Paper by Sara Mesquita, Lília Perfeito, Daniela Paolotti, and Joana Gonçalves-Sá: “Epidemiology and Public Health have increasingly relied on structured and unstructured data, collected inside and outside of typical health systems, to study, identify, and mitigate diseases at the population level. Focusing on infectious disease, we review how Digital Epidemiology (DE) was at the beginning of 2020 and how it was changed by the COVID-19 pandemic, in both nature and breadth. We argue that DE will become a progressively useful tool as long as its potential is recognized and its risks are minimized. Therefore, we expand on the current views and present a new definition of DE that, by highlighting the statistical nature of the datasets, helps in identifying possible biases. We offer some recommendations to reduce inequity and threats to privacy and argue in favour of complex multidisciplinary approaches to tackling infectious diseases…(More)”
Using Data for Good: Identifying Who Could Benefit from Simplified Tax Filing
Blog by New America: “For years, New America Chicago has been working with state agencies, national and local advocates and thought leaders, as well as community members on getting beneficial tax credits, like the Earned Income Tax Credit (EITC) and Child Tax Credit (CTC), into the hands of those who need them most. Illinois paved the way recently with its innovative simplified filing initiative which helps residents easily claim their state Earned Income Credit (EIC) by confirming their refund with a prepopulated return.
This past year we had discussions with Illinois policymakers and state agencies, like the Illinois Department of Revenue (IDoR) and the Illinois Department of Human Services (IDHS), to envision new ways for expanding the simplified filing initiative. It is currently designed to reach those who have filed a federal tax return and claimed their EITC, leaving out non-filer households who typically do not file taxes because they earn less than the federal income requirement or have other barriers.
In Illinois, over 600,000 households are enrolled in SNAP, and over 1 million households are enrolled in Medicaid. Every year thousands of families spend countless hours applying for these and other social safety net programs using IDHS’ Application for Benefits Eligibility (ABE). Unfortunately, many of these households are most in need of the federal EITC and the recently expanded state EIC but will never receive it. We posed the question, what if Illinois could save families time and money by using that already provided income and household information to streamline access to the state EIC for low-income families that don’t normally file taxes?
Our friends at Inclusive Economy Lab (IEL) conducted analysis using Census microdata to estimate the number of Illinois households who are enrolled in Medicaid and SNAP but do not file their federal or state tax forms…(More)”.
The Future of Community
Book by John Kraski and Justin Shenkarow: “… a groundbreaking new take on the seismic impact web3 is having—and will continue to have—on our technological and social landscapes. The authors discuss why web3 really is the “next big thing” to shape our digital and offline futures and how it will transform the world.
You’ll discover a whole host of web3 applications poised to excite and disrupt industries around the world, from fan tokens that reshape how we think about interactions between artists and fans to self-sovereign identities on the blockchain that allow you to take full control over how your personal data is used and collected online.
You’ll also find:
- Insightful explorations of technologies and techniques like tokenization, decentralized marketplaces, decentralized autonomous organizations, and more
- Explanations of how web3 allows you to take greater ownership and control of your digital and offline assets
- Discussions of why web3 increases transparency and accountability at every level of business, government, and social hierarchies…(More)”.
Computational social science is growing up: why puberty consists of embracing measurement validation, theory development, and open science practices
Paper by Timon Elmer: “Puberty is a phase in which individuals often test the boundaries of themselves and surrounding others and further define their identity – and thus their uniqueness compared to other individuals. Similarly, as Computational Social Science (CSS) grows up, it must strike a balance between its own practices and those of neighboring disciplines to achieve scientific rigor and refine its identity. However, there are certain areas within CSS that are reluctant to adopt rigorous scientific practices from other fields, which can be observed through an overreliance on passively collected data (e.g., through digital traces, wearables) without questioning the validity of such data. This paper argues that CSS should embrace the potential of combining both passive and active measurement practices to capitalize on the strengths of each approach, including objectivity and psychological quality. Additionally, the paper suggests that CSS would benefit from integrating practices and knowledge from other established disciplines, such as measurement validation, theoretical embedding, and open science practices. Based on this argument, the paper provides ten recommendations for CSS to mature as an interdisciplinary field of research…(More)”.
Open Data Commons Licences (ODCL): Licensing personal and non personal data supporting the commons and privacy
Paper by Yaniv Benhamou and Melanie Dulong de Rosnay: “Data are often subject to a multitude of rights (e.g. original works or personal data posted on social media, or collected through captcha, subject to copyright, database and data protection) and voluntary shared through non standardized, non interoperable contractual terms. This leads to fragmented legal regimes and has become an even major challenge in the AI-era, for example when online platforms set their own Terms of Services (ToS), in business-to-consumer relationship (B2C).
