How to reverse-engineer quality rankings

By Allison Chang, Cynthia Rudin, Michael J. Cavaretta, Robert Thomas, Gloria Chou. 2012

12 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (2018, background): “se. In fact, the reason a system may be gamed is because it most likely was not designed properly in the ﬁrst place, leading to a form of Goodhart’s law if it were revealed. Quoting from Chang et al. [35] about product rating systems: “If the ratings are accurate measures of quality, then making the”
Learning Optimized Risk Scores (2016, methodology): “Our work is part of a broader stream of research on integer programming and other discrete optimization methods in supervised learning (e.g., Carrizosa et al., 2016; Liu and Wu, 2007; Goldberg and Eckstein, 2012; Guan et al., 2009; Nguyen and Franke, 2012; Sato et al., 2017, 2016; Rudin and Ertekin,”
Actionable Recourse in Linear Classification (2018, background): “These include: inverse classication, which aims to determine how the inputs to a prediction model can be manipulated to obtain a desired outcome [1, 8]; strategic classication, which considers the converse problem of training classiers that are robust to malicious manipulation [13, 17]; adversarial ”
Direct Learning to Rank and Rerank (2018, methodology): “…algorithms have been used also in sentiment analysis (Kessler and Nicolov, 2009), natural language processing (Ji et al, 2006; Collins and Koo, 2005), image retrieval (Jain and Varma, 2011; Kang et al, 2011), and reverse-engineering product quality rating systems (Chang et al, 2012).”
"On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking" (2012, background): “…on the axis of scalability, there are ﬁrst algorithms that are designed for small-scale datasets only and that directly solve the NP-hard problem [5] without using any surrogate loss; after them come algorithms that use a surrogate loss chosen as a non-convex but continuous and (almost…”
The Big Data Newsvendor: Practical Insights from Machine Learning (2013, background): “In a related work, Chang et al. (2012) use data from other items for predicting quality rankings for product categories that contain few products.”