Data and density: Two tools to boost health equity in cities

Curated on Posted on by Stefaan Verhulst

Article by Ann Aerts and Diana Rodríguez Franco: “Improving health and health equity for vulnerable populations requires addressing the social determinants of health. In the US, it is estimated that medical care only accounts for 10-20% of health outcomes while social determinants like education and income account for the remaining 80-90%.

Place-based interventions, however, are showing promise for improving health outcomes despite persistent inequalities. Research and practice increasingly point to the role of cities in promoting health equity — or reversing health inequities — as 56% of the global population lives in cities, and several social determinants of health are directly tied to urban factors like opportunity, environmental health, neighbourhoods and physical environments, access to food and more.

Thus, it is critical to identify both true drivers of good health and poor health outcomes so that underserved populations can be better served.

Place-based strategies can address health inequities and lead to meaningful improvements for vulnerable populations…

Initial data analysis revealed a strong correlation between cardiovascular disease risk in city residents and social determinants such as higher education, commuting time, access to Medicaid, rental costs and internet access.

Understanding which data points are correlated with health risks is key to effectively tailoring interventions.

Determined to reverse this trend, city authorities have launched a “HealthyNYC” campaign and are working with the Novartis Foundation to uncover the behavioural and social determinants behind non-communicable diseases (NCDs) (e.g. diabetes and cardiovascular disease), which cause 87% of all deaths in New York City…(More)”

Computing Power and the Governance of AI

Curated on Posted on by Stefaan Verhulst

Blog by Lennart Heim, Markus Anderljung, Emma Bluemke, and Robert Trager: “Computing power – compute for short – is a key driver of AI progress. Over the past thirteen years, the amount of compute used to train leading AI systems has increased by a factor of 350 million. This has enabled the major AI advances that have recently gained global attention.

‍Governments have taken notice. They are increasingly engaged in compute governance: using compute as a lever to pursue AI policy goals, such as limiting misuse risks, supporting domestic industries, or engaging in geopolitical competition. 

‍There are at least three ways compute can be used to govern AI. Governments can: 

  • Track or monitor compute to gain visibility into AI development and use
  • Subsidize or limit access to compute to shape the allocation of resources across AI projects
  • Monitor activity, limit access, or build “guardrails” into hardware to enforce rules

Compute governance is a particularly important approach to AI governance because it is feasible. Compute is detectable: training advanced AI systems requires tens of thousands of highly advanced AI chips, which cannot be acquired or used inconspicuously. It is excludable: AI chips, being physical goods, can be given to or taken away from specific actors and in cases of specific uses. And it is quantifiable: chips, their features, and their usage can be measured. Compute’s detectability and excludability are further enhanced by the highly concentrated structure of the AI supply chain: very few companies are capable of producing the tools needed to design advanced chips, the machines needed to make them, or the data centers that house them. 

‍However, just because compute can be used as a tool to govern AI doesn’t mean that it should be used in all cases. Compute governance is a double-edged sword, with both potential benefits and the risk of negative consequences: it can support widely shared goals like safety, but it can also be used to infringe on civil liberties, perpetuate existing power structures, and entrench authoritarian regimes. Indeed, some things are better ungoverned. 

‍In our paper we argue that compute is a particularly promising node for AI governance. We also highlight the risks of compute governance and offer suggestions for how to mitigate them. This post summarizes our findings and key takeaways, while also offering some of our own commentary…(More)”

AI is too important to be monopolised

Curated on Posted on by Stefaan Verhulst

Article by Marietje Schaake: “…From the promise of medical breakthroughs to the perils of election interference, the hopes of helpful climate research to the challenge of cracking fundamental physics, AI is too important to be monopolised.

Yet the market is moving in exactly that direction, as resources and talent to develop the most advanced AI sit firmly in the hands of a very small number of companies. That is particularly true for resource-intensive data and computing power (termed “compute”), which are required to train large language models for a variety of AI applications. Researchers and small and medium-sized enterprises risk fatal dependency on Big Tech once again, or else they will miss out on the latest wave of innovation. 

On both sides of the Atlantic, feverish public investments are being made in an attempt to level the computational playing field. To ensure scientists have access to capacities comparable to those of Silicon Valley giants, the US government established the National AI Research Resource last month. This pilot project is being led by the US National Science Foundation. By working with 10 other federal agencies and 25 civil society groups, it will facilitate government-funded data and compute to help the research and education community build and understand AI. 

