Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril

Curated on Posted on by Stefaan Verhulst

Special Publication by the National Academy of Medicine (NAM): “The emergence of artificial intelligence (AI) in health care offers unprecedented opportunities to improve patient and clinical team outcomes, reduce costs, and impact population health. While there have been a number of promising examples of AI applications in health care, it is imperative to proceed with caution or risk the potential of user disillusionment, another AI winter, or further exacerbation of existing health- and technology-driven disparities.

This Special Publication synthesizes current knowledge to offer a reference document for relevant health care stakeholders. It outlines the current and near-term AI solutions; highlights the challenges, limitations, and best practices for AI development, adoption, and maintenance; offers an overview of the legal and regulatory landscape for AI tools designed for health care application; prioritizes the need for equity, inclusion, and a human rights lens for this work; and outlines key considerations for moving forward.

AI is poised to make transformative and disruptive advances in health care, but it is prudent to balance the need for thoughtful, inclusive health care AI that plans for and actively manages and reduces potential unintended consequences, while not yielding to marketing hype and profit motives…(More)”

Primer on Data Sharing

Curated on Posted on by Stefaan Verhulst

Primer by John Ure: “…encapsulates insights gleaned from the Inter-Modal Transport Data Sharing Programme, a collaborative effort known as Data Trust 1.0 (DT1), conducted in Hong Kong between 2020 and 2021. This initiative was a pioneering project that explored the feasibility of sharing operational data between public transport entities through a Trusted Third Party. The objective was to overcome traditional data silos and promote evidence-based public transport planning.

DT1, led by the ‘HK Team’ in conjunction with Dr. Jiangping Zhou and colleagues from the University of Hong Kong, successfully demonstrated that data sharing between public transport companies, both privately-owned and government-owned, was viable. Operational data, anonymised and encrypted, were shared with a Trusted Third Party and aggregated for analysis, supported by a Transport Data Analytics Service Provider. The data was used solely for analysis purposes, and confidentiality was maintained throughout.

The establishment of the Data Trust was underpinned by the creation of a comprehensive Data Sharing Framework (DSF). This framework, developed collaboratively, laid the groundwork for future data sharing endeavours. The DSF has been shared internationally, fostering the exchange of knowledge and best practices across diverse organisations and agencies. The Guide serves as a repository of lessons learned, accessible studies, and references, aimed at facilitating a comprehensive understanding of data sharing methodologies.

The central aim of the Guide is twofold: to promote self-learning and to offer clarity on intricate approaches related to data sharing. Its intention is to encourage researchers, governmental bodies, commercial enterprises, and civil society entities, including NGOs, to actively engage in data sharing endeavours. By combining data sets, these stakeholders can glean enhanced insights and contribute to the common good…(More)”.

The Case Against AI Everything, Everywhere, All at Once

Curated on Posted on by Stefaan Verhulst

Essay by Judy Estrin: “The very fact that the evolution of technology feels so inevitable is evidence of an act of manipulation, an authoritarian use of narrative brilliantly described by historian Timothy Snyder. He calls out the politics of inevitability “...a sense that the future is just more of the present, … that there are no alternatives, and therefore nothing really to be done.” There is no discussion of underlying values. Facts that don’t fit the narrative are disregarded.

Here in Silicon Valley, this top-down authoritarian technique is amplified by a bottom-up culture of inevitability. An orchestrated frenzy begins when the next big thing to fuel the Valley’s economic and innovation ecosystem is heralded by companies, investors, media, and influencers.

They surround us with language coopted from common values—democratization, creativity, open, safe. In behavioral psych classes, product designers are taught to eliminate friction—removing any resistance to us to acting on impulse.

The promise of short-term efficiency, convenience, and productivity lures us. Any semblance of pushback is decried as ignorance, or a threat to global competition. No one wants to be called a Luddite. Tech leaders, seeking to look concerned about the public interest, call for limited, friendly regulation, and the process moves forward until the tech is fully enmeshed in our society.

