Category: 2022

September 2022

Susan Ariel Aaronson (George Washington University)

IIEP working paper 2022-10

Abstract: OECD.AI is the world’s best source for information on public policies dedicated to AI, trustworthy AI and international efforts to advance cooperation in AI. However, the web site is also a missed opportunity to ascertain best practice and to build trust in AI not just for citizens of reporting nations but for the world. The author came to that conclusion after examining the documentation that nations placed online at OECD.AI. website. She utilized a landscape analysis to group these policies reported to the OECD by country and type, whether the initiative was evaluated or reported on, and whether it provided new insights about best practice trust, in AI, and/or trustworthy AI. Some 61 countries and the EU reported to the OECD on their AI initiatives (for a total of 62). Although the members of the OECD are generally high and high-middle income nations, the 62 governments providing information to OECD.AI represent a mix of AI capacity, income level, economic system, and location. Some 814 initiatives placed on the website as of August 2022, but 4 were duplicative and some 30 were blank, leaving 780. Of these, countries claimed that 48 of these initiatives were evaluated. However, we actually found only four evaluations (and one in progress) with a clear evaluative methodology. Two initiatives were labeled evaluations but did not include a methodology. Many of the other 42 were reports rather than evaluations. In addition, only a small percentage (41 initiatives or 5% of all initiatives) were designed to build trust in AI or to create trustworthy AI systems. National policymakers and not the OECD Secretariat decide what each of the 62 governments choose to put on the site. These officials don’t list every initiative their country implements to foster AI. But their choices reveal their priorities. Most of the documentation focuses on what they are doing to build domestic AI capacity and a supportive governance context for AI. We also found relatively few efforts to build international cooperation on AI, or to strengthen other countries’ AI capacity. Taken in sum, these efforts are important but reveal little effort to build international trust in AI.

JEL Codes: A1, 034, 032, 038, 057

Key Words: AI (artificial intelligence) trust, trustworthy, policies, innovation

September 2022

Susan Ariel Aaronson (George Washington University)

IIEP working paper 2022-09

Abstract: Global public goods are goods and services with benefits and costs that potentially extend to all countries, people, and generations. Global data sharing can also help solve what scholars call wicked problems-problems so complex that they require innovative, cost effective and global mitigating strategies. Wicked problems are problems that no one knows how to solve without creating further problems. Hence, policymakers must find ways to encourage greater data sharing among entities that hold large troves of various types of data, while protecting that data from theft, manipulation etc. Many factors impede global data sharing for public good purposes; this analysis focuses on two. First, policymakers generally don’t think about data as a global public good; they view data as a commercial asset that they should nurture and control. While they may understand that data can serve the public interest, they are more concerned with using data to serve their country’s economic interest. Secondly, many leaders of civil society and business see the data they have collected as proprietary data. So far many leaders of private entities with troves of data are not convinced that their organization will benefit from such sharing. At the same time, companies voluntarily share some data for social good purposes. However, data cannot meet its public good purpose if data is not shared among societal entities. Moreover, if data as a sovereign asset, policymakers are unlikely to encourage data sharing across borders oriented towards addressing shared problems. Consequently, society will be less able to use data as both a commercial asset and as a resource to enhance human welfare. This paper discusses why the world has made so little progress encouraging a vision of data as a global public good. As UNCTAD noted, data generated in one country can also provide social value in other countries, which would call for sharing of data at the international level through a set of shared and accountable rules (UNCTAD: 2021). Moreover, the world is drowning in data, yet much of that data remains hidden and underutilized. But guilt is a great motivator. The author suggests a new agency, the Wicked Problems Agency, to act as a counterweight to that opacity and to create a demand and a market for data sharing in the public good.

JEL Codes: 024,034/032, 038, 045, C45 

Key Words: data, AI, public good, wicked problems, data-sharing