The Issues

  • Artificial Intelligence
  • Competition Policy and Antitrust
  • Innovation and Economic Growth
  • Intellectual Property
  • Interoperability
  • Networks, the Internet, and Cloud Computing
  • Privacy and Security

TAP Highlights

Commercialization of Machine Learning

“Advances in computer technology in the last decade opened up the potential for big gains in applications of neural networks aimed at recognizing and diagnosing visual images. Many startups are making decisions about how to deploy such software, and what products to develop next.” – Harvard business professor Shane Greenstein

Can Algorithms that Automate Decisions Be Held Accountable?

“A Model Algorithmic Impact Assessment process should deliberately widen the lens from algorithms as a technology in isolation, to algorithms as systems embedded in human systems— both those that design the technology, and those that use it.” – Margot Kaminski, University of Colorado Law,  and Gianclaudio Malgieri, VUB

What Is Contextual Integrity?

Consider this: a smartphone user may be okay with their location data being sent to a website when looking for restaurant suggestions; but they may not be okay if that data is also used for advertising. Contextual integrity is a new model for privacy that focuses on societal norms and the appropriateness of information flows.

What Is the Promise of AI?

Artificial intelligence (AI) technologies are transforming society and changing processes in business and government. Additionally, AI poses challenges that need to be addressed by policy makers and stakeholders involved in its development and deployment. Read TAP’s new fact sheet on the issues that arise in discussions of AI.

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New TAP Blog Posts

Earning Stripes in Medical Machine Learning

Posted on November 14, 2019
thumbnail Harvard business professor Shane Greenstein discusses some of the factors that machine-learning startups consider in developing their innovative products for commercial value.

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