Tasha Austin-Williams, Ph.D.

In this paper, we explore how AI, specifically machine learning, can help manage the P2P platform Kiva’s investment risks and deliver impact, emphasizing the importance of prediction model retraining to account for regulatory and other changes across the P2P landscape to drive better decision-making. As part of this research, we also explore how changes in important model variables affect aggregate model predictions.

This paper provides perspectives on how teaming artificial intelligence with humans can help de-bias decision-making and recommends six steps organizations can take to help confirm responsible development of AI and limit the implementation of algorithms that may result in individual and societal biases.

AI offers so much opportunity in the financial sector, which is truly exciting. However, that enthusiasm cannot obscure the risks this technology poses if it isn’t governed properly. It doesn’t matter how sophisticated or automated an AI-driven application is or the cost savings it delivers if it harms historically disenfranchised communities or negatively affects the customer experience. As financial institutions embrace AI, they also need to embrace robust AI governance to prevent bias and… Show more

AI offers so much opportunity in the financial sector, which is truly exciting. However, that enthusiasm cannot obscure the risks this technology poses if it isn’t governed properly. It doesn’t matter how sophisticated or automated an AI-driven application is or the cost savings it delivers if it harms historically disenfranchised communities or negatively affects the customer experience. As financial institutions embrace AI, they also need to embrace robust AI governance to prevent bias and maximize the full potential of this transformative technology. Show less

The magic of generative AI isn’t in the silicon; it’s in the process—the way humans and AI work together to unearth new, valuable solutions to hard problems. That process is known as generative design. This paper provides recommendations on how leaders can harness new generative AI tools to help design the innovations of the future.

AI has moved from the realm of science fiction and leaped into practical reality. Though technological advancements push the boundaries of what’s possible every day, government leaders still face significant challenges in adopting and scaling AI. This project analyzes and presents more than 40 AI use cases across 15 countries to understand how government agencies use AI and factors that contribute to successful AI projects.

In collaboration with Google, this article highlights how the Department of State can use AI today to address the biggest global changes.

Our global survey of 500 government leaders shows three key findings for organizations looking to adopt AI at scale:
1) Government organizations have made a strong start in exploring a wide variety of AI proofs of concept.
2) The transformational benefits of AI require adoption of AI at scales much larger than proofs of concept.
3) To move from pilots to at-scale AI, organizations need to not just adopt the technology, but to adapt their organizations across six key dimensions.

This article highlights the opportunities and risks of using open data for AI and offers recommended steps government leaders can take to make use of open data to accelerate AI, while mitigating their exposure to new risks.

This article highlights the importance of fostering diversity in STEM learning and careers with AI.

As data becomes increasingly critical to problem-solving, governments are exploring ways to enhance data-sharing so that it can add greater value and save time, money, and even live.

This white paper draws on the experiences of relevant stakeholders, informed by implementation of other open data laws, to identify and elaborate on the challenges for federal agencies in developing strategies and practices to support OPEN’s goals.