Carrie S. Alexander

AI poses a challenge for current legal frameworks, referred to as an “AI liability gap.” Current legal systems based on knowledge, intent, and assumptions of moral agency evolved over hundreds of years, when it was still widely believed that human intelligence was not “natural” but “created” or, to borrow the term used for AI, “artificial.” In fact, it was the suggestion that human minds might be “natural” that provoked a cultural crisis in the late nineteenth century regarding society’s ability to govern humans untethered from divine accountability. This article looks at the way many in late-nineteenth-century England and the United States navigated this cultural crisis, which led to a breakdown in relationship that has persisted throughout the past century and a half and now undermines current efforts to construct meaningful dialogue regarding the effects of AI on humans and society. The article argues for a different approach, one that accommodates and embraces doubt and uncertainty as foundations for meaningful relationship and dialogue, which are essential prerequisites and foundations in efforts to govern AI and address the challenges AI now presents.

Governments, researchers, and developers emphasize creating “trustworthy AI,” defined as AI that prevents bias, ensures data privacy, and generates reliable results that perform as expected. However, in some cases problems arise not when AI is not trustworthy, technologically, but when it is. This article focuses on such problems in the food system. AI technologies facilitate the generation of masses of data that may illuminate existing food-safety and employee-safety risks. These systems may collect incidental data that could be used, or may be designed specifically, to assess and manage risks. The predictions and knowledge generated by these data and technologies may increase company liability and expense, and discourage adoption of these predictive technologies. Such problems may extend beyond the food system to other industries. Based on interviews and literature, this article discusses vulnerabilities to liability and obstacles to technology adoption that arise, arguing that “trustworthy AI” cannot be achieved through technology alone, but requires social, cultural, political, as well as technical cooperation. Implications for law and further research are also discussed.

This article presents findings from interviews that were conducted with agriculture and food system researchers to understand their views about what it means to conduct ‘responsible’ or ‘trustworthy’ artificial intelligence (AI) research. Findings are organized into four themes: (1) data access and related ethical problems; (2) regulations and their impact on AI food system technology research; (3) barriers to the development and adoption of AI-based food system technologies; and (4) bridges of trust that researchers feel are important in overcoming the barriers they identified. All four themes reveal gray areas and contradictions that make it challenging for academic researchers to earn the trust of farmers and food producers. At the same time, this trust is foundational to research that would contribute to the development of high-quality AI technologies. Factors such as increasing regulations and worsening environmental conditions are stressing agricultural systems and are opening windows of opportunity for technological solutions. However, the dysfunctional process of technology development and adoption revealed in these interviews threatens to close these windows prematurely. Insights from these interviews can support governments and institutions in developing policies that will keep the windows open by helping to bridge divides between interests and supporting the development of technologies that deserve to be called “responsible” or “trustworthy” AI.

Demography, the social science of population studies, has changed dramatically over the past forty years, responding to a dual crisis of funding and moral legitimacy that hit the field in the mid-1970s. This article uses structural topic modeling in conjunction with the Oral History Project of the Population Association of America (PAA) to examine how demography survived the crisis. It finds that demographers turned to a new source of funding, the National Institutes of Health, shifted their research focus from overseas population growth to domestic socioeconomic inequality, and transformed the PAA from an interest group for people concerned about population problems to a professional association for academic demographers. These three shifts turned demography into the field it is today.