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Susan Ariel Aaronson

Report

Data disquiet: concerns about the governance of data for Generative AI


People increasingly rely on large language model (LLM) chatbots such as ChatGPT or Copilot to receive and organise information. But these chatbots often make mistakes or provide 'made-up' or false information. The author of this paper argues that policy-makers have responded to this challenge in a piecemeal fashion, and uses qualitative methods to examine these...
Report

Missing persons: the case of national AI strategies


The authors of this paper find that policy-makers are missing an opportunity to build trust in artificial intelligence (AI) by not involving a broader cross-section of the public in AI policy development.
Report

Could a global “Wicked Problems Agency” incentivize data sharing?


The world is rife with 'wicked' problems — problems that no one knows how to solve without creating further problems. The author of this paper proposes a new international organisation, the Wicked Problems Agency, to catalyse both data sharing and data analysis in the interest of mitigating wicked problems.
Policy report

Building trust in AI: a landscape analysis of government AI programs


The Organisation for Economic Co-operation and Development’s (OECD’s) website on artificial intelligence (AI) policy (the OECD.AI Policy Observatory) is the world’s best source for information on public policies dedicated to AI, trustworthy AI and international efforts to advance cooperation in AI. The author’s review of the information contained on the site reveals that governments have...
Report

A future built on data: data strategies, competitive advantage and trust


In the twenty-first century, data became the subject of national strategy. This paper examines these visions and strategies to better understand what policy-makers hope to achieve.

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