AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons

no code implementations • 19 Feb 2025 • Shaona Ghosh, Heather Frase, Adina Williams, Sarah Luger, Paul Röttger, Fazl Barez, Sean McGregor, Kenneth Fricklas, Mala Kumar, Quentin Feuillade--Montixi, Kurt Bollacker, Felix Friedrich, Ryan Tsang, Bertie Vidgen, Alicia Parrish, Chris Knotz, Eleonora Presani, Jonathan Bennion, Marisa Ferrara Boston, Mike Kuniavsky, Wiebke Hutiri, James Ezick, Malek Ben Salem, Rajat Sahay, Sujata Goswami, Usman Gohar, Ben Huang, Supheakmungkol Sarin, Elie Alhajjar, Canyu Chen, Roman Eng, Kashyap Ramanandula Manjusha, Virendra Mehta, Eileen Long, Murali Emani, Natan Vidra, Benjamin Rukundo, Abolfazl Shahbazi, Kongtao Chen, Rajat Ghosh, Vithursan Thangarasa, Pierre Peigné, Abhinav Singh, Max Bartolo, Satyapriya Krishna, Mubashara Akhtar, Rafael Gold, Cody Coleman, Luis Oala, Vassil Tashev, Joseph Marvin Imperial, Amy Russ, Sasidhar Kunapuli, Nicolas Miailhe, Julien Delaunay, Bhaktipriya Radharapu, Rajat Shinde, Tuesday, Debojyoti Dutta, Declan Grabb, Ananya Gangavarapu, Saurav Sahay, Agasthya Gangavarapu, Patrick Schramowski, Stephen Singam, Tom David, Xudong Han, Priyanka Mary Mammen, Tarunima Prabhakar, Venelin Kovatchev, Ahmed Ahmed, Kelvin N. Manyeki, Sandeep Madireddy, Foutse khomh, Fedor Zhdanov, Joachim Baumann, Nina Vasan, Xianjun Yang, Carlos Mougn, Jibin Rajan Varghese, Hussain Chinoy, Seshakrishna Jitendar, Manil Maskey, Claire V. Hardgrove, TianHao Li, Aakash Gupta, Emil Joswin, Yifan Mai, Shachi H Kumar, Cigdem Patlak, Kevin Lu, Vincent Alessi, Sree Bhargavi Balija, Chenhe Gu, Robert Sullivan, James Gealy, Matt Lavrisa, James Goel, Peter Mattson, Percy Liang, Joaquin Vanschoren

This work represents a crucial step toward establishing global standards for AI risk and reliability evaluation while acknowledging the need for continued development in areas such as multiturn interactions, multimodal understanding, coverage of additional languages, and emerging hazard categories.

Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML

no code implementations • 17 Nov 2024 • Prakhar Ganesh, Usman Gohar, Lu Cheng, Golnoosh Farnadi

In this work, we show significant variance in fairness achieved by several algorithms and the influence of the learning pipeline on fairness scores.

Benchmarking Fairness +2

CoDefeater: Using LLMs To Find Defeaters in Assurance Cases

1 code implementation • 18 Jul 2024 • Usman Gohar, Michael C. Hunter, Robyn R. Lutz, Myra B. Cohen

Constructing assurance cases is a widely used, and sometimes required, process toward demonstrating that safety-critical systems will operate safely in their planned environment.

Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges

no code implementations • 10 Jun 2024 • Usman Gohar, Zeyu Tang, Jialu Wang, Kun Zhang, Peter L. Spirtes, Yang Liu, Lu Cheng

The widespread integration of Machine Learning systems in daily life, particularly in high-stakes domains, has raised concerns about the fairness implications.

