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Current Issues in Public Utilities and Public Policy

Empirical Studies Focusing on Japan

• © 2023
• Fumitoshi Mizutani   ORCID: https://orcid.org/0000-0002-9063-6090 0 ,
• Takuya Urakami   ORCID: https://orcid.org/0000-0002-6859-4269 1 ,
• Eri Nakamura   ORCID: https://orcid.org/0000-0001-7029-6001 2

Graduate School of Business Administration, Kobe Universtiy, Kobe, Japan

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Faculty of Business Administration, Kindai University, Higashiosaka City, Japan

Graduate school of business administration, kobe university, kobe, japan.

• Empirically investigates performance, cost, demand, planning and management in public utilities in transportation
• Examines the effects, background, and mechanisms of public utilities operation and environment
• Explores future policy and governance in public utilities referring to general private industries

Part of the book series: Kobe University Monograph Series in Social Science Research (KUMSSSR)

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Table of contents (17 chapters)

Front matter, introduction.

Fumitoshi Mizutani

Public Utilities and Infrastructure

An analysis of the effect of demand response on electricity consumption: assessing households’ heterogeneous reaction to dynamic pricing.

• Fumitoshi Mizutani, Takuro Tanaka, Eri Nakamura

Estimation of Tap Water Demand in Japan: A Panel Data Analysis

• Noriyoshi Nakayama, Takuya Urakami

Economies of Scale and Consolidation Effects in the Japanese Sewerage Industry

• Tomoyasu Tanaka

Productivity Measurement and Decomposition of Total Factor Productivity in Japan’s Postal and Parcel Delivery Industry

• Shuji Uranishi

Preferential Slot Allocation for LCCs at a Congested Airport, and Airfare: The Case of Haneda Airport in Tokyo

• Jun Mizutani

The Impact of Airport Concession on Technical Efficiency: Evidence from Major Airports in Japan

• Koji Adachi

Transportation

Travel-based multitasking in japan between 2001 and 2016: descriptive analysis.

• Nobuhiro Sanko

Understanding the Relationship Between Daily Travel and Long-Term Subjective Well-Being

• Chikako Keumi

The Relationship Between Bicycle Accidents and Urban Structure in Cities in Japan

• Yusuke Suzuki

Brand Strategy in Airline Mergers: Simulation Examples with Delta and Northwest

• Ryohei Yamamoto

The Impact of Cooperation on the Multimarket Contact Effect in the U.S. Airline Industry

• Ryota Asahi

Governance and Policy

The japanese sewerage industry: institutional aspects and the governance systems.

Takuya Urakami

Evaluating the Institutional Reforms and Private Participation in Japanese Container Ports—Evidence from Multi-stage DEA Analysis

• Hiroki Sakai

Industrial Structure Change and Corporate Strategy: The Case of the Japanese Apparel Industry

Effects of sustainable development goals engagement on profit in public utilities: comparison with general private companies.

Eri Nakamura

• Public utilities
• Network industry
• Transportation economics
• Infrastructure industry
• Empirical analysis
• Policy and governance

About this book

Editors and affiliations, about the editors.

Fumitoshi Mizutani is a professor at the Graduate School of Business Administration, Kobe University, where he has worked since obtaining a Ph.D. from Harvard University in 1993. Currently he is serving as president of the Japan Society of Public Utility Economics and the Japan Society of Transport Economics. His main research has focused on the economic analysis of regulatory changes in public utility industries. He has also been involved in international research projects such as with the World Bank, and he has published in books and international journals such as Journal of Regulatory Economics, Empirical Economics, and Review of Network Economics . He has been awarded academic prizes.　

Takuya Urakami is a professor at the Faculty of Business Administration, Kindai University. His interest is in productivity, efficiency and performance analysis, especially of the water and sewerage industry. He currently serves on council committees forthe Ministry of Health, Labor and Welfare and the Ministry of Land, Infrastructure, Transport and Tourism as well as various municipal governments. He is now a research representative for Kindai University, cooperating with the Centre for Productivity and Performance of Loughborough University, UK. He has published in international journals such as European Journal of Operational Research, Urban Studies, and Papers in Regional Science.

Eri Nakamura has been an associate professor at the Graduate School of Business Administration, Kobe University, since 2014. After obtaining her Ph.D. from Kobe University in 2011, she worked as a lecturer at Shinshu University until 2013. She was a visiting researcher at Vienna University of Economics and Business from 2016 to 2018. She has been engaged in research on efficiency, organizational management, governance and corporate social responsibility in public utilities as well as general private companies. She has published in international journals such as Utilities Policy, Journal of Business and Industrial Economics, and Journal of Management and Governance.

