Data Stewardship: The Way Forward in the New Digital Data Landscape

Curated on Posted on by Stefaan Verhulst

Essay by Courtney Cameron: “…It is absolutely critical that Statistics Canada, as a national statistical office (NSO) and public service organization, along with other government agencies and services, adapt to the new data ecosystem and digital landscape. Canada is falling behind in adjusting to rapid digitalization, exploding data volumes, the ever-increasing digital market monopolization by private companies, foreign data harvesting, and in managing the risks associated with data sharing or reuse. If Statistics Canada and the federal public service are to keep up with private companies or foreign powers in this digital data context, and to continue to provide useful insights and services for Canadians, concerns of data digitalization, data interoperability and data security must be addressed through effective data stewardship.

However, it is not sufficient to have data stewards responsible for data: as data governance expert David Plotkin argues in Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance, government departments must also consult these stewards on decisions about the data that they steward, if they are to ensure that decisions are made in the best interests of those who get value from the information. Frameworks, policies and procedures are needed to ensure this, as is having a steward involved in the processes as they occur. Plotkin also writes that data stewardship involvement needs to be integrated into enterprise processes, such as in project management and systems development methodologies. Data stewardship and data governance principles must be accepted as a part of the corporate culture, and stewardship leaders need to advise, drive and support this shift.

Finally, stewardship goes beyond sound data management and standards: it is important to be mindful of the role of an NSO. Public acceptability and trust are of vital importance. Social licence, or acceptability, and public engagement are necessary for NSOs to be able to perform their duties. These are achieved through practising data stewardship and adhering to the principles of open data, as well as by ensuring transparent processes, confidentiality and security, and by communicating the value of citizens’ sharing their data…With the rapidly accelerating proliferation of data and the increasing demand for, and potential of, data sharing and collaboration, NSOs and public governance organizations alike need to reimagine data stewardship as a function and role encompassing a wider range of purposes and responsibilities…(More)”. See also: Data Stewards — Drafting the Job Specs for A Re-imagined Data Stewardship Role

QuantGov

Curated on Posted on by Stefaan Verhulst

About: “QuantGov is an open-source policy analytics platform designed to help create greater understanding and analysis of the breadth of government actions through quantifying policy text. By using the platform, researchers can quickly and effectively retrieve unique data that lies embedded in large bodies of text – data on text complexity, part of speech metrics, topic modeling, etc. …

QuantGov is a tool designed to make policy text more accessible. Think about it in terms of a hyper-powerful Google search that not only finds (1) specified content within massive quantities of text, but (2) also finds patterns and groupings and can even make predictions about what is in a document. Some recent use cases include the following:

  • Analyzing state regulatory codes and predicting which parts of those codes are related to occupational licensing….And predicting which occupation the regulation is talking about….And determining the cost to receive the license.
  • Analyzing Canadian province regulatory code while grouping individual regulations by industry-topic….And determining which Ministers are responsible for those regulations….And determining the complexity of the text for those regulation.
  • Quantifying the number of tariff exclusions that exists due to the Trade Expansion Act of 1962 and recent tariff polices….And determining which products those exclusions target.
  • Comparing the regulatory codes and content of 46 US states, 11 Canadian provinces, and 7 Australian states….While using consistent metrics that can lead to insights that provide legitimate policy improvements…(More)”.

Technological Citizenship in Times of Digitization: An Integrative Framework

Curated on Posted on by Stefaan Verhulst

Article by Anne Marte Gardenier, Rinie van Est & Lambèr Royakkers: “This article introduces an integrative framework for technological citizenship, examining the impact of digitization and the active roles of citizens in shaping this impact across the private, social, and public sphere. It outlines the dual nature of digitization, offering opportunities for enhanced connectivity and efficiency while posing challenges to privacy, security, and democratic integrity. Technological citizenship is explored through the lenses of liberal, communitarian, and republican theories, highlighting the active roles of citizens in navigating the opportunities and risks presented by digital technologies across all life spheres. By operationalizing technological citizenship, the article aims to address the gap in existing literature on the active roles of citizens in the governance of digitization. The framework emphasizes empowerment and resilience as crucial capacities for citizens to actively engage with and govern digital technologies. It illuminates citizens’ active participation in shaping the digital landscape, advocating for policies that support their engagement in safeguarding private, social, and public values in the digital age. The study calls for further research into technological citizenship, emphasizing its significance in fostering a more inclusive and equitable digital society…(More)”.

