Algorithm Transparency - Publications - Faculty & Research

2023
Chapter

Marketing Through the Machine’s Eyes: Image Analytics and Interpretability

By: Shunyuan Zhang, Flora Feng and Kannan Srinivasan

he growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility—if only the... View Details

Keywords: Transparency; Marketing Research; Algorithmic Bias; AI and Machine Learning; Marketing

Citation

2021
Article

To Thine Own Self Be True? Incentive Problems in Personalized Law

By: Jordan M. Barry, John William Hatfield and Scott Duke Kominers

Recent years have seen an explosion of scholarship on “personalized law.” Commentators foresee a world in which regulators armed with big data and machine learning techniques determine the optimal legal rule for every regulated party, then instantaneously disseminate... View Details

Keywords: Personalized Law; Regulation; Regulatory Avoidance; Regulatory Arbitrage; Law And Economics; Law And Technology; Law And Artificial Intelligence; Futurism; Moral Hazard; Elicitation; Signaling; Privacy; Law; Governing Rules, Regulations, and Reforms; Information Technology; AI and Machine Learning

Citation

SSRN

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2019
Article

More Amazon Effects: Online Competition and Pricing Behaviors

By: Alberto Cavallo

I study how online competition, with its shrinking margins, algorithmic pricing technologies, and the transparency of the web, can change the pricing behavior of large retailers in the U.S. and affect aggregate inflation dynamics. In particular, I show that in the past... View Details

Keywords: Amazon; Online Prices; Inflation; Uniform Pricing; Price Stickiness; Monetary Economics; Economics; Macroeconomics; Inflation and Deflation; System Shocks; United States

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October 2017 (Revised April 2018)
Case

Improving Worker Safety in the Era of Machine Learning (A)

By: Michael W. Toffel, Dan Levy, Jose Ramon Morales Arilla and Matthew S. Johnson

Managers make predictions all the time: How fast will my markets grow? How much inventory do I need? How intensively should I monitor my suppliers? Which potential customers will be most responsive to a particular marketing campaign? Which job candidates should I... View Details