Innovation Policy and the Economy, Volume 17

Shane  Greenstein; Josh  Lerner; Scott  Stern

Book | 2016

Innovation Policy and the Economy, Volume 17

by Shane Greenstein, Josh Lerner and Scott Stern

Abstract

The seventeenth volume of the National Bureau of Economic Research’s Innovation Policy and the Economy provides an accessible forum for bringing the work of leading academic researchers to an audience of policymakers and those interested in the interaction between public policy and innovation. In the first chapter, Joel Waldfogel discusses how reduced costs of production have resulted in a “Golden Age of Television,” arguing that this development has gone underappreciated. The second chapter, by Marc Rysman and Scott Schuh, discusses the prospects for innovation in payment systems, including mobile payments, faster payment systems, and digital currencies. In the third chapter, Catherine Tucker and Amalia Miller analyze the consequences of patient data becoming virtually costless to store, share, and individualize, showing how data management and privacy issues have become a key factor in health policy. The fourth chapter, by Michael Luca, examines how online marketplaces have proliferated over the past decade, evolving far beyond the pioneers such as eBay and Amazon. In the fifth chapter, Tim Bresnahan and Pai-Ling Yin characterize information and communication technologies in the workplace, addressing how wages vary with increasing demand for smart managers and professionals, and workers with organizational participation skills.

Keywords: Innovation and Invention; Technological Innovation; Governance; Policy; Economy;

Format: Print

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Citation:

Greenstein, Shane, Josh Lerner and Scott Stern, eds. Innovation Policy and the Economy, Volume 17. Chicago: University of Chicago Press, 2016.

Teaching note is meant to accompany Zebra Medical Vision case, which offers a look at a company’s decisions as a small startup competing with other startups and major technology companies. It also demonstrates the challenges faced by a machine learning company working on brand-new technology, including difficulties finding training data, hiring competition, and negotiating FDA approvals and other government regulations. Teaching Note for HBS No. 619-014.

An Israeli startup founded in 2014, Zebra Medical Vision developed algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making diagnoses. By July 2018, Zebra had developed seven algorithms to analyze scans for emphysema, liver density, compression fractures, bone density, brain bleeds, breast cancer, and a calcium score used to detect calcified plaque in coronary arteries. For each scan analyzed, Zebra charged hospitals $1. By 2018 Zebra found itself in a race with its competitors to perfect these algorithms, create software tools, distribute the tools to physician partners, and create a market. Zebra already had several partners in the U.S. and Europe who gave feedback on its development. Management had to answer the following questions: What should they do next at Zebra? Should they work on the accuracy of the already developed algorithms or continue to develop many new tools? If they chose to develop new products, which applications should they address? And how should they go to market?

Article | MIS Quarterly | September 2018

Do Experts or Crowd-Based Models Produce More Bias? Evidence from Encyclopædia Britannica and Wikipedia

Shane Greenstein and Feng Zhu

Organizations today can use both crowds and experts to produce knowledge. While prior work compares the accuracy of crowd-produced and expert-produced knowledge, we compare bias in these two models in the context of contested knowledge, which involves subjective, unverifiable, or controversial information. Using data from Encyclopædia Britannica, authored by experts, and Wikipedia, an encyclopedia produced by an online community, we compare the slant and bias of pairs of articles on identical topics of U.S. politics. Our slant measure is less (more) than zero when an article leans towards Democratic (Republican) viewpoints, while bias is the absolute value of the slant. We find that Wikipedia articles are more slanted towards Democratic views than are Britannica articles, as well as more biased. The difference in bias between a pair of articles decreases with more revisions. The bias on a per word basis hardly differs between the sources because Wikipedia articles tend to be longer than Britannica articles. These results highlight the pros and cons of each knowledge production model, help identify the scope of the empirical generalization of prior studies comparing the information quality of the two production models, and offer implications for organizations managing crowd-based knowledge production.

Keywords: online community; Collective Intelligence; wisdom of crowds; bias; Wikipedia; Britannica; knowledge production; Knowledge Sharing; Knowledge Dissemination; Prejudice and Bias;

Citation:

Greenstein, Shane, and Feng Zhu. "Do Experts or Crowd-Based Models Produce More Bias? Evidence from Encyclopædia Britannica and Wikipedia." MIS Quarterly 42, no. 3 (September 2018): 945–959. View Details

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