Artificial intelligence and complex sustainability policy problems: translating promise into practice

Curated on Posted on by Stefaan Verhulst

Paper by Ruby O’Connor et al: “Addressing sustainability policy challenges requires tools that can navigate complexity for better policy processes and outcomes. Attention on Artificial Intelligence (AI) tools and expectations for their use by governments have dramatically increased over the past decade. We conducted a narrative review of academic and grey literature to investigate how AI tools are being used and adapted for policy and public sector decision-making. We found that academics, governments, and consultants expressed positive expectations about AI, arguing that AI could or should be used to address a wide range of policy challenges. However, there is much less evidence of how public decision makers are actually using AI tools or detailed insight into the outcomes of use. From our findings we draw four lessons for translating the promise of AI into practice: 1) Document and evaluate AI’s application to sustainability policy problems in the real-world; 2) Focus on existing and mature AI technologies, not speculative promises or external pressures; 3) Start with the problem to be solved, not the technology to be applied; and 4) Anticipate and adapt to the complexity of sustainability policy problems…(More)”.

The Poisoning of the American Mind

Curated on Posted on by Stefaan Verhulst

Book by Lawrence M. Eppard: “Humans are hard-wired to look for information that they agree with (regardless of the information’s veracity), avoid information that makes them uncomfortable (even if that information is true), and interpret information in a manner that is most favorable to their sense of self. The damage these cognitive tendencies cause to one’s perception of reality depends in part upon the information that a person surrounds himself/herself with. Unfortunately, in the U.S. today, both liberals and conservatives are regularly bombarded with misleading information as well as lies from people they believe to be trustworthy and authoritative sources. While there are several factors one could plausibly blame for this predicament, the decline in the quality of the sources of information that the right and left rely on over the last few decades plays a primary role. As a result of this decline, we are faced with an epistemic crisis that is poisoning the American mind and threatening our democracy. In his forthcoming book with Jacob L. Mackey, The Poisoning of the American Mind, Lawrence M. Eppard explores epistemic problems in both the right-wing and left-wing ideological silos in the U.S., including ideology presented as fact, misinformation, disinformation, and malinformation…(More)”.

Anti-Corruption and Integrity Outlook 2024

Curated on Posted on by Stefaan Verhulst

OECD Report: “This first edition of the OECD Anti-Corruption and Integrity Outlook analyses Member countries’ efforts to uphold integrity and fight corruption. Based on data from the Public Integrity Indicators, it analyses the performance of countries’ integrity frameworks, and explores how some of the main challenges to governments today (including the green transition, artificial intelligence, and foreign interference) are increasing corruption and integrity risks for countries. It also addresses how the shortcomings in integrity systems can impede countries’ responses to these major challenges. In providing a snapshot of how countries are performing today, the Outlook supports strategic planning and policy work to strengthen public integrity for the future…(More)”.

We don’t need an AI manifesto — we need a constitution

Curated on Posted on by Stefaan Verhulst

Article by Vivienne Ming: “Loans drive economic mobility in America, even as they’ve been a historically powerful tool for discrimination. I’ve worked on multiple projects to reduce that bias using AI. What I learnt, however, is that even if an algorithm works exactly as intended, it is still solely designed to optimise the financial returns to the lender who paid for it. The loan application process is already impenetrable to most, and now your hopes for home ownership or small business funding are dying in a 50-millisecond computation…

In law, the right to a lawyer and judicial review are a constitutional guarantee in the US and an established civil right throughout much of the world. These are the foundations of your civil liberties. When algorithms act as an expert witness, testifying against you but immune to cross examination, these rights are not simply eroded — they cease to exist.

