Nowadays, we are witnessing an increasing adoption of Machine Learning (ML) for solving complex real-world problems. However, despite some reports showing that ML models can produce results comparable and even superior to human experts, they are often vulnerable to carefully crafted perturbations and are prone to bias and hallucinations. Ensuring the trustworthiness of software systems enabled by machine learning is a very challenging task. In this talk, I will discuss challenges that we should overcome to build trustworthy ML-enabled systems and present some recent techniques and tools that we have proposed to support their quality assurance. I will also share my views on the role that software engineering can play to improve the trustworthiness of software systems, like ChatGPT, that are based on Generative Large Language models (which are prone to hallucinations).

Bio Foutse Khomh is a Full Professor of Software Engineering at Polytechnique Montréal, a Canada CIFAR AI Chair on Trustworthy Machine Learning Software Systems, and an FRQ-IVADO Research Chair on Software Quality Assurance for Machine Learning Applications. He received a Ph.D. in Software Engineering from the University of Montreal in 2011, with the Award of Excellence. He also received a CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize for 2019. His research interests include software maintenance and evolution, machine learning systems engineering, cloud engineering, and dependable and trustworthy ML/AI. His work has received four ten-year Most Influential Paper (MIP) Awards, and six Best/Distinguished Paper Awards. He also served on the steering committee of SANER (chair), MSR, PROMISE, ICPC (chair), and ICSME (vice-chair). He initiated and co-organized the Software Engineering for Machine Learning Applications (SEMLA) symposium and the RELENG (Release Engineering) workshop series. He is co-founder of the NSERC CREATE SE4AI: A Training Program on the Development, Deployment, and Servicing of Artificial Intelligence-based Software Systems and one of the Principal Investigators of the DEpendable Explainable Learning (DEEL) project. He is also co-founder and chair of the academic committee of the Confiance.ia Quebec initiative on Trustworthy AI. He is on the editorial board of multiple international software engineering journals (e.g., IEEE Software, EMSE, JSEP) and is a Senior Member of IEEE.

Foutse Khomh is a Full Professor, a Canada CIFAR AI Chair, and FRQ-IVADO Research Chair at Polytechnique Montréal, where he heads the SWAT Lab (http://swat.polymtl.ca/). He received a Ph.D. in Software Engineering from the University of Montreal in 2011. His research interests include software maintenance and evolution, cloud engineering, machine learning systems engineering, empirical software engineering, software analytics, and dependable and trustworthy AI/ML. He has published over 180 conferences and journal papers. His work has received four ten-year Most Influential Paper (MIP) Awards, and six Best/Distinguished Paper Awards. He has served on the program committees of several international conferences including ICSE, FSE, ICSM(E), SANER, MSR, ICPC, SCAM, ESEM and has reviewed for top international journals such as SQJ, JSS, EMSE, TSE, and TOSEM. He is program chair for Satellite Events at SANER 2015, program co-chair of SCAM 2015, ICSME 2018, PROMISE 2019, and ICPC 2019, and general chair of ICPC 2018, SCAM 2020, and general co-chair of SANER 2020. He initiated and co-organized the Software Engineering for Machine Learning Applications (SEMLA) symposium. He is one of the organizers of the RELENG workshop series (http://releng.polymtl.ca) and Associate Editor for IEEE Software, EMSE, and JSEP.