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STEP Abstracts

AI and Work (STEP)

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Generative AI and the future of work in America, McKinsey Global Institute Report, July 26, 2023

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STEP Abstracts

AI and Education (STEP)

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Principles for the Development, Deployment, and Use of Generative AI Technologies, ACM Technology Policy Council (June 27, 2023)

Generative Artificial Intelligence (AI) is a broad term used to describe computing techniques and tools that can be used to create new content such as text, speech and audio, images and video, and computer code. While such systems offer tremendous opportunities for benefits to society, they also pose very significant risks. The increasing power of generative AI systems, the speed of their evolution, broad application, and potential to cause significant or even catastrophic harm means that great care must be taken in researching, designing, developing, deploying, and using them. Existing mechanisms and modes for avoiding such harm likely will not suffice.

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STEP Abstracts

AI Regulation (STEP)

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CARE Race to Regulate the Internet program was held on May 8
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Should States or the Federal Government Set the Rules for Websites Content, Child Protection and Personal Data Control?

See headlines and details at the CARE Race to Regulate the Internet update.


Statement in Support of Mandatory Comprehensive Digital Accessibility Regulations

US Technology Policy Committee (May 31, 2024)

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STEP Abstracts

Content Provenance in the Age of AI (STEP)

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FACTS

INFORMATION

DATA

VALIDITY

Content Provenance refers to the facts about the history of a piece of digital content assets (image, video, audio recording, document).

Technical specifications were released in 2022 by the Coalition for Content Provenance and Authenticity (C2PA Content Credentials).

Data provenance refers to a documented trail that accounts for the origin of a piece of data and where it has moved from to where it is presently. The purpose of data provenance is to tell developers the origin, changes to, and details supporting the confidence or validity of data. The concept of provenance guarantees that data creators are transparent about their work -- where it came from and the chain of information where data can be tracked as data is used and adapted for their own purposes.

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STEP Abstracts

Cybersecurity (STEP)

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Statement On Mass Cybersecurity Incidents Likely to Recur, US Technology Policy Committee (August 11, 2024)

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STEP Abstracts

Trustworthy AI

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ACM TechBrief on Trusted AI

The effectiveness of mechanisms and metrics implemented to promote trust of AI must be empirically evaluated to determine if they actually work. Distrust of AI implicates trustworthiness and calls for a deeper understanding of stakeholder perceptions, concerns, and fears associated with AI and its specific applications. Fostering public trust of AI will require that policymakers demonstrate how they are making industry accountable to the public and their legitimate concerns.

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STEP Abstracts

Explainable AI

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EXPLAINABLE AI
  • Definitions of key terms
  • Summarize areas of research
  • Comments from individuals and organizations
  • Understandings, issues and predictions