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Mui Research Group @ ASDRP

Computer Science for the Next Generation

Algorithmic Bias in Artificial Intelligence & Machine Learning

Area of research : data analysis & policy research

Goal : Investigate sources of bias for A.I. / M.L. algorithms -- particularly social, racial, and selection biases.  Research on policy implications for these biases.

Algorithmic bias is an increasing serious societal issue.  It occurs when the outcomes of a software program are biased based on data collected or algorithms created by non-representative groups of humans.  A few months ago, Amazon needed to scrap its "artificial intelligence" based recruiting tool because the selection was shown to be biased against women.  Other examples include search engine results and social media platforms.  All of these wonders of "AI" already have significant impact ranging from inadvertent privacy violations to reinforcing social biases of race, gender, sexuality, and ethnicity.  In this research, we are interested to study algorithmic biases that reflect "systematic and unfair" discrimination. 

Reference: J. Dastin, "Amazon scraps secret AI recruiting tool that showed bias against women", Reuters, October 9, 2018.

Evolution of Cooperation

Area of research: multi-agents system & game theory

Goal: This research investigates how cooperation can evolve from self-interested agents across indefinite time horizon.  

The complexity of human's cooperative behavior cannot be fully explained by theories of kin selection and group selection. If reciprocal altruism is to provide an explanation for altruistic behavior, it would have to depart from direct reciprocity, which requires dyads of individuals to interact repeatedly. For indirect reciprocity to rationalize cooperation among genetically unrelated or even culturally dissimilar individuals, information about the reputation of individuals must be assessed and propagated in a population.  In this research, we will build a framework for the evolution of indirect reciprocity by social information: information selectively retrieved from and propagated through dynamically evolving networks of friends and acquaintances. 


Reference: L. Mui, et al, (2003) "Evolution of indirect reciprocity by social information",Journal of Theoretical Biology 223(4):523-31 · September 2003

Social & Economic Influence on Healthcare Outcomes

Area of research : multi-factorial data analysis

Although social and economic policies are not usually considered in designing modern healthcare services, such policies are empirically shown influential on health and disease by altering social determinants of health (SDH).  For this research project, we will be sourcing & curating longitudinal datasets across authoritative sources, and to analyze the social & economic influences on health.   Our goal is to shed light on likely confounding social & economic factors that could inform better health policy planning.  Goal : 


Reference: T. L. Osypuk, et al., "Do Social and Economic Policies Influence Health? A Review", Curr Epidemiol Rep. 2014 Sep 1; 1(3): 149–164.

Research | Summer 2019


Phil Mui, Ph.D., is a Senior Vice President of Technology (Architecture Strategy) in Salesforce, and of the Office of the CTO.  He has responsibilities over Salesforce's technology strategy, architecture long range plans, and strategic projects.  Before Salesforce, Phil led the product development of Google Analytics and the digital transformation of Acxiom + LiveRamp.  Outside work, Phil is active as board members / advisors in various non-profits and for-profit organizations.  He coaches his local elementary school's Math Olympiad team.  He received his SB, MEng, PhD in EECS (Electrical Engineering & Computer Science) from MIT, and an MPhil in Management Studies as a Marshall Scholar at Oxford.  His Ph.D. dissertation was on Computational models of trust and reputation : agents, evolutionary games, and social networks at the MIT Lab for Computer Science (the current CSAIL).

Phil Mui, Ph.D.



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