Decision Making Fairness

Introducing v0.5 of the AI Safety Benchmark from MLCommons

1 code implementation • 18 Apr 2024 • Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Max Bartolo, Borhane Blili-Hamelin, Kurt Bollacker, Rishi Bomassani, Marisa Ferrara Boston, Siméon Campos, Kal Chakra, Canyu Chen, Cody Coleman, Zacharie Delpierre Coudert, Leon Derczynski, Debojyoti Dutta, Ian Eisenberg, James Ezick, Heather Frase, Brian Fuller, Ram Gandikota, Agasthya Gangavarapu, Ananya Gangavarapu, James Gealy, Rajat Ghosh, James Goel, Usman Gohar, Sujata Goswami, Scott A. Hale, Wiebke Hutiri, Joseph Marvin Imperial, Surgan Jandial, Nick Judd, Felix Juefei-Xu, Foutse khomh, Bhavya Kailkhura, Hannah Rose Kirk, Kevin Klyman, Chris Knotz, Michael Kuchnik, Shachi H. Kumar, Srijan Kumar, Chris Lengerich, Bo Li, Zeyi Liao, Eileen Peters Long, Victor Lu, Sarah Luger, Yifan Mai, Priyanka Mary Mammen, Kelvin Manyeki, Sean McGregor, Virendra Mehta, Shafee Mohammed, Emanuel Moss, Lama Nachman, Dinesh Jinenhally Naganna, Amin Nikanjam, Besmira Nushi, Luis Oala, Iftach Orr, Alicia Parrish, Cigdem Patlak, William Pietri, Forough Poursabzi-Sangdeh, Eleonora Presani, Fabrizio Puletti, Paul Röttger, Saurav Sahay, Tim Santos, Nino Scherrer, Alice Schoenauer Sebag, Patrick Schramowski, Abolfazl Shahbazi, Vin Sharma, Xudong Shen, Vamsi Sistla, Leonard Tang, Davide Testuggine, Vithursan Thangarasa, Elizabeth Anne Watkins, Rebecca Weiss, Chris Welty, Tyler Wilbers, Adina Williams, Carole-Jean Wu, Poonam Yadav, Xianjun Yang, Yi Zeng, Wenhui Zhang, Fedor Zhdanov, Jiacheng Zhu, Percy Liang, Peter Mattson, Joaquin Vanschoren

We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.

Towards Engineering Fair and Equitable Software Systems for Managing Low-Altitude Airspace Authorizations

1 code implementation • 14 Jan 2024 • Usman Gohar, Michael C. Hunter, Agnieszka Marczak-Czajka, Robyn R. Lutz, Myra B. Cohen, Jane Cleland-Huang

Small Unmanned Aircraft Systems (sUAS) have gained widespread adoption across a diverse range of applications.

Decision Making Fairness +1

Evaluating the Social Impact of Generative AI Systems in Systems and Society

no code implementations • 9 Jun 2023 • Irene Solaiman, Zeerak Talat, William Agnew, Lama Ahmad, Dylan Baker, Su Lin Blodgett, Canyu Chen, Hal Daumé III, Jesse Dodge, Isabella Duan, Ellie Evans, Felix Friedrich, Avijit Ghosh, Usman Gohar, Sara Hooker, Yacine Jernite, Ria Kalluri, Alberto Lusoli, Alina Leidinger, Michelle Lin, Xiuzhu Lin, Sasha Luccioni, Jennifer Mickel, Margaret Mitchell, Jessica Newman, Anaelia Ovalle, Marie-Therese Png, Shubham Singh, Andrew Strait, Lukas Struppek, Arjun Subramonian

Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of evaluating those impacts or for which impacts should be evaluated.

A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges

no code implementations • 11 May 2023 • Usman Gohar, Lu Cheng

The widespread adoption of Machine Learning systems, especially in more decision-critical applications such as criminal sentencing and bank loans, has led to increased concerns about fairness implications.

Fairness

Towards Understanding Fairness and its Composition in Ensemble Machine Learning

1 code implementation • 8 Dec 2022 • Usman Gohar, Sumon Biswas, Hridesh Rajan

Furthermore, studies have shown that hyperparameters influence the fairness of ML models.

Fairness