Performance assessment on technology transition from small businesses to the U.S. Department of Defense

AbstractWe produce the first systematic study of the determinants and implications of in-person banking. Using survey data from the U.S., we show that firms which are informationally opaque or operate in rural areas are liable to contact their primary bank in-person. This tendency extends to older, less educated, and female business owners. We find that a relationship based on face-to-face communication, on average, lasts 17.88 months longer, spans a wider range of financial services, and is more likely to be exclusive. The associated loans mature 3.37 months later and bear interest rates which are 11 basis points lower. For good quality firms, in-person communication also relates to less discouraged borrowing. These results are robust to multiple approaches for endogeneity, including recursive bivariate probits, treatment effect models, and instrumental variables regressions. Overall, our findings offer empirical grounding to soft information theory and a note of caution to banks against suppressing channels of interpersonal communication.

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Probabilistic Performance Assessment vs. the Safety Case Approach

MRS Advances ◽

10.1557/adv.2018.681 ◽

2019 ◽

Vol 4 (17-18) ◽

pp. 987-992

Author(s):

François Diaz-Maurin ◽

Rodney C. Ewing

Keyword(s):

Performance Assessment ◽

Uncertainty Quantification ◽

International Community ◽

Regulatory Framework ◽

Geologic Repository ◽

Large Uncertainty ◽

Safety Case ◽

The Face ◽

The U.S

ABSTRACTThe “safety case” approach has been developed to address the issue of evaluating the performance of a geologic repository in the face of the large uncertainty that results for evaluations that extend over hundreds of thousands of years. This paper reviews the concept of the safety case as it has been defined by the international community. We contrast the safety case approach with that presently used in the U.S. repository program. Especially, we focus on the role of uncertainty quantification. There are inconsistencies between the initial proposal to dealing with uncertainties in a safety case and current U.S. practice. The paper seeks to better define the safety case concept so that it can be usefully applied to the regulatory framework of the U.S. repository program.

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Compounded inequality: how the U.S. Paycheck Protection Program is failing Los Angeles Latino small businesses

Ethnic and Racial Studies ◽

10.1080/01419870.2020.1844265 ◽

2020 ◽

pp. 1-12

Author(s):

Karina Santellano

Keyword(s):

Los Angeles ◽

Small Businesses ◽

The U.S

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Corporate Governance of Artificial Intelligence in the Public Interest

Information ◽

10.3390/info12070275 ◽

2021 ◽

Vol 12 (7) ◽

pp. 275

Author(s):

Peter Cihon ◽

Jonas Schuett ◽

Seth D. Baum

Keyword(s):

Artificial Intelligence ◽

Corporate Governance ◽

Law Enforcement ◽

Nonprofit Organizations ◽

Public Interest ◽

Department Of Defense ◽

The Public ◽

Wide Range ◽

The Media ◽

The U.S

Corporations play a major role in artificial intelligence (AI) research, development, and deployment, with profound consequences for society. This paper surveys opportunities to improve how corporations govern their AI activities so as to better advance the public interest. The paper focuses on the roles of and opportunities for a wide range of actors inside the corporation—managers, workers, and investors—and outside the corporation—corporate partners and competitors, industry consortia, nonprofit organizations, the public, the media, and governments. Whereas prior work on multistakeholder AI governance has proposed dedicated institutions to bring together diverse actors and stakeholders, this paper explores the opportunities they have even in the absence of dedicated multistakeholder institutions. The paper illustrates these opportunities with many cases, including the participation of Google in the U.S. Department of Defense Project Maven; the publication of potentially harmful AI research by OpenAI, with input from the Partnership on AI; and the sale of facial recognition technology to law enforcement by corporations including Amazon, IBM, and Microsoft. These and other cases demonstrate the wide range of mechanisms to advance AI corporate governance in the public interest, especially when diverse actors work together.

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