We bought into this narrative before, when social media, smartphones and cloud computing came on the scene. We didn’t question whether the only way to build community, find like-minded people, or be heard, was through one enormous “town square,” rife with behavioral manipulation, pernicious algorithmic feeds, amplification of pre-existing bias, and the pursuit of likes and follows.

It’s now obvious that it was a path towards polarization, toxicity of conversation, and societal disruption. Big Tech was the main beneficiary as industries and institutions jumped on board, accelerating their own disruption, and civic leaders were focused on how to use these new tools to grow their brands and not on helping us understand the risks.

We are at the same juncture now with AI…(More)”.

AI By the People, For the People

Curated on Posted on by Stefaan Verhulst

Article by Billy Perrigo/Karnataka: “…To create an effective English-speaking AI, it is enough to simply collect data from where it has already accumulated. But for languages like Kannada, you need to go out and find more.

This has created huge demand for datasets—collections of text or voice data—in languages spoken by some of the poorest people in the world. Part of that demand comes from tech companies seeking to build out their AI tools. Another big chunk comes from academia and governments, especially in India, where English and Hindi have long held outsize precedence in a nation of some 1.4 billion people with 22 official languages and at least 780 more indigenous ones. This rising demand means that hundreds of millions of Indians are suddenly in control of a scarce and newly-valuable asset: their mother tongue.

Data work—creating or refining the raw material at the heart of AI— is not new in India. The economy that did so much to turn call centers and garment factories into engines of productivity at the end of the 20th century has quietly been doing the same with data work in the 21st. And, like its predecessors, the industry is once again dominated by labor arbitrage companies, which pay wages close to the legal minimum even as they sell data to foreign clients for a hefty mark-up. The AI data sector, worth over $2 billion globally in 2022, is projected to rise in value to $17 billion by 2030. Little of that money has flowed down to data workers in India, Kenya, and the Philippines.

These conditions may cause harms far beyond the lives of individual workers. “We’re talking about systems that are impacting our whole society, and workers who make those systems more reliable and less biased,” says Jonas Valente, an expert in digital work platforms at Oxford University’s Internet Institute. “If you have workers with basic rights who are more empowered, I believe that the outcome—the technological system—will have a better quality as well.”

In the neighboring villages of Alahalli and Chilukavadi, one Indian startup is testing a new model. Chandrika works for Karya, a nonprofit launched in 2021 in Bengaluru (formerly Bangalore) that bills itself as “the world’s first ethical data company.” Like its competitors, it sells data to big tech companies and other clients at the market rate. But instead of keeping much of that cash as profit, it covers its costs and funnels the rest toward the rural poor in India. (Karya partners with local NGOs to ensure access to its jobs go first to the poorest of the poor, as well as historically marginalized communities.) In addition to its $5 hourly minimum, Karya gives workers de-facto ownership of the data they create on the job, so whenever it is resold, the workers receive the proceeds on top of their past wages. It’s a model that doesn’t exist anywhere else in the industry…(More)”.

Interested but Uncertain: Carbon Markets and Data Sharing among U.S. Crop Farmers

Curated on Posted on by Stefaan Verhulst

Paper by Guang Han and Meredith T. Niles: “The potential for farmers and agriculture to sequester carbon and contribute to global climate change goals is widely discussed. However, there is currently low participation in agricultural carbon markets and a limited understanding of farmer perceptions and willingness to participate. Furthermore, farmers’ concerns regarding data privacy may complicate participation in agricultural carbon markets, which necessitates farmer data sharing with multiple entities. This study aims to address research gaps by assessing farmers’ willingness to participate in agricultural carbon markets, identifying the determinants of farmers’ willingness regarding carbon markets participation, and exploring how farmers’ concerns for data privacy relate to potential participation in agricultural carbon markets. Data were collected through a multistate survey of 246 farmers and analyzed using descriptive statistics, factor analysis, and multinomial regression models. We find that the majority of farmers (71.8%) are aware of carbon markets and would like to sell carbon credits, but they express high uncertainty about carbon market information, policies, markets, and cost impacts. Just over half of farmers indicated they would share their data for education, developing tools and models, and improving markets and supply chains. Farmers who wanted to participate in carbon markets were more likely to have higher farm revenues, more likely to share their data overall, more likely to share their data with private organizations, and more likely to change farming practices and had more positive perceptions of the impact of carbon markets on farm profitability. In conclusion, farmers have a general interest in carbon market participation, but more information is needed to address their uncertainties and concerns…(More)”.