Bibliographic Information

Book Title : Current Issues in Public Utilities and Public Policy

Book Subtitle : Empirical Studies Focusing on Japan

Editors : Fumitoshi Mizutani, Takuya Urakami, Eri Nakamura

Series Title : Kobe University Monograph Series in Social Science Research

DOI : https://doi.org/10.1007/978-981-19-7489-2

Publisher : Springer Singapore

eBook Packages : Economics and Finance , Economics and Finance (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023

Hardcover ISBN : 978-981-19-7488-5 Published: 26 January 2023

Softcover ISBN : 978-981-19-7491-5 Published: 27 January 2024

eBook ISBN : 978-981-19-7489-2 Published: 25 January 2023

Series ISSN : 2524-504X

Series E-ISSN : 2524-5058

Edition Number : 1

Number of Pages : XVII, 351

Number of Illustrations : 20 b/w illustrations, 14 illustrations in colour

Topics : Urban Economics , Industrial Organization , Industries , Economic Policy , Economic Policy , Industries

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• Published: 18 April 2024

Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

• James Shaw 1 , 13 ,
• Joseph Ali 2 , 3 ,
• Caesar A. Atuire 4 , 5 ,
• Phaik Yeong Cheah 6 ,
• Armando Guio Español 7 ,
• Judy Wawira Gichoya 8 ,
• Adrienne Hunt 9 ,
• Daudi Jjingo 10 ,
• Katherine Littler 9 ,
• Daniela Paolotti 11 &
• Effy Vayena 12  

BMC Medical Ethics volume  25 , Article number:  46 ( 2024 ) Cite this article

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The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

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Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table  1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table  2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

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Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

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JS led the writing, contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. CA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. PYC contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AE contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JWG contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AH contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DJ contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. KL contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DP contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. EV contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper.

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Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

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Immigration's Effect on US Wages and Employment Redux

In this article we revive, extend and improve the approach used in a series of influential papers written in the 2000s to estimate how changes in the supply of immigrant workers affected natives' wages in the US. We begin by extending the analysis to include the more recent years 2000-2022. Additionally, we introduce three important improvements. First, we introduce an IV that uses a new skill-based shift-share for immigrants and the demographic evolution for natives, which we show passes validity tests and has reasonably strong power. Second, we provide estimates of the impact of immigration on the employment-population ratio of natives to test for crowding out at the national level. Third, we analyze occupational upgrading of natives in response to immigrants. Using these estimates, we calculate that immigration, thanks to native-immigrant complementarity and college skill content of immigrants, had a positive and significant effect between +1.7 to +2.6\% on wages of less educated native workers, over the period 2000-2019 and no significant wage effect on college educated natives. We also calculate a positive employment rate effect for most native workers. Even simulations for the most recent 2019-2022 period suggest small positive effects on wages of non-college natives and no significant crowding out effects on employment.

We are grateful for Rebecca Brough for her research assistance and suggestions. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

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2024 Graduation Stories: Alison Pei

She researches labor market dynamics and innovation, share this story.

Since joining the Sanford community in 2019, Alison Pei PhD'24 has dedicated her research to the relationship between labor market dynamics and innovation, with a focus on the impact of regulations on worker outcomes.

Her work "Monopsony in the High-Skilled Migrant Labor Market: Evidence from H-1B Petition Data," co-authored with fellow Ph.D. Candidate in Economics, Seohee Kim, has contributed to the current heated debate on the topic of monopsony (a situation where there's only one buyer but many sellers, which gives the buyer a lot of control over prices for goods or services) in both policy and research circles. Their study explored wage effects of employer concentration on college-educated foreign nationals working in specialized roles within the U.S.

More recently, Alison worked on a year-long research grant awarded by the Alfred P. Sloan Foundation, collaborating with Dr. Matthew Johnson, a professor at Sanford. The research, which explored innovation and noncompete agreements was cited in the U.S. Federal Trade Commission's ruling on April 23rd, announcing a ban of non-compete agreements.

Following graduation, Alison plans on relocating to pursue a career aligned with her expertise at a leading economic consulting firm in Boston, with a focus on field of antitrust and competition.

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How have your childhood experiences shaped who you are today.

I grew up in Huzhou, Zhejiang, a small town on the east coast of China that has picturesque natural scenery and abundant cultural heritage. Despite being a historical product of China's one-child policy, my childhood mirrored that of countless others across the nation, mostly revolving around school. Despite being born shy and introverted, I credit my parents with instilling in me the courage to pursue my dream, even if it meant being thousands of miles away from home.

