Diane Wilson

Depression among individuals who have been racially and ethnically minoritized in the United States can be vastly different from that of non-Hispanic White Americans. For example, African American adults who have depression rate their symptoms as more severe, have a longer course of illness, and experience more depression-associated disability. The purpose of this review was to conceptualize how structural racism and cumulative trauma can be fundamental drivers of the intergenerational transmission of depression. The authors propose that understanding risk factors for depression, particularly its intergenerational reach, requires accounting for structural racism. In light of the profoundly different experiences of African Americans who experience depression (i.e., a more persistent course of illness and greater disability), it is critical to examine whether an emerging explanation for some of these differences is the intergenerational transmission of this disorder due to structural racism.

Details of the designs and mechanisms in support of human-AI collaboration must be considered in the real-world fielding of AI technologies. A critical aspect of interaction design for AI-assisted human decision making are policies about the display and sequencing of AI inferences within larger decision-making workflows. We have a poor understanding of the influences of making AI inferences available before versus after human review of a diagnostic task at hand. We explore the effects of providing AI assistance at the start of a diagnostic session in radiology versus after the radiologist has made a provisional decision. We conducted a user study where 19 veterinary radiologists identified radiographic findings present in patients’ X-ray images, with the aid of an AI tool. We employed two workflow configurations to analyze (i) anchoring effects, (ii) human-AI team diagnostic performance and agreement, (iii) time spent and confidence in decision making, and (iv) perceived usefulness of the AI. We found that participants who are asked to register provisional responses in advance of reviewing AI inferences are less likely to agree with the AI regardless of whether the advice is accurate and, in instances of disagreement with the AI, are less likely to seek the second opinion of a colleague. These participants also reported that the AI advice to be less useful. Surprisingly, requiring provisional decisions on cases in advance of the display of AI inferences did not lengthen the time participants spent on the task. The study provides generalizable and actionable insights for the deployment of clinical AI tools in human-in-the-loop systems and introduces a methodology for studying alternative designs for human-AI collaboration. We make our experimental platform available as open source to facilitate future research on the influence of alternate designs on human-AI workflows.

:Electronic commerce has made possible a new form of electronic marketplace-the “all-in-one markel.” AII-in-one markets combine multiple trading mechanisms on a common platform and organize trading so that buyers and suppliers can dynamically shift trading methods or simultaneously take advantage of the best features of open market competition and long-term supplier partnerships. Firms that effectively leverage these new platforms are likely to realize substantial benefits in their sourcing and distribution strategies. This paper illustrates how electronic commerce enables allin-one markets, and considers the opportunities and challenges they pose for buyers, sellers, and third-party market makers.