People aren’t perfect. Neither ethics training for AI engineers nor legislation by woefully uninformed politicians can change that simple truth. I don’t need to assume that Big Tech chief executives are bad actors or that large companies are malevolent to understand that what is in their self-interest is not always in mine. The framers of the US Constitution recognised this simple truth and sought to leverage human nature for a greater good. The Constitution didn’t simply assume people would always act towards that greater good. Instead it defined a dynamic mechanism — self-interest and the balance of power — that would force compromise and good governance. Its vision of treating people as real actors rather than better angels produced one of the greatest frameworks for governance in history.

Imagine you were offered an AI-powered test for post-partum depression. My company developed that very test and it has the power to change your life, but you may choose not to use it for fear that we might sell the results to data brokers or activist politicians. You have a right to our AI acting solely for your health. It was for this reason I founded an independent non-profit, The Human Trust, that holds all of the data and runs all of the algorithms with sole fiduciary responsibility to you. No mother should have to choose between a life-saving medical test and her civil rights…(More)”.

A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI

Curated on Posted on by Stefaan Verhulst

Report by Hannah Chafetz, Sampriti Saxena, and Stefaan G. Verhulst: “Since late 2022, generative AI services and large language models (LLMs) have transformed how many individuals access, and process information. However, how generative AI and LLMs can be augmented with open data from official sources and how open data can be made more accessible with generative AI – potentially enabling a Fourth Wave of Open Data – remains an under explored area. 

For these reasons, The Open Data Policy Lab (a collaboration between The GovLab and Microsoft) decided to explore the possible intersections between open data from official sources and generative AI. Throughout the last year, the team has conducted a range of research initiatives about the potential of open data and generative including a panel discussion, interviews, and Open Data Action Labs – a series of design sprints with a diverse group of industry experts. 

These initiatives were used to inform our latest report, “A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI,” (May 2024) which provides a new framework and recommendations to support open data providers and other interested parties in making open data “ready” for generative AI…

The report outlines five scenarios in which open data from official sources (e.g. open government and open research data) and generative AI can intersect. Each of these scenarios includes case studies from the field and a specific set of requirements that open data providers can focus on to become ready for a scenario. These include…(More)” (Arxiv).

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“Data Commons”: Under Threat by or The Solution for a Generative AI Era ? Rethinking Data Access and Re-us

Curated on Posted on by Stefaan Verhulst

Article by Stefaan G. Verhulst, Hannah Chafetz and Andrew Zahuranec: “One of the great paradoxes of our datafied era is that we live amid both unprecedented abundance and scarcity. Even as data grows more central to our ability to promote the public good, so too does it remain deeply — and perhaps increasingly — inaccessible and privately controlled. In response, there have been growing calls for “data commons” — pools of data that would be (self-)managed by distinctive communities or entities operating in the public’s interest. These pools could then be made accessible and reused for the common good.

Data commons are typically the results of collaborative and participatory approaches to data governance [1]. They offer an alternative to the growing tendency toward privatized data silos or extractive re-use of open data sets, instead emphasizing the communal and shared value of data — for example, by making data resources accessible in an ethical and sustainable way for purposes in alignment with community values or interests such as scientific research, social good initiatives, environmental monitoring, public health, and other domains.

Data commons can today be considered (the missing) critical infrastructure for leveraging data to advance societal wellbeing. When designed responsibly, they offer potential solutions for a variety of wicked problems, from climate change to pandemics and economic and social inequities. However, the rapid ascent of generative artificial intelligence (AI) technologies is changing the rules of the game, leading both to new opportunities as well as significant challenges for these communal data repositories.

On the one hand, generative AI has the potential to unlock new insights from data for a broader