Zhe Zhang

Current Research

I am an assistant professor at University of California San Diego's (UCSD) Rady School of Management. I am within the Innovation, Technology, and Operations (ITO) group. 

I completed my Ph.D. at Carnegie Mellon University at the Heinz College School of Information Systems and Management in Pittsburgh, PA, USA. My research agenda is on the spillover, broader, societal impacts of information technology. 

This is represented by two areas of work: one on fairness and implications of data-driven and algorithmic decision making, and one on the societal and spillover effects of digital transformation.

Spillover and Broader Effects of Digital Transformation

• "Business Models in the Sharing Economy: Manufacturing durable goods in the presence of Peer-to-Peer rental markets" (joint work with Vibhanshu Abhishek and Jose Guajardo)

  • Information Systems Research (2021):
    https://pubsonline.informs.org/doi/10.1287/isre.2021.1034 

  • Finalist for Best Student Paper in Supply Chain Management (POMS 2017)

  • Consider a manufacturer of durable goods, such as Toyota for cars. The growth of the sharing economy and digital innovation, allowed consumers who purchased from Toyota to engage in peer-to-peer (P2P) sharing of those cars, but also allowed potential consumers to use peer-rented cars instead of purchasing. In this paper, we analyze the strategic implications of P2P rental markets for such manufacturers. We find a surprising analytical insight. When consumers are heterogeneous in their usage frequencies, they have differing willingness-to-pay for the good. However, P2P sharing has an equalizing effect on this willingness-to-pay. Thus, P2P sharing can increase the monopoly-pricing capability of a manufacturer. In this paper, we explore the implications of this finding, and suggest business models under different settings.

• "The Spillover Effects of Amazon Prime on Online Retail Spending"

  • Working paper, joint work with Xiaofeng Liu and Kevin Zhu.

  • In this project, we are interested in what happens to a consumer's consumption patterns after they adopt Amazon Prime. Using a novel dataset on consumer debit and credit card transactions over a several year period, we are able to identify new Prime adopters, as well as how their consumption changes in other, non-Amazon places after Prime adoption. This includes other online retailers, small versus large online retailers, as well as offline retail consumption as well. In this paper, we also address potential self-selection concerns with the timing of Prime adoption. With the FTC's 2023 lawsuit against Amazon and Prime specifically, this paper's findings and novel empirical findings have meaningful policy and managerial insights.

• "Autonomous Retail's Effect on Food Consumption: Evidence from Amazon's Just Walk Out and a Major Convenience Retailer"

  • Working paper, joint work with Hyoduk Shin and Xiaofeng Liu.

• "Ridehailing's Effects on Offline Consumption"

  • Working paper, joint work with Beibei Li.

  • Uber and Lyft expanded across many US metropolitan areas between 2013 and 2015. We study the entry of Uber and Lyft into several of these metropolitan areas. Uber and Lyft have effected several parts of the economy, affecting both transportation patterns, tourism, and gig work. Using novel data at the individual-level, we provide novel evidence for how ridehailing services may have reshaped offline consumption among locals. We study the early adopters of ridehailing services, and show how this not only increases their local consumption, but also if this differs across categories, spatially, and along consumer demographics as well.
    
    

Fairness and Managerial Implications of Data-Driven Decision-Making and Machine Learning

• "Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness"

  • Working paper, joint work with Vincent Jeanselme, Maria DeArteaga, Brian D.M. Tom, and Jessica Barrett.

  • Initial version published at ML 4 Health (ML4H) at NeurIPS

  • Machine learning risks reinforcing biases present in data, and, as we argue in this work, in what is absent from data. In healthcare, biases have marked medical history, leading to unequal care affecting marginalised groups. Patterns in missing data often reflect these group discrepancies, but the algorithmic fairness implications of group-specific missingness are not well understood. In this paper, we contribute the first, to the best of our knowledge, thorough investigation of the relationship between the choice of imputation method and algorithmic fairness. The analysis consists of multiple parts: a review of real historical cases of missing data in healthcare settings and structure these into 3 scenarios, a theoretical analysis with novel insights on the role of imputation, and both simulation and real-world evidence for how imputation can significantly affect group fairness outcomes. An important finding is that in many meaningful cases and settings, group-specific imputation, while commonly recommended, can actually be harmful for group-fairness concerns.

• "Identifying Significant Predictive Bias in Classifiers" 

  • Working paper, joint with Daniel Neill.

  • Initial version published in the FAccT conference (formerly known as FAT ML conference).

  • This work is motivated by the increasing use of data-driven classifiers and risk assessment models for decision-making in various public and private sectors. Beyond overall performance assessment of these models, it's important that we identify if there are subpopulations or subgroups where such models may be over- or under-estimating the probability. With exponentially many such groups though, this can be a difficult problem. Our method enables such efficient and best-performing identification, providing both a way to audit the use of such models and improve them.

• "Strategic Contestants, Over-Fitting, and the Role of Data Splitting in Data Science Contests"

  • Working paper, joint work with Ping-Chieh Huang and Sanjiv Erat.

Background / Contact

Ph.D. Information Systems and Management, Carnegie Mellon University, Heinz College

Jointly advised by Professors Daniel Neill, Beibei Li, and Vibhanshu Abhishek

B.S. Mathematical and Computational Sciences (MCS), Stanford University

B.A. Economics, Stanford University

z9zhang@ucsd.edu

github.com/writezhe

In my undergraduate, I studied economics and policy, particularly around climate policy and energy markets. After my undergraduate, I spent some time continuing economics research on coal markets and the behavioral impact of pricing structure in electricity, and also spent time as a researcher at the Natural Resources Defense Council (NRDC) and Union of Concerned Scientists (UCS) nonprofits. I came to Carnegie Mellon to pursue my PhD using multiple disciplines including computer science and economics.

During my PhD, I have spent time as a part-time Data Creative staff at DataKind in NYC, NY, providing data science project brainstorming for potential partner organizations. I also was a 2016 Fellow at the Data Science for Social Good Summer Fellowship (DSSG) in Chicago, IL. There I worked on an interpretable prediction project for education support services.