The EU set up a decentralised network of supercomputers with a similar aim back in 2018, before the recent wave of generative AI created a new sense of urgency. The EuroHPC has lived in relative obscurity and the initiative appears to have been under-exploited. As European Commission president Ursula von der Leyen said late last year: we need to put this power to use. The EU now imagines that democratised supercomputer access can also help with the creation of “AI factories,” where small businesses pool their resources to develop new cutting-edge models. 

There has long been talk of considering access to the internet a public utility, because of how important it is for education, employment and acquiring information. Yet rules to that end were never adopted. But with the unlocking of compute as a shared good, the US and the EU are showing real willingness to make investments into public digital infrastructure.

Even if the latest measures are viewed as industrial policy in a new jacket, they are part of a long overdue step to shape the digital market and offset the outsized power of big tech companies in various corners of our societies…(More)”.

Toward a 21st Century National Data Infrastructure: Managing Privacy and Confidentiality Risks with Blended Data

Curated on Posted on by Stefaan Verhulst

Report by the National Academies of Sciences, Engineering, and Medicine: “Protecting privacy and ensuring confidentiality in data is a critical component of modernizing our national data infrastructure. The use of blended data – combining previously collected data sources – presents new considerations for responsible data stewardship. Toward a 21st Century National Data Infrastructure: Managing Privacy and Confidentiality Risks with Blended Data provides a framework for managing disclosure risks that accounts for the unique attributes of blended data and poses a series of questions to guide considered decision-making.

Technical approaches to manage disclosure risk have advanced. Recent federal legislation, regulation and guidance has described broadly the roles and responsibilities for stewardship of blended data. The report, drawing from the panel review of both technical and policy approaches, addresses these emerging opportunities and the new challenges and responsibilities they present. The report underscores that trade-offs in disclosure risks, disclosure harms, and data usefulness are unavoidable and are central considerations when planning data-release strategies, particularly for blended data…(More)”.

Enabling Data-Driven Innovation : Learning from Korea’s Data Policies and Practices for Harnessing AI 

Curated on Posted on by Stefaan Verhulst

Report by the World Bank: “Over the past few decades, the Republic of Korea has consciously undertaken initiatives to transform its economy into a competitive, data-driven system. The primary objectives of this transition were to stimulate economic growth and job creation, enhance the nation’s capacity to withstand adversities such as the aftermath of COVID-19, and position it favorably to capitalize on emerging technologies, particularly artificial intelligence (AI). The Korean government has endeavored to accomplish these objectives through establishing a dependable digital data infrastructure and a comprehensive set of national data policies. This policy note aims to present a comprehensive synopsis of Korea’s extensive efforts to establish a robust digital data infrastructure and utilize data as a key driver for innovation and economic growth. The note additionally addresses the fundamental elements required to realize these benefits of data, including data policies, data governance, and data infrastructure. Furthermore, the note highlights some key results of Korea’s data policies, including the expansion of public data opening, the development of big data platforms, and the growth of the AI Hub. It also mentions the characteristics and success factors of Korea’s data policy, such as government support and the reorganization of institutional infrastructures. However, it acknowledges that there are still challenges to overcome, such as in data collection and utilization as well as transitioning from a government-led to a market-friendly data policy. The note concludes by providing developing countries and emerging economies with specific insights derived from Korea’s forward-thinking policy making that can assist them in harnessing the potential and benefits of data…(More)”.

Applying AI to Rebuild Middle Class Jobs

Curated on Posted on by Stefaan Verhulst

Paper by David Autor: “While the utopian vision of the current Information Age was that computerization would flatten economic hierarchies by democratizing information, the opposite has occurred. Information, it turns out, is merely an input into a more consequential economic function, decision-making, which is the province of elite experts. The unique opportunity that AI offers to the labor market is to extend the relevance, reach, and value of human expertise. Because of AI’s capacity to weave information and rules with acquired experience to support decision-making, it can be applied to enable a larger set of workers possessing complementary knowledge to perform some of the higher-stakes decision-making tasks that are currently arrogated to elite experts, e.g., medical care to doctors, document production to lawyers, software coding to computer engineers, and undergraduate education to professors. My thesis is not a forecast but an argument about what is possible: AI, if used well, can assist with restoring the middle-skill, middle-class heart of the US labor market that has been hollowed out by automation and globalization…(More)”.

