On the Patentability of Artificial Intelligence: a review of recent cases from the U.S.

Ethics of Artificial Intelligence in Transport

The Oxford Handbook of Ethics of AI ◽

10.1093/oxfordhb/9780190067397.013.42 ◽

2020 ◽

pp. 667-683

Author(s):

Bryant Walker Smith

Keyword(s):

Artificial Intelligence ◽

Ethical Issues ◽

Ethical Question ◽

Power Dynamics ◽

Automated Vehicles ◽

Automated Driving ◽

The U.S ◽

Policy Solutions

This chapter highlights key ethical issues in the use of artificial intelligence in transport by using automated driving as an example. These issues include the tension between technological solutions and policy solutions; the consequences of safety expectations; the complex choice between human authority and computer authority; and power dynamics among individuals, governments, and companies. In 2017 and 2018, the U.S. Congress considered automated driving legislation that was generally supported by many of the larger automated-driving developers. However, this automated-driving legislation failed to pass because of a lack of trust in technologies and institutions. Trustworthiness is much more of an ethical question. Automated vehicles will not be driven by individuals or even by computers; they will be driven by companies acting through their human and machine agents. An essential issue for this field—and for artificial intelligence generally—is how the companies that develop and deploy these technologies should earn people’s trust.

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Reliability of Regulating Artificial Intelligence to Restrain Cartelization: A Libertarian Approach

Asian Journal of Law and Economics ◽

10.1515/ajle-2021-0012 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Matin Pedram

Keyword(s):

Artificial Intelligence ◽

Building Block ◽

Common Good ◽

Market Process ◽

Executive Order ◽

The Common Good ◽

Government Interference ◽

The Common ◽

The U.S

Abstract Competition is building block of any successful economy, while a cartelized economy is against the common good of society. Nowadays, developing artificial intelligence (AI) and its plausibility to foster cartels persuade governments to revitalize their interference in the market and implement new regulations to tackle AI implications. In this sense, as pooling of technologies might enable cartels to impose high prices and violate consumers’ rights, it should be restricted. By contrast, in the libertarian approach, cartels’ impacts are defined by government interference in the market. Accordingly, it is irrational to rely on a monopolized power called government to equilibrate a cartelized market. This article discusses that AI is a part of the market process that should be respected, and a restrictive or protective approach such as the U.S. government Executive Order 13859 is not in line with libertarian thought and can be a ladder to escalate the cartelistic behaviors.

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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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Trusted Partners: Human-Machine Teaming and the Future of Military AI

10.51593/20200024 ◽

2021 ◽

Author(s):

Margarita Konaev ◽

Tina Huang ◽

Husanjot Chahal

Keyword(s):

Artificial Intelligence ◽

Future Directions ◽

The Future ◽

The U.S

As the U.S. military integrates artificial intelligence into its systems and missions, there are outstanding questions about the role of trust in human-machine teams. This report examines the drivers and effects of such trust, assesses the risks from too much or too little trust in intelligent technologies, reviews efforts to build trustworthy AI systems, and offers future directions for research on trust relevant to the U.S. military.

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U.S. AI Workforce: Labor Market Dynamics

10.51593/20200086 ◽

2021 ◽

Author(s):

Diana Gehlhaus ◽

Ilya Rahkovsky

Keyword(s):

Artificial Intelligence ◽

Labor Market ◽

The State ◽

Potential Effectiveness ◽

Good Data ◽

Information Gap ◽

Labor Market Dynamics ◽

Ongoing Concern ◽

Insight Into ◽

The U.S

A lack of good data on the U.S. artificial intelligence workforce limits the potential effectiveness of policies meant to increase and cultivate this cadre of talent. In this issue brief, the authors bridge that information gap with new analysis on the state of the U.S. AI workforce, along with insight into the ongoing concern over AI talent shortages. Their findings suggest some segments of the AI workforce are more likely than others to be experiencing a supply-demand gap.

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‘At the Expense of Human Values’

10.3828/liverpool/9781800348301.003.0003 ◽

2020 ◽

pp. 35-48

Author(s):

Joshua Grimm

Keyword(s):

Artificial Intelligence ◽

Science Fiction ◽

History Of Science ◽

Cognitive Dissonance ◽

Nuclear Power ◽

Silicon Valley ◽

Human Values ◽

Mad Scientist ◽

History Of ◽

The U.S

Ex Machina plays against type extremely well, and it is, in part, particularly effective because of the genre tropes are relatively consistent. There’s really only one term to cinematically describe a reclusive, temperamental genius working on a project he hopes will change humanity: the mad scientist. In the history of science fiction film, the mad scientist has traditionally either been directly responsible for a crisis (potential or realized) by creating the problem, or indirectly responsible by trying to control something so powerful that no one could possibly control it. The latter was used largely in the 1950s and 1970s by reflecting the two perceived threats during those eras: atomic/nuclear power and pollution, respectively. But in these films, the extent of the power being studied must be balanced against what that scientist is trying to accomplish. In Ex Machina, Nathan’s portrayal is a fascinating one, embodying the Silicon Valley, “work hard, play hard” bro-culture we see in the U.S. tech industry, and he’s able to completely detach his own actions/desires from his work, a cognitive dissonance that allows him to create a line of slaves at the same time he tries to reproduce artificial intelligence. This chapter will place Nathan within the larger context of science fiction’s history of mad scientists, analyzing similarities and determining what those differences mean.

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