Amelia Katirai 교수

Public and private investments into developing digital health technologies-including artificial intelligence (AI)-are intensifying globally. Japan is a key case study given major governmental investments, in part through a Cross-Ministerial Strategic Innovation Promotion Program (SIP) for an "Innovative AI Hospital System." Yet, there has been little critical examination of the SIP Research Plan, particularly from an ethics approach. This paper reports on an analysis of the Plan to identify the

Patients and members of the public are the end users of healthcare, but little is known about their views on the use of artificial intelligence (AI) in healthcare, particularly in the Japanese context. This paper reports on an exploratory two-part workshop conducted with members of a Patient and Public Involvement Panel in Japan, which was designed to identify their expectations and concerns about the use of AI in healthcare broadly. 55 expectations and 52 concerns were elicited from workshop pa

Healthcare has emerged as a key setting where expectations are rising for the potential benefits of artificial intelligence (AI), encompassing a range of technologies of varying utility and benefit. This paper argues that, even as the development of AI for healthcare has been pushed forward by a range of public and private actors, insufficient attention has been paid to a key contradiction at the center of AI for healthcare: that its pursuit to improve health is necessarily accompanied by enviro

Patient involvement (PI) in determining medical research priorities is an important way to ensure that limited research funds are allocated to best serve patients. As a disease area for which research funds are limited, we see a particular utility for PI in priority-setting for medical research on rare diseases. In this review, we argue that PI initiatives are an important form of evidence for policymaking. We conducted a study to identify the extent to which PI initiatives are being conducted i

The use of emotion recognition technologies in the workplace is expanding. These technologies claim to provide insights into internal emotional states based on external cues like facial expressions. Despite interconnections between autism and the development of emotion recognition technologies as reported in prior research, little attention has been paid to the particular issues that arise for autistic individuals when emotion recognition technologies are implemented in consequential settings li

Abstract The development and deployment of artificial intelligence (AI) has rapidly outpaced regulation. As a result, many organizations opt to develop their own principles for the ethical development of AI, though little research has examined the processes through which they are developed. Prior research indicates that these processes involve perceived trade-offs between competing considerations, and primarily between ethical concerns and organizational benefits or technological development. In

The race to develop image generation models is intensifying, with a rapid increase in the number of text-to-image models available. This is coupled with growing public awareness of these technologies. Though other generative AI models--notably, large language models--have received recent critical attention for the social and other non-technical issues they raise, there has been relatively little comparable examination of image generation models. This paper reports on a novel, comprehensive categ

Recent years have seen an increasing focus on issues of equity and inclusion in higher education, as part of a shift towards symbiotic societies (Edyburn 2010). As a result of advocacy and legal changes, there have been significant increases in the number of disabled students participating in higher education in both the United States and Japan (Raue and Lewis 2011; JASSO 2018). In this paper, I report the findings of a preliminary content analytic exploration of the disability services office w