Picture this: as a kid, my dad and I played this wild game where he'd egg me on to jump off high stairs, swooping in like a superhero to catch me at the bottom. Then there were those epic moments atop construction sites with my dad, where we'd gaze out at the world below. It was exhilarating, teaching me to trust and embrace risks.

As I entered junior high, my mom encouraged me to step forward, take on leadership roles, and participate in public speaking contests. These experiences helped me become bolder and more comfortable stepping out of my comfort zone.

Can you share a proud sanford moment?

One of my proudest moments occurred recently, on April 23rd, 2024, when the Federal Tarade Commission (FTC) made a landmark decision to ban noncompete clauses. What made it even more special was receiving an email from the FTC stating that our research paper, " Innovation and the Enforceability of Noncompete Agreements " (co-written with Matthew S. Johnson and Michael A. Lipsitz), was cited in the final rule. It was a poignant realization that our academic work had a tangible impact on shaping real-world policy changes.

What's a meaningful moment with a Sanford mentor?

The journey to this moment kicked off back in my second year when we blended my co-authors' expertise in noncompete agreements with my budding interest in innovation. I was treated as a collaborator, despite my relative lack of research experience compared to them. They kept encouraging me to voice my thoughts and contribute ideas as if I were a seasoned researcher. Their trust and support fueled my growth as we dove deep into the project alongside these brilliant minds.

Now, seeing our work make waves in the actual ruling fills me with an incredible sense of pride and accomplishment. It's a testament to the impact research can have in driving real change. This experience only stokes my passion for continuing to navigate the waters where research, policymaking, and the real world collide.

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Ellen Gibbs

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Before her career at Duke, she was a Relationship Manager Assistant for a franchise development agency located in Denver. She also served her alma mater as its Social Media Manager of the Department of Housing. Ellen supports Sanford by streamlining information, communication, events, and financial support.

Aside from her work, Ellen actively volunteers with the Humane Society of the United States and works on her art.

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Project 2025 and the Heritage Foundation have plans to defund public broadcasting

Project 2025 contributor and Heritage Foundation senior fellow Mike Gonzalez has been railing against public media since at least 2017

Written by Sophie Lawton

Published 04/26/24 12:27 PM EDT

Project 2025 and the right-wing think tank the Heritage Foundation have a plan for a future Republican administration to defund “woke” public media institutions, including PBS and NPR.

Last year, Project 2025 , a comprehensive transition plan organized by the Heritage Foundation, released a nearly 900-page policy book titled Mandate for Leadership: A Conservative Promise . The book outlines a radical set of policy proposals that would dismantle the civil service, outlaw abortion, and roll back civil rights. (The effort is backed by over 100 conservative partner organizations and has tied itself to former President Donald Trump’s presidential campaign.)

One chapter, written by Heritage Foundation senior fellow Mike Gonzalez, lays out a plan for how a Republican administration might defund the Corporation for Public Broadcasting, which uses taxpayer dollars to help fund public media institutions like PBS and NPR.

In his chapter on public broadcasting, Gonzalez claimed “ all Republican presidents have recognized that public funding of domestic broadcasts is a mistake” and “the next conservative President must” defund public media “and do it despite opposition from congressional members of his own party if necessary.”

In one passage, Gonzalez argued that “the government should not be compelling the conservative half of the country to pay for the suppression of its own views.” He claimed:

Not only is the federal government trillions of dollars in debt and unable to afford the more than half a billion dollars squandered on leftist opinion each year, but the government should not be compelling the conservative half of the country to pay for the suppression of its own views. As Thomas Jefferson put it, “To compel a man to furnish contributions of money for the propagations of opinions which he disbelieves and abhors, is sinful and tyrannical.”

The chapter then outlines how a Republican president could defund public media by refusing to sign any spending bill that earmarks “a penny for the CPB”:

The 47th President can just tell the Congress—through the budget he proposes and through personal contact—that he will not sign an appropriations spending bill that contains a penny for the CPB. The President may have to use the bully pulpit, as NPR and PBS have teams of lobbyists who have convinced enough Members of Congress to save their bacon every time their taxpayer subsidies have been at risk since the Nixon era.

Gonzalez also supported his argument by citing a Pew Research study that showed the PBS viewers identify as “mostly liberal.”

“That may be an acceptable business model for MSNBC or CNN, but not for a taxpayer-subsidized broadcaster,” he wrote.

Gonzalez and the Heritage Foundation have advocated for defunding public media for years — and those attacks have only increased

The Project 2025 policy book wasn’t the first time Gonzalez railed against public media. Gonzalez has been calling on Congress to defund public media since at least 2017 . Those attacks have become more frequent in recent months.