Corporate Responsibility in the Age of AI

Curated on Posted on by Stefaan Verhulst

Essay by Maria Eitel: “In the past year, a cacophony of conversations about artificial intelligence has erupted. Depending on whom you listen to, AI is either carrying us into a shiny new world of endless possibilities or propelling us toward a grim dystopia. Call them the Barbie and Oppenheimer scenarios – as attention-grabbing and different as the Hollywood blockbusters of the summer. But one conversation is getting far too little attention: the one about corporate responsibility.

I joined Nike as its first Vice President of Corporate Responsibility in 1998, landing right in the middle of the hyper-globalization era’s biggest corporate crisis: the iconic sports and fitness company had become the face of labor exploitation in developing countries. In dealing with that crisis and setting up corporate responsibility for Nike, we learned hard-earned lessons, which can now help guide our efforts to navigate the AI revolution.

There is a key difference today. Taking place in the late 1990s, the Nike drama played out relatively slowly. When it comes to AI, however, we don’t have the luxury of time. This time last year, most people had not heard about generative AI. The technology entered our collective awareness like a lightning strike in late 2022, and we have been trying to make sense of it ever since…

Our collective future now hinges on whether companies – in the privacy of their board rooms, executive meetings, and closed-door strategy sessions – decide to do what is right. Companies need a clear North Star to which they can always refer as they pursue innovation. Google had it right in its early days, when its corporate credo was, “Don’t Be Evil.” No corporation should knowingly harm people in the pursuit of profit.

It will not be enough for companies simply to say that they have hired former regulators and propose possible solutions. Companies must devise credible and effective AI action plans that answer five key questions:

  • What are the potential unanticipated consequences of AI?
  • How are you mitigating each identified risk?
  • What measures can regulators use to monitor companies’ efforts to mitigate potential dangers and hold them accountable?
  • What resources do regulators need to carry out this task?
  • How will we know that the guardrails are working?

The AI challenge needs to be treated like any other corporate sprint. Requiring companies to commit to an action plan in 90 days is reasonable and realistic. No excuses. Missed deadlines should result in painful fines. The plan doesn’t have to be perfect – and it will likely need to be adapted as we continue to learn – but committing to it is essential…(More)”.

Creating public sector value through the use of open data

Curated on Posted on by Stefaan Verhulst

Summary paper prepared as part of data.europa.eu: “This summary paper provides an overview of the different stakeholder activities undertaken, ranging from surveys to a focus group, and presents the key insights from this campaign regarding data reuse practices, barriers to data reuse in the public sector and suggestions to overcome these barriers. The following recommendations are made to help data.europa.eu support public administrations to boost open data value creation.

  • When it comes to raising awareness and communication, any action should also contain examples of data reuse by the public sector. Gathering and communicating such examples and use cases greatly helps in understanding the importance of the role of the public sector as a data reuser
  • When it comes to policy and regulation, it would be beneficial to align the ‘better regulation’ activities and roadmaps of the European Commission with the open data publication activities, in order to better explore the internal data needs. Furthermore, it would be helpful to facilitate a similar alignment and data needs analysis for all European public administrations. For example, this could be done by providing examples, best practices and methodologies on how to map data needs for policy and regulatory purposes.
  • Existing monitoring activities, such as surveys, should be revised to ensure that data reuse by the public sector is included. It would be useful to create a panel of users, based on the existing wide community, that could be used for further surveys.
  • The role of data stewards remains central to favouring reuse. Therefore, examples, best practices and methodologies on the role of data stewards should be included in the support activities – not specifically for public sector reusers, but in general…(More)”.

