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Welcome to our Workshop on Responsible Data!

The development of large-scale datasets has been essential to the progress of machine learning and artificial intelligence. However, many of these datasets are not inclusive or diverse - particularly computer vision datasets, which can lead to biased models and algorithms. This workshop will bring together practitioners and researchers to discuss the challenges and opportunities of building more responsible datasets.

The workshop will cover a range of topics, including:

Post workshop, we plan to write a white paper summarizing the round table discussions and opinions from experts in the field (with necessary permissions). We will also follow through with making a community space on discord (or similar platform) to continue the community building and collaboration post-workshop.

Important Dates

Submission Deadline March 31, 2024 April 12, 2024
Final Decisions April 22, 2024 April 30, 2024
Workshop Date June 18, 2024  


The following schedule is tentative and will be confirmed closer to the workshop:

Time Topic Speaker(s)/Presenter(s)
8:30-8:45 Opening Remarks Dr. Candice Schumann
8:45-9:15 Keynote Benchmarking models in a changing world: geospatial and temporal distribution shifts, diverse end users with conflicting priorities, and heterogeneously sampled data across modalities Dr. Sara Beery
9:15-9:40 Rapid Fire Talks 1 See Extended Abstracts Session 1
9:45-10:15 Poster Session 1 See Extended Abstracts Session 1
10:15-10:45 Coffee Break  
10:45-11:45 Round Table Discussion 1  
11:45-13:00 Lunch Break  
13:00-13:30 Keynote Mapping the Computer Vision Surveillance and Weapons Pipeline Dr. William Agnew
13:30-14:15 Round Table Discussion 2  
14:15-14:40 Rapid Fire Talks 2 See Extended Abstracts Session 2
14:45-15:15 Poster Session 2 See Extended Abstracts Session 2
15:15-15:45 Coffee Break  
15:45-16:45 Panel Discussion Moderator: Susanna Ricco

Noa Franko-Ohana,
Dr. Sven Cattell,
Dr. Morgan Klaus Scheuerman,
Emily McReynolds
16:45-17:15 Closing Remarks Dr. Caner Hazirbas

Extended Abstracts

Session 1

Session 2

Keynote Speakers

Sara Beery
Sara Beery (She/Her)
Assistant Professor
William Agnew
William Agnew (He/They)
CBI Postdoc Fellow
Dr. Sara Beery is the Homer A. Burnell Career Development Professor in the MIT Faculty of Artificial Intelligence and Decision-Making. She was previously a visiting researcher at Google, working on large-scale urban forest monitoring as part of the Auto Arborist project. She received her PhD in Computing and Mathematical Sciences at Caltech in 2022, where she was advised by Pietro Perona and awarded the Amori Doctoral Prize for her thesis. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including geospatial and temporal domain shift, learning from imperfect data, fine-grained categories, and long-tailed distributions. She partners with industry, nongovernmental organizations, and government agencies to deploy her methods in the wild worldwide. She works toward increasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education, and has founded the AI for Conservation slack community, serves as the Biodiversity Community Lead for Climate Change AI, and founded and directs the Summer Workshop on Computer Vision Methods for Ecology.

Dr. William Agnew is a CBI postdoc fellow at CMU. William received his Ph.D. from University of Washington with Sidd Srinivasa, where he worked on AI ethics, critical AI, and robotics. William also helped found Queer in AI. William is interested in developing and sharing tools and ideas that go beyond participatory design and allow marginalized individuals and communities to own and meaningfully control their data and models derived from that data. Building on ideas from usable security/privacy, usage licenses, and indigenous data sovereignty, William wants to contribute to data and AI futures where individuals and communities know where their data is and can remove, add, or change their data in different datasets.


