Topic: Automation + AI
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Large Language Models (LLMs): An Explainer
In this blog post, CSET’s Natural Language Processing (NLP) Engineer, James Dunham, helps explain LLMs in plain English.
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Disability, Bias, and AI
This report explores key questions that a focus on disability raises for the project of understanding the social implications of AI, and for ensuring that AI technologies don’t reproduce and extend histories of marginalization.
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Democratizing AI: Principles for Meaningful Public Participation
In this policy brief and video, Michele Gilman summarizes evidence-based recommendations for better structuring public participation processes for AI, and underscores the urgency of enacting them.
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Controlling Large Language Models: A Primer
Concerns over risks from generative artificial intelligence systems have increased significantly over the past year, driven in large part by the advent of increasingly capable large language models. But, how do AI developers attempt to control the outputs of these models? This primer outlines four commonly used techniques and explains why this objective is so challenging.
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Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing
This paper introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle.
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Better Together? An Evaluation of AI-Supported Code Translation
This research explores how software engineers are able to work with generative machine learning models. The results explore the benefits of generative code models and the challenges software engineers face when working with their outputs. The authors also argue for the need for intelligent user interfaces that help software engineers effectively work with generative code models.
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A Snapshot of Artificial Intelligence Procurement Challenges
Artificial intelligence promises exciting new opportunities for the government to make policy, deliver services and engage with residents. But government procurement practices need to adapt if we are to ensure that rapidly-evolving AI tools meet intended purposes, avoid bias, and minimize risks to people, organizations, and communities. This report lays out five distinct challenges related to procuring AI in government.
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State of Washington Executive Order 24-01
State of Washington's executive order on artificial intelligence (AI)
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City of Lebanon Use of Artificial Intelligence Policy
First Artificial Intelligence (AI) Policy in the City of Lebanon, NH
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Catalyzing the Responsible and Productive Use of Artificial Intelligence in Maryland State Government
Executive order from the State of Maryland on the topic of artificial intelligence (AI)
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Use of Advanced Automation in SNAP
This 2024 memo outlines guidelines for state agencies' use of advanced automation in SNAP administration.
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State of California Benefits and Risks of Generative Artificial Intelligence Report
A report from the State of California presenting an initial analysis of where generative AI (GenAI) may improve access of essential goods and services.