e., between different ML models in the product, between the ML product and user, and between development teams) and processes (i.e., preparation of training data or workflows for using an ML service/product). Our findings underscore the value and importance of examining beyond an ML model in examining social and ethical risks, especially when we have minimal information about an ML model.
2023-08-29
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
arxiv.org
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Renee Shelby
Shalaleh Rismani
Kathryn Henne
AJung Moon
Negar Rostamzadeh
Paul Nicholas
N'Mah Yilla-Akbari
Jess Gallegos
Andrew J Smart
Emilio Garcia
Gurleen Virk
2023-08-29
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (published)
doi.org
arxiv.org
From Plane Crashes to Algorithmic Harm: Applicability of Safety Engineering Frameworks for Responsible ML
Shalaleh Rismani
Renee Shelby
Andrew J Smart
Edgar Jatho
Joshua A. Kroll
AJung Moon
Negar Rostamzadeh
2023-04-19
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (published)
doi.org
openreview.net
Healthsheet: Development of a Transparency Artifact for Health Datasets
Negar Rostamzadeh
Diana Mincu
Subhrajit Roy
Andrew J Smart
Lauren Wilcox
Mahima Pushkarna
Jessica Schrouff
Razvan Amironesei
Nyalleng Moorosi
Katherine Heller
2022-06-20
2022 ACM Conference on Fairness, Accountability, and Transparency (published)
doi.org
arxiv.org
Bias-inducing geometries: an exactly solvable data model with fairness implications
Stefano Sarao Mannelli
Federica Gerace
Negar Rostamzadeh
Luca Saglietti
2022-05-31
ArXiv (preprint)
arxiv.org
arxiv.org
Healthsheet: Development of a Transparency Artifact for Health Datasets
Negar Rostamzadeh
Diana Mincu
Subhrajit Roy
Andrew J Smart
Lauren Wilcox
Mahima Pushkarna
Jessica Schrouff
Razvan Amironesei
Nyalleng Moorosi
Katherine Heller
Machine learning (ML) approaches have demonstrated promising results in a wide range of healthcare applications. Data plays a crucial role i… (see more)n developing ML-based healthcare systems that directly affect people’s lives. Many of the ethical issues surrounding the use of ML in healthcare stem from structural inequalities underlying the way we collect, use, and handle data. Developing guidelines to improve documentation practices regarding the creation, use, and maintenance of ML healthcare datasets is therefore of critical importance. In this work, we introduce Healthsheet, a contextualized adaptation of the original datasheet questionnaire [22] for health-specific applications. Through a series of semi-structured interviews, we adapt the datasheets for healthcare data documentation. As part of the Healthsheet development process and to understand the obstacles researchers face in creating datasheets, we worked with three publicly-available healthcare datasets as our case studies, each with different types of structured data: Electronic health Records (EHR), clinical trial study data, and smartphone-based performance outcome measures. Our findings from the interviewee study and case studies show 1) that datasheets should be contextualized for healthcare, 2) that despite incentives to adopt accountability practices such as datasheets, there is a lack of consistency in the broader use of these practices 3) how the ML for health community views datasheets and particularly Healthsheets as diagnostic tool to surface the limitations and strength of datasets and 4) the relative importance of different fields in the datasheet to healthcare concerns.
2022-02-26
ArXiv (preprint)
doi.org
arxiv.org
Sociotechnical Harms: Scoping a Taxonomy for Harm Reduction
Renee Shelby
Shalaleh Rismani
Kathryn Henne
AJung Moon
Negar Rostamzadeh
Paul Nicholas
N'mah Fodiatu Yilla
Jess Gallegos
Andrew J Smart
Emilio Garcia
Gurleen Virk
2022-01-01
arXiv.org (preprint)
doi.org