Noa Franko-Ohana
Noa Franko-Ohana
VP Partnerships
Sven Cattell
Sven Cattell (He/Him)
Founder of AI Village
Morgan Klaus Scheuerman
Morgan Klaus Scheuerman (He/Him)
Postdoctoral Associate
CU Boulder
Emily McReynolds
Emily McReynolds (She/Her)
Lead AI Strategist
Noa Franko-Ohana is a seasoned technology professional with a background in R&D, Product Management and innovation boasting 2 decades of leadership in startups and enterprises such as IBM, Microsoft, Seagate and more. In her current role as VP Partnerships at, Noa has passionately dedicated efforts to bridging the gap between artificial intelligence and human intuition. At, Noa leads the developments of generative AI evaluation solutions with focusing on ethical solutions, utilizing global, diverse human guidance for evaluating and training ML models. Noa's experience at Seagate, IBM and Microsoft further solidified her commitment to responsible AI, where she led programs that supported startup engagement, ethical technology advocacy, and the development of AI solutions that benefit society.

Sven Cattell founded the AI Village in 2018 and has been running it ever since. He was the principal organizer of AIV’s Generative Red Team at DEFCON 31. Sven is also the founder of, a startup focused on the security and integrity of datasets and the AI they build. He was previously a senior data scientist at Elastic where he built the malware model training pipeline. He has a PhD in Algebraic Topology, and a postdoc in geometric machine learning where he focused on anomaly and novelty detection.

Morgan Klaus Scheuerman is a research scientist on Sony AI's AI Ethics team and a visiting scholar in Information Science at University of Colorado Boulder. He received his PhD from University of Colorado Boulder, where he was a Microsoft PhD Research Fellow. Morgan broadly focuses on mitigating technical harms, particularly in the context of AI development and deployment. Much of his work has examined how computer vision systems embed specific values that disempower historically marginalized groups. He publishes at top-tier research venues like CSCW, FAccT, CHI, and Big Data & Society. His work has received multiple best paper awards, honorable mentions, and diversity and inclusion awards.

Emily McReynolds (She/Her) has worked in data protection, machine learning & AI, across academia, civil society, and in the tech industry. In previous roles, she led partnerships with civil society & industry engagement on responsible AI at Meta, and created end-to-end data strategy for ML development at Microsoft. With a passion for translating complex technical concepts into understandable sound bites, she has spearheaded a number of tech explanation projects including AI System Cards, a resource for understanding how AI works in different contexts. She was the founding program director for the University of Washington’s Tech Policy Lab, an interdisciplinary collaboration across the Computer Science, Information, and Law schools. She started coding in the time of HTML and taught people to use computers back when we used floppy disks.


Candice Schumann
Candice Schumann (They/She)
Research Engineer
Google Research
Caner Hazirbas
Caner Hazirbas (He/Him)
Research Scientist
Meta AI
Olga Russakovsky
Olga Russakovsky (She/Her)
Associate Professor, CS
Vikram V. Ramaswamy
Vikram V. Ramaswamy (He/They)
Lecturer, CS
Jerone Andrews
Jerone Andrews (He/Him)
Research Scientist
Sony AI
Alice Xiang
Alice Xiang (She/Her)
Global Head of AI Ethics
Sony AI
Susanna Ricco
Susanna Ricco (She/Her)
Research Engineer
Google Research
Courtney Heldreth
Courtney Heldreth (She/Her)
UX Researcher
Google Research
Biao Wang
Biao Wang (He/Him)
Associate Product Manager
Google Research
Cristian Canton Ferrer
Cristian Canton Ferrer (He/Him)
Head of GenAI Trust & Safety
Meta AI
Jess Holbrook
Jess Holbrook (He/Him)
Director and Principal Researcher, GenAI
Meta AI


Contact the organizers at

Call for Papers

Authors are invited to submit relevent research (including work in progress, novel perspectives, etc.) as extended abstracts for the poster session and workshop discussion. Please see relevent topics above. Accepted abstracts will be presented at the poster session, and will not be included in the printed proceedings of the workshop.

The extended abstract can be at most 4 pages long in CVPR format, not including references. Authors may supply supplementary material, however, reviewers will not be required to read this material. Reviews will be double blind. The submission deadline is March 31, 2024.

Submit your extended abstracts through OpenReview.