This article proposes standard terms that may apply to all kind of data (including personal and mixed datasets subject to different legal regimes) based on the open data philosophy initially developed for Free and Open Source software and Creative Commons licenses for artistic and other copyrighted works. In a first part, we analyse how to extend open standard terms to all kinds of data (II). In a second part, we suggest to combine these open standard terms with collective governance instruments, in particular data trust, inspired by commons-based projects and by the centennial collective management of copyright (III). In a last part, after few concluding remarks (IV), we propose a template “Open Data Commons Licenses“ (ODCL) combining compulsory and optional elements to be selected by licensors, illustrated by pictograms and icons inspired by the bricks of Creative Commons licences and legal design techniques (V).
This proposal addresses the bargaining power imbalance and information asymmetry (by offering the licensor the ability to decide the terms), and conceptualises contract law differently. It reverses the current logic of contract: instead of letting companies (licensees) impose their own ToS to the users (licensors, being the copyright owner, data subject, data producer), licensors will reclaim the ability to set their own terms for access and use of data, by selecting standard terms. This should also allow the management of complex datasets, increase data sharing, and improve trust and control over the data. Like previous open licencing standards, the model is expected to lower the transaction costs by reducing the need to develop and read new complicated contractual terms. It can also spread the virality of open data to all data in an AI-era, if any input data under such terms used for AI training purposes propagates its conditions to all aggregated and output data. In other words, any data distributed under our ODCL template will turn all outcome into more or less open data and foster a data common ecosystem. Finally, instead of full openness, our model allows for restrictions outside of certain boundaries (e.g. authorized users and uses), in order to protect the commons and certain values. The model would require to be governed and monitored by a collective data trust…(More)”.
The case for adaptive and end-to-end policy management
Article by Pia Andrews: “Why should we reform how we do policy? Simple. Because the gap between policy design and delivery has become the biggest barrier to delivering good public services and policy outcomes and is a challenge most public servants experience daily, directly or indirectly.
This gap wasn’t always the case, with policy design and delivery separated as part of the New Public Management reforms in the ’90s. When you also consider the accelerating rate of change, increasing cadence of emergencies, and the massive speed and scale of new technologies, you could argue that end-to-end policy reform is our most urgent problem to solve.
Policy teams globally have been exploring new design methods like human-centred design, test-driven iteration (agile), and multi-disciplinary teams that get policy end users in the room (eg, NSW Policy Lab). There has also been an increased focus on improving policy evaluation across the world (eg, the Australian Centre for Evaluation). In both cases, I’m delighted to see innovative approaches being normalised across the policy profession, but it has become obvious that improving design and/or evaluation is still far from sufficient to drive better (or more humane) policy outcomes in an ever-changing world. It is not only the current systemic inability to detect and respond to unintended consequences that emerge but the lack of policy agility that perpetuates issues even long after they might be identified.
Below I outline four current challenges for policy management and a couple of potential solutions, as something of a discussion starter
Problem 1) The separation of (and mutual incomprehension between) policy design, delivery and the public
The lack of multi-disciplinary policy design, combined with a set-and-forget approach to policy, combined with delivery teams being left to interpret policy instructions without support, combined with a gap and interpretation inconsistency between policy modelling systems and policy delivery systems, all combined with a lack of feedback loops in improving policy over time, has led to a series of black holes throughout the process. Tweaking the process as it currently stands will not fix the black holes. We need a more holistic model for policy design, delivery and management…(More)”.
Considerations for Governing Open Foundation Models
Brief by Rishi Bommasani et al: “Foundation models (e.g., GPT-4, Llama 2) are at the epicenter of AI, driving technological innovation and billions in investment. This paradigm shift has sparked widespread demands for regulation. Animated by factors as diverse as declining transparency and unsafe labor practices, limited protections for copyright and creative work, as well as market concentration and productivity gains, many have called for policymakers to take action.
Central to the debate about how to regulate foundation models is the process by which foundation models are released. Some foundation models like Google DeepMind’s Flamingo are fully closed, meaning they are available only to the model developer; others, such as OpenAI’s GPT-4, are limited access, available to the public but only as a black box; and still others, such as Meta’s Llama 2, are more open, with widely available model weights enabling downstream modification and scrutiny. As of August 2023, the U.K.’s Competition and Markets Authority documents the most common release approach for publicly-disclosed models is open release based on data from Stanford’s Ecosystem Graphs. Developers like Meta, Stability AI, Hugging Face, Mistral, Together AI, and EleutherAI frequently release models openly.
Governments around the world are issuing policy related to foundation models. As part of these efforts, open foundation models have garnered significant attention: The recent U.S. Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence tasks the National Telecommunications and Information Administration with preparing a report on open foundation models for the president. In the EU, open foundation models trained with fewer than 1025 floating point operations (a measure of the amount of compute expended) appear to be exempted under the recently negotiated AI Act. The U.K.’s AI Safety Institute will “consider open-source systems as well as those deployed with various forms of access controls” as part of its initial priorities. Beyond governments, the Partnership on AI has introduced guidelines for the safe deployment of foundation models, recommending against open release for the most capable foundation models.
Policy on foundation models should support the open foundation model ecosystem, while providing resources to monitor risks and create safeguards to address harms. Open foundation models provide significant benefits to society by promoting competition, accelerating innovation, and distributing power. For example, small businesses hoping to build generative AI applications could choose among a variety of open foundation models that offer different capabilities and are often less expensive than closed alternatives. Further, open models are marked by greater transparency and, thereby, accountability. When a model is released with its training data, independent third parties can better assess the model’s capabilities and risks…(More)”.
Populist Leaders and the Economy
Paper by Manuel Funke, Moritz Schularick and Christoph Trebesch: “Populism at the country level is at an all-time high, with more than 25 percent of nations currently governed by populists. How do economies perform under populist leaders? We build a new long-run cross-country database to study the macroeconomic history of populism. We identify 51 populist presidents and prime ministers from 1900 to 2020 and show that the economic cost of populism is high. After 15 years, GDP per capita is 10 percent lower compared to a plausible nonpopulist counterfactual. Economic disintegration, decreasing macroeconomic stability, and the erosion of institutions typically go hand in hand with populist rule…(More)”.
Observer Theory
Article by Stephen Wolfram: “We call it perception. We call it measurement. We call it analysis. But in the end it’s about how we take the world as it is, and derive from it the impression of it that we have in our minds.
We might have thought that we could do science “purely objectively” without any reference to observers or their nature. But what we’ve discovered particularly dramatically in our Physics Project is that the nature of us as observers is critical even in determining the most fundamental laws we attribute to the universe.
But what ultimately does an observer—say like us—do? And how can we make a theoretical framework for it? Much as we have a general model for the process of computation—instantiated by something like a Turing machine—we’d like to have a general model for the process of observation: a general “observer theory”.
Central to what we think of as an observer is the notion that the observer will take the raw complexity of the world and extract from it some reduced representation suitable for a finite mind. There might be zillions of photons impinging on our eyes, but all we extract is the arrangement of objects in a visual scene. Or there might be zillions of gas molecules impinging on a piston, yet all we extract is the overall pressure of the gas.
In the end, we can think of it fundamentally as being about equivalencing. There are immense numbers of different individual configurations for the photons or the gas molecules—that are all treated as equivalent by an observer who’s just picking out the particular features needed for some reduced representation.
There’s in a sense a certain duality between computation and observation. In computation one’s generating new states of a system. In observation, one’s equivalencing together different states.
That equivalencing must in the end be implemented “underneath” by computation. But in observer theory what we want to do is just characterize the equivalencing that’s achieved. For us as observers it might in practice be all about how our senses work, what our biological or cultural nature is—or what technological devices or structures we’ve built. But what makes a coherent concept of observer theory possible is that there seem to be general, abstract characterizations that capture the essence of different kinds of observers…(More)”.
Privacy-Enhancing and Privacy-Preserving Technologies: Understanding the Role of PETs and PPTs in the Digital Age
Paper by the Centre for Information Policy Leadership: “…explores how organizations are approaching privacy-enhancing technologies (“PETs”) and how PETs can advance data protection principles, and provides examples of how specific types of PETs work. It also explores potential challenges to the use of PETs and possible solutions to those challenges.
CIPL emphasizes the enormous potential inherent in these technologies to mitigate privacy risks and support innovation, and recommends a number of steps to foster further development and adoption of PETs. In particular, CIPL calls for policymakers and regulators to incentivize the use of PETs through clearer guidance on key legal concepts that impact the use of PETs, and by adopting a pragmatic approach to the application of these concepts.
CIPL’s recommendations towards wider adoption are as follows:
- Issue regulatory guidance and incentives regarding PETs: Official regulatory guidance addressing PETs in the context of specific legal obligations or concepts (such as anonymization) will incentivize greater investment in PETs.
- Increase education and awareness about PETs: PET developers and providers need to show tangible evidence of the value of PETs and help policymakers, regulators and organizations understand how such technologies can facilitate responsible data use.
- Develop industry standards for PETs: Industry standards would help facilitate interoperability for the use of PETs across jurisdictions and help codify best practices to support technical reliability to foster trust in these technologies.
- Recognize PETs as a demonstrable element of accountability: PETs complement robust data privacy management programs and should be recognized as an element of organizational accountability…(More)”.