Ali is a postdoctoral researcher in responsible AI at the Institute for Experiential AI. His work focuses on building methods to measure the adverse outcomes of machine learning systems and to mitigate these effects. Currently, he is interested in empirically measuring the adverse effects—such as toxicity, hallucinations, and privacy leaks—of large language models, as well as finetuning methods to curtail these effects. He is broadly interested in empowering machine learning practitioners with tools to evaluate and monitor models before they harm users.

He has a doctorate in computer science from the Khoury College of Computer Sciences at Northeastern University. His doctoral work measured the harms of Facebook’s advertising system through algorithm auditing—documenting gender and race disparities in employment ads, selective exposure and cost differentiation for political ads, and overexposure to clickbait ads for older adults. He is a recipient of an honorable mention and a diversity and inclusion award at the ACM Conference on Computer Supported Cooperative Work (CSCW), as well as an outstanding graduate student award at Northeastern. His work has been covered in popular press, including Wired, MIT Technology Review, CNBC, and The Intercept; and motivated a federal lawsuit against Facebook’s ad personalization technology.