Arslan Aziz

Dr Arslan Aziz is a researcher working at the intersection of digital platforms, artificial intelligence, and economics. His work uses causal inference and large-scale experimentation to understand how platform design shapes the behaviour of consumers, sellers, and workers. His current research examines two questions raised by generative AI. The first concerns platform governance of AI-generated content: how marketplaces and online communities should moderate, disclose, and design around content that is no longer reliably human-authored. The second concerns AI's impact on human capital development: whether AI tools genuinely enhance human learning and skill formation, or substitute for the effortful practice that builds expertise. This agenda builds on a decade of research into information disclosure and trust online. Dr Aziz's work on rating inflation, incentivised reviews, delivery-time commitments, and the value of consumer tracking has been published thrice in Information Systems Research (FT50/ UTD24 journal/ ABDC A*) and Information Technology & People (ABDC A), and supported by grants from the Social Sciences and Humanities Research Council of Canada. He has presented widely at the top conferences in Information Systems, including the International Conference of Information Systems, Workshop in Information Systems and Economics, Conference on Information Systems and Technology, NBER Summer Institute on Economics of Digitization, MIT Conference on Digital Experimentation, and more. He combines academic research with senior industry practice. Before joining UQ he was a Staff Data Scientist at DoorDash and Meta, where he led experimentation and measurement for multi-billion-dollar business lines, and was previously Assistant Professor of Information Systems at the UBC Sauder School of Business. He holds a PhD from Carnegie Mellon University. Managing AI-generated Content: Generative AI has made it cheap to produce text, images, and reviews that are indistinguishable from human-authored content, undermining the signals that online marketplaces and communities have long relied on to establish trust. Dr Aziz studies how platforms should respond: what disclosure and labelling regimes actually change user behaviour, when moderation and detection are worth their costs, and how governance rules alter the incentives of the sellers, creators, and reviewers subject to them. This work extends his earlier research on rating inflation and incentivised reviews, which showed that well-intentioned platform policies often produce effects their designers did not anticipate. The aim is to give platforms and regulators empirical evidence on which interventions preserve informational quality without suppressing legitimate participation. AI's Impact on Human Capital Development: AI tools can raise immediate task performance while quietly displacing the effortful practice through which people build durable skills, a trade-off that matters most for students and early-career workers. Dr Aziz uses field and laboratory experiments to separate these two effects, asking whether learners who use AI assistance retain and transfer what they have learned once the assistance is removed. His current work examines how the design of a tool changes the answer, comparing systems that supply finished answers with those that scaffold a learner's own reasoning. The findings speak to how educators, employers, and tool designers should structure AI use so that productivity gains today do not come at the cost of capability tomorrow.

[2025] Is prevention better than cure? Effects of cyber risk disclosures on shareholder response to breaches. Cao, Rui, Matta, Moksh, Cavusoglu, Hasan, Aziz, Arslan and Kafaee, Ozum (2025). Is prevention better than cure? Effects of cyber risk disclosures on shareholder response to breaches. Information Systems Research, 37 (2), 842-862. doi: 10.1287/isre.2022.0405 [2024] How do recommendations influence patient satisfaction? Evidence from an online health community. Wang, Hao, Liu, Shan, Gao, Baojun and Aziz, Arslan (2024). How do recommendations influence patient satisfaction? Evidence from an online health community. Information Technology & People, 38 (6), 2381-2412. doi: 10.1108/itp-04-2023-0332 [2024] Strategic expectation setting of delivery time on marketplaces. Xie, Si, Sharma, Si