Machine Learning in the Wild: Early Evidence of Non-Compliant ML-Automation in Open-Source Software
This program is tentative and subject to change.
The increasing availability of Machine Learning (ML) models, particularly foundation models, enables their use across a range of downstream applications, from scenarios with missing data to safety-critical contexts. This, in principle, may contravene not only the models’ terms of use but also governmental principles and regulations. This paper presents a preliminary investigation into the use of ML models by 173 open-source projects on GitHub, spanning 16 application areas. We evaluate whether models are used to make decisions, the scope of these decisions, and whether any post-processing measures are taken to reduce the risks inherent in fully autonomous systems. Lastly, we investigate the models’ compliance with established terms of use. This study lays the groundwork for defining guidelines for developers and creating analysis tools that automatically identify potential regulatory violations in the use of ML models in software systems.
This program is tentative and subject to change.
Thu 9 JulDisplayed time zone: Eastern Time (US & Canada) change
14:00 - 15:30 | SE and AI 3Tool Demonstrations / Ideas, Visions and Reflections / Industry Papers / Journal-First Paper / Research Papers at MB 3.210 | ||
14:00 20mTalk | Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions Journal-First Paper Xinyi Hou Huazhong University of Science and Technology, Yanjie Zhao Huazhong University of Science and Technology, Shenao Wang Huazhong University of Science and Technology, Haoyu Wang Huazhong University of Science and Technology | ||
14:20 20mTalk | Human-aligned AI Model Cards with Weighted Hierarchy Architecture Industry Papers Pengyue Yang The University of Sydney, Haolin Jin The University of Sydney, Qingwen Zeng The University of Sydney, Jiawen Wen The University of Sydney, Harry Rao Bytedance, Huaming Chen The University of Sydney | ||
14:40 10mTalk | Machine Learning in the Wild: Early Evidence of Non-Compliant ML-Automation in Open-Source Software Ideas, Visions and Reflections Zohaib Arshid University of Sannio, Italy, Daniele Bifolco University of Sannio, Fiorella Zampetti University of Sannio, Italy, Massimiliano Di Penta University of Sannio, Italy | ||
14:50 20mTalk | SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents Journal-First Paper Amirhossein Zolfagharian University of Ottawa - School of Electrical Engineering & Computer Science (EECS), Manel Abdellatif École de Technologie Supérieure, Lionel Briand University of Ottawa, Canada; Lero centre, University of Limerick, Ireland, Ramesh S | ||
15:10 10mTalk | DevGen: Automated Generation of Virtual Device Models for Kernel Drivers via Large Language Models Ideas, Visions and Reflections Mingyu Wang Xidian University, Bin Yu Xidian University, Wenjian Lu Xidian University, kefeng gao Xidian University, Zhi Wang Xidian University, Cheng Wen Xidian University, Xu Lu Xidian University, Cong Tian Xidian University | ||
15:20 10mTalk | Panther: Faster and Cheaper Computations with Randomized Numerical Linear Algebra Tool Demonstrations Fahd Seddik University of British Columbia, Abdulrahman Elbedewy University of Texas at Austin, Gaser Elmasry Cairo University, Mohamed Abdelmoniem Noon, Yahia Zakaria Cairo University | ||