The GPTJudge: Justice in a Generative AI World

Curated on Posted on by Stefaan Verhulst

Paper by Grossman, Maura and Grimm, Paul and Brown, Dan and Xu, Molly: “Generative AI (“GenAI”) systems such as ChatGPT recently have developed to the point where they are capable of producing computer-generated text and images that are difficult to differentiate from human-generated text and images. Similarly, evidentiary materials such as documents, videos and audio recordings that are AI-generated are becoming increasingly difficult to differentiate from those that are not AI-generated. These technological advancements present significant challenges to parties, their counsel, and the courts in determining whether evidence is authentic or fake. Moreover, the explosive proliferation and use of GenAI applications raises concerns about whether litigation costs will dramatically increase as parties are forced to hire forensic experts to address AI- generated evidence, the ability of juries to discern authentic from fake evidence, and whether GenAI will overwhelm the courts with AI-generated lawsuits, whether vexatious or otherwise. GenAI systems have the potential to challenge existing substantive intellectual property (“IP”) law by producing content that is machine, not human, generated, but that also relies on human-generated content in potentially infringing ways. Finally, GenAI threatens to alter the way in which lawyers litigate and judges decide cases.

This article discusses these issues, and offers a comprehensive, yet understandable, explanation of what GenAI is and how it functions. It explores evidentiary issues that must be addressed by the bench and bar to determine whether actual or asserted (i.e., deepfake) GenAI output should be admitted as evidence in civil and criminal trials. Importantly, it offers practical, step-by- step recommendations for courts and attorneys to follow in meeting the evidentiary challenges posed by GenAI. Finally, it highlights additional impacts that GenAI evidence may have on the development of substantive IP law, and its potential impact on what the future may hold for litigating cases in a GenAI world…(More)”.

Philosophy of Open Science

Curated on Posted on by Stefaan Verhulst

Book by Sabina Leonelli: “The Open Science [OS] movement aims to foster the wide dissemination, scrutiny and re-use of research components for the good of science and society. This Element examines the role played by OS principles and practices within contemporary research and how this relates to the epistemology of science. After reviewing some of the concerns that have prompted calls for more openness, it highlights how the interpretation of openness as the sharing of resources, so often encountered in OS initiatives and policies, may have the unwanted effect of constraining epistemic diversity and worsening epistemic injustice, resulting in unreliable and unethical scientific knowledge. By contrast, this Element proposes to frame openness as the effort to establish judicious connections among systems of practice, predicated on a process-oriented view of research as a tool for effective and responsible agency…(More)”.

Data Collaboratives: Enabling a Healthy Data Economy Through Partnerships

Curated on Posted on by Stefaan Verhulst

Paper by Stefaan Verhulst (as Part of the Digital Revolution and New Social Contract Program): “…Overcoming data silos is key to addressing these data asymmetries and promoting a healthy data economy. This is equally true of silos that exist within sectors as it is of those among sectors (e.g., between the public and private sectors). Today, there is a critical mismatch between data supply and demand. The data that could be most useful rarely gets applied to the social, economic, cultural, and political problems it could help solve. Data silos, driven in large part by deeply entrenched asymmetries and a growing sense of “ownership,” are stunting the public good potential of data.

This paper presents a framework for responsible data sharing and reuse that could increase sharing between the public and private sectors to address some of the most entrenched asymmetries. Drawing on theoretical and empirical material, we begin by outlining how a period of rapid datafication—the Era of the Zettabyte—has led to data asymmetries that are increasingly deleterious to the public good. Sections II and III are normative. Having outlined the nature and scope of the problem, we present a number of steps and recommendations that could help overcome or mitigate data asymmetries. In particular, we focus on one institutional structure that has proven particularly promising: data collaboratives, an emerging model for data sharing between sectors. We show how data collaboratives could ease the flow of data between the public and private sectors, helping break down silos and ease asymmetries. Section II offers a conceptual overview of data collaboratives, while Section III provides an approach to operationalizing data collaboratives. It presents a number of specific mechanisms to build a trusted sharing ecology….(More)”.