AI cannot be used to deny health care coverage, feds clarify to insurers

Curated on Posted on by Stefaan Verhulst

Article by Beth Mole: “Health insurance companies cannot use algorithms or artificial intelligence to determine care or deny coverage to members on Medicare Advantage plans, the Centers for Medicare & Medicaid Services (CMS) clarified in a memo sent to all Medicare Advantage insurers.

The memo—formatted like an FAQ on Medicare Advantage (MA) plan rules—comes just months after patients filed lawsuits claiming that UnitedHealth and Humana have been using a deeply flawed AI-powered tool to deny care to elderly patients on MA plans. The lawsuits, which seek class-action status, center on the same AI tool, called nH Predict, used by both insurers and developed by NaviHealth, a UnitedHealth subsidiary.

According to the lawsuits, nH Predict produces draconian estimates for how long a patient will need post-acute care in facilities like skilled nursing homes and rehabilitation centers after an acute injury, illness, or event, like a fall or a stroke. And NaviHealth employees face discipline for deviating from the estimates, even though they often don’t match prescribing physicians’ recommendations or Medicare coverage rules. For instance, while MA plans typically provide up to 100 days of covered care in a nursing home after a three-day hospital stay, using nH Predict, patients on UnitedHealth’s MA plan rarely stay in nursing homes for more than 14 days before receiving payment denials, the lawsuits allege…(More)”

We urgently need data for equitable personalized medicine

Curated on Posted on by Stefaan Verhulst

Article by Manuel Corpas: “…As a bioinformatician, I am now focusing my attention on gathering the statistics to show just how biased medical research data are. There are problems across the board, ranging from which research questions get asked in the first place, to who participates in clinical trials, to who gets their genomes sequenced. The world is moving toward “precision medicine,” where any individual can have their DNA analyzed and that information can be used to help prescribe the right drugs in the right dosages. But this won’t work if a person’s genetic variants have never been identified or studied in the first place.

It’s astonishing how powerful our genetics can be in mediating medicines. Take the gene CYP2D6, which is known to play a vital role in how fast humans metabolize 25 percent of all the pharmaceuticals on the market. If you have a genetic variant of CYP2D6 that makes you metabolize drugs more quickly, or less quickly, it can have a huge impact on how well those drugs work and the dangers you face from taking them. Codeine was banned from all of Ethiopia in 2015, for example, because a high proportion of people in the country (perhaps 30 percent) have a genetic variant of CYP2D6 that makes them quickly metabolize that drug into morphine, making it more likely to cause respiratory distress and even death…(More)”

Consumer vulnerability in the digital age

Curated on Posted on by Stefaan Verhulst

OECD Report: “Protecting consumers when they are most vulnerable has long been a core focus of consumer policy. This report first discusses the nature and scale of consumer vulnerability in the digital age, including its evolving conceptualisation, the role of emerging digital trends, and implications for consumer policy. It finds that in the digital age, vulnerability may be experienced not only by some consumers, but increasingly by most, if not all, consumers. Accordingly, it sets out several measures to address the vulnerability of specific consumer groups and all consumers, and concludes with avenues for more research on the topic…(More)”.

Training Data for the Price of a Sandwich

Curated on Posted on by Stefaan Verhulst

Article by Stefan Baack: “Common Crawl (henceforth also referred to as CC) is an organization that has been essential to the technological advancements of generative AI, but is largely unknown to the broader public. This California nonprofit with only a handful of employees has crawled billions of web pages since 2008 and it makes this data available without charge via Amazon Web Services (AWS). Because of the enormous size and diversity (in terms of sources and formats) of the data, it has been pivotal as a source for training data for many AI builders. Generative AI in its current form would probably not be possible without Common Crawl, given that the vast majority of data used to train the original model behind OpenAI’s ChatGPT, the generative AI product that set off the current hype, came from it (Brown et al. 2020). The same is true for many models published since then.

Although pivotal, Common Crawl has so far received relatively little attention for its contribution to generative AI…(More)”.