On July 24th Adrian K. Yee successfully defended his PhD dissertation titled Signaling, Machine Learning & Democracy: A Theory of Misinformation written under the supervision of professor Brian Baigrie. There is more good news to share: Adrian has just signed a contract to begin a position as Research Assistant Professor at Lingnan University, Department of Philosophy and as faculty member at the Hong Kong Catastrophic Risk Centre starting August 2023. Building off his dissertation focus on the philosophy & social science of misinformation, he will be researching three areas: social and political problems pertaining to artificial intelligence/machine learning, military intelligence analysis, and the economics of information and attention. He has also just had a paper accepted for publication on the intersection of philosophy of medicine and economic methodology entitled 'Medical Epistemology Meets Economics: How (Not) to GRADE Universal Basic Income Research' (Journal of Economic Methodology, abstract below).
Congratulations Adrian on earning your PhD, and on the other two wonderful achievements!
There have recently been novel applications of medical systematic review guidelines to economic policy interventions which contain controversial methodological assumptions that require further scrutiny. A landmark 2017 Cochrane review of unconditional cash transfer (UCT) studies, based on the Grading of Recommendations Assessment, Development and Evaluation (GRADE), exemplifies both the possibilities and limitations of applying medical systematic review guidelines to UCT and universal basic income (UBI) studies. Recognizing the need to upgrade GRADE to incorporate the differences between medical and policy interventions, the GRADE Public Health Project Group (PHPG) was convened to enumerate and address these methodological challenges. However, in light of our analysis of additional methodological challenges that arise for UCT and UBI studies, we argue that the adaptation of medical systematic review guidelines to economic methodology is far from straightforward and is in fact more challenging than claimed by the PHPG.