Paper by Joshua S. Gans: “New generative artificial intelligence (AI) models, including large language models and image generators, have created new challenges for copyright policy as such models may be trained on data that includes copy-protected content. This paper examines this issue from an economics perspective and analyses how different copyright regimes for generative AI will impact the quality of content generated as well as the quality of AI training. A key factor is whether generative AI models are small (with content providers capable of negotiations with AI providers) or large (where negotiations are prohibitive). For small AI models, it is found that giving original content providers copyright protection leads to superior social welfare outcomes compared to having no copyright protection. For large AI models, this comparison is ambiguous and depends on the level of potential harm to original content providers and the importance of content for AI training quality. However, it is demonstrated that an ex-post `fair use’ type mechanism can lead to higher expected social welfare than traditional copyright regimes…(More)”.
How Health Data Integrity Can Earn Trust and Advance Health
Article by Jochen Lennerz, Nick Schneider and Karl Lauterbach: “Efforts to share health data across borders snag on legal and regulatory barriers. Before detangling the fine print, let’s agree on overarching principles.
Imagine a scenario in which Mary, an individual with a rare disease, has agreed to share her medical records for a research project aimed at finding better treatments for genetic disorders. Mary’s consent is grounded in trust that her data will be handled with the utmost care, protected from unauthorized access, and used according to her wishes.
It may sound simple, but meeting these standards comes with myriad complications. Whose job is it to weigh the risk that Mary might be reidentified, even if her information is de-identified and stored securely? How should that assessment be done? How can data from Mary’s records be aggregated with patients from health systems in other countries, each with their own requirements for data protection and formats for record keeping? How can Mary’s wishes be respected, both in terms of what research is conducted and in returning relevant results to her?
From electronic medical records to genomic sequencing, health care providers and researchers now have an unprecedented wealth of information that could help tailor treatments to individual needs, revolutionize understanding of disease, and enhance the overall quality of health care. Data protection, privacy safeguards, and cybersecurity are all paramount for safeguarding sensitive medical information, but much of the potential that lies in this abundance of data is being lost because well-intentioned regulations have not been set up to allow for data sharing and collaboration. This stymies efforts to study rare diseases, map disease patterns, improve public health surveillance, and advance evidence-based policymaking (for instance, by comparing effectiveness of interventions across regions and demographics). Projects that could excel with enough data get bogged down in bureaucracy and uncertainty. For example, Germany now has strict data protection laws—with heavy punishment for violations—that should allow de-identified health insurance claims to be used for research within secure processing environments, but the legality of such use has been challenged…(More)”.
Does information about citizen participation initiatives increase political trust?
Paper by Martin Ardanaz, Susana Otálvaro-Ramírez, and Carlos Scartascini: “Participatory programs can reduce the informational and power asymmetries that engender mistrust. These programs, however, cannot include every citizen. Hence, it is important to evaluate if providing information about those programs could affect trust among those who do not participate. We assess the effect of an informational campaign about these programs in the context of a survey experiment conducted in the city of Buenos Aires, Argentina. Results show that providing detailed information about citizen involvement and outputs of a participatory budget initiative marginally shapes voters’ assessments of government performance and political trust. In particular, it increases voters’ perceptions about the benevolence and honesty of the government. Effects are larger for individuals with ex ante more negative views about the local government’s quality and they differ according to the respondents’ interpersonal trust and their beliefs about the ability of their communities to solve the type of collective-action problems that the program seeks to address. This article complements the literature that has examined the effects of participatory interventions on trust, and the literature that evaluates the role of information. The results in the article suggest that participatory budget programs could directly affect budget allocations and trust for those who participate, and those that are well-disseminated could also affect trust in the broader population. Because mistrustful individuals tend to shy away from demanding the government public goods that increase overall welfare, well-disseminated participatory budget programs could affect budget allocations directly and through their effect on trust…(More)”.
Computing Power and the Governance of AI
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
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)”.
Developing skills for digital government
OECD “review of good practices across OECD governments”: “Digital technologies are having a profound impact on economies, labour markets and societies. They also have the potential to transform government, by enabling the implementation of more accessible and effective services. To support a shift towards digital government, investment is needed in developing the skills of civil servants. This paper reviews good practices across OECD countries to foster skills for digital government. It presents different approaches in public administration to organising training activities as well as opportunities for informal learning. It also provides insights into how relevant skills can be identified through competence frameworks, how they can be assessed, and how learning opportunities can be evaluated….(More)”
Tech Strikes Back
Essay by Nadia Asparouhova: “A new tech ideology is ascendant online. “Introducing effective accelerationism,” the pseudonymous user Beff Jezos tweeted, rather grandly, in May 2022. “E/acc” — pronounced ee-ack — “is a direct product [of the] tech Twitter schizosphere,” he wrote. “We hope you join us in this new endeavour.”
The reaction from Jezos’s peers was a mix of positive, critical, and perplexed. “What the f*** is e/acc,” posted multiple users. “Accelerationism is unfortunately now just a buzzword,” sighed political scientist Samo Burja, referring to a related concept popularized around 2017. “I guess unavoidable for Twitter subcultures?” “These [people] are absolutely bonkers,” grumbled Timnit Gebru, an artificial intelligence researcher and activist who frequently criticizes the tech industry. “Their fanaticism + god complex is exhausting.”
Despite the criticism, e/acc persists, and is growing, in the tech hive mind. E/acc’s founders believe that the tech world has become captive to a monoculture. If it becomes paralyzed by a fear of the future, it will never produce meaningful benefits. Instead, e/acc encourages more ideas, more growth, more competition, more action. “Whether you’re building a family, a startup, a spaceship, a robot, or better energy policy, just build,” writes one anonymous poster. “Do something hard. Do it for everyone who comes next. That’s it. Existence will take care of the rest.”…(More)”.
We urgently need data for equitable personalized medicine
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
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
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)”.