Companies have been developing and using artificial intelligence (AI) for decades. But we've seen exponential growth since OpenAI released their version of a large language model (LLM), ChatGPT, in 2022. Open-source versions of these tools can help agencies optimize their processes and surpass current levels of data analysis, all in a secure environment that won’t risk exposing sensitive information.
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.
This study evaluates the use of RPA technology by three states to automate SNAP administration, focusing on repetitive tasks previously performed manually.
Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. This academic study presents an approach to evaluate bias present in automated facial analysis algorithms and datasets.
A catalogue to help teams design trustworthy services that work for people. Categories including informing decisions, signing into services, giving and removing consent, and doing security checks.
To help policy makers, regulators, legislators and others characterize AI systems deployed in specific contexts, the OECD has developed a user-friendly tool to evaluate AI systems from a policy perspective.
Organisation for Economic Co-operation and Development (OECD)
This academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.
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.
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.
Center for Security and Emerging Technology (CSET)
This study examines the adoption and implementation of AI chatbots in U.S. state governments, identifying key drivers, challenges, and best practices for public sector chatbot deployment.
In this interview, Code for America staff members share how client success, data science, and qualitative research teams work together to consider the responsible deployment of artificial intelligence (AI) in responding to clients who seek assistance with three products.
This post introduces EPIC's exploration of actionable recommendations and points of agreement from leading A.I. frameworks, beginning with the National Institute of Standards and Technology's AI Risk Management Framework.
