The team aimed to automate applying rules efficiently by creating computable policies, recognizing the need for AI tools to convert legacy policy content into automated business rules using Decision Model Notation (DMN) for effective processing and monitoring.
This report explores the role that academic and corporate Research Ethics Committees play in evaluating AI and data science research for ethical issues, and also investigates the kinds of common challenges these bodies face.
This analysis examines the surge in U.S. state-level AI legislation in 2023, highlighting enacted laws, proposed bills, and emerging regulatory trends.
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.
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 primer is written for a non-technical audience to increase understanding of the terminology, applications, and difficulties of evaluating facial recognition technologies.
The State of California government published guidelines for the safe and effective use of Generative Artificial (GenAI) within state agencies, in accordance with Governor Newsom's Executive Order N-12-23 on Generative Artificial Intelligence.
Executed on September 24, 2024, a memorandum for the heads of executive departments and agencies on advancing the responsible acquisition of artificial intelligence in government.
A workshop led by Elham Ali on integrating the principles of human-centered design and equity to Artificial Intelligence (AI) design, use, and evaluation.
This course is designed to help public professionals accelerate the process of finding and implementing urgently-needed evidence-based solutions to public problems.
The article discusses the phenomenon of model multiplicity in machine learning, arguing that developers should be legally obligated to search for less discriminatory algorithms (LDAs) to reduce disparities in algorithmic decision-making.