In various op-eds, which later published on the Heritage Foundation’s website, Gonzalez has argued for defunding public media, which he has claimed only serve to amplify “the woke mindset of bi-coastal elites.”

In an April 23 column titled “The Next GOP President Should Defund Woke Public Broadcasting,” Gonzalez wrote that “it is difficult to see” how taxpayer funding for public broadcasting “survives” if Republicans win the House, Senate, and White House in a future election.

“Many of us have long known that there was a serious problem with a taxpayer-funded programming system that ignored half the country,” he wrote. “They thought they could take money from everyone, but only reflect, and respect, the thinking of a woke minority.”

Gonzalez also characterized NPR’s new CEO, Katherine Maher, as “the poster child of everything wrong with the CPB,” suggesting  that Maher’s “very progressive public statements” — which seemingly include social media posts criticizing former Trump and expressing support for Black Lives Matter — indicated she has a “a very conscious bias toward all things Left.”

When Heritage shared Gonzalez’s article on X, the caption simply stated: “Defund NPR."

In January, Gonzalez made similar arguments in an article titled “Taxpayers Shouldn’t Have To Fund Biased, Woke Public Broadcasting.”

In that article, Gonzalez criticized NPR for “amplifying the woke mindset of bi-coastal elites,” which he characterized as “the ‘alternative facts’ of the parasitic critical Marxist orthodoxy that, for the past three or four decades, has invaded the host of American cultural institutions.”

After a former NPR editor criticized his former employer for cultivating a supposed liberal bias at the organization, Trump himself, whose previous administration proposed slashing public media funding, opined on the matter.

In an all-caps Truth Social post, the GOP presidential candidate wrote: “NO MORE FUNDING FOR NPR, A TOTAL SCAM!

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the war for talent goes on —

Apple poaches ai experts from google, creates secretive european ai lab, at least 36 former googlers now work on ai for apple..

Michael Acton, Financial Times - Apr 30, 2024 2:16 pm UTC

Apple has poached dozens of artificial intelligence experts from Google and has created a secretive European laboratory in Zurich, as the tech giant builds a team to battle rivals in developing new AI models and products.

According to a Financial Times analysis of hundreds of LinkedIn profiles as well as public job postings and research papers, the $2.7 trillion company has undertaken a hiring spree over recent years to expand its global AI and machine learning team.

The iPhone maker has particularly targeted workers from Google, attracting at least 36 specialists from its rival since it poached John Giannandrea to be its top AI executive in 2018.

While the majority of Apple’s AI team work from offices in California and Seattle, the tech group has also expanded a significant outpost in Zurich.

Professor Luc Van Gool from Swiss university ETH Zurich said Apple’s acquisitions of two local AI startups—virtual reality group FaceShift and image recognition company Fashwell—led Apple to build a research laboratory, known as its “Vision Lab,” in the city.

Zurich-based employees have been involved in Apple’s research into the underlying technology that powers products such as OpenAI’s ChatGPT chatbot. Their papers have focused on ever more advanced AI models that incorporate text and visual inputs to produce responses to queries.

The company has been advertising jobs in generative AI across two locations in Zurich, one of which has a particularly low profile. A neighbor told the FT they were not even aware of the office’s existence. Apple did not respond to requests to comment.

Apple has been typically tight-lipped about its AI plans even as big tech rivals Microsoft, Google, and Amazon tout multibillion-dollar investments in cutting-edge technology.

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Public Health Showcase celebrates policy success stories, students’ research

More than 100 poster abstracts demonstrating a diverse array of public health topics were featured at the 2024 Public Health Showcase.   The event, sponsored by the University of Kentucky College of Public Health, provided an invaluable platform for sharing research, innovative community practices, transformative teaching methodologies, and strategies for workforce development within the realm of public health.

The Public Health Showcase attracted a diverse audience, including University of Kentucky undergraduate and graduate students, faculty, staff, community members, public health practitioners, health insurers, healthcare organizations, community organizations, and state agencies and organizations invested in advancing population health in the Commonwealth of Kentucky and beyond.

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Undergraduate Practice and Service

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• Julia Heller, “Exploration Of Income-Based Factors Association With Persistent Deficits From Birth” 

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• Qi Yan, “Advancing Salivary Biomarker Development with Logistic Regression - Machine Learning”
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In addition,  Dr. Rachel Hogg-Graham ,  Associate Professorin the Health Management and Policy department at CPH, was recognized as Outstanding Faculty Mentor for being the main mentor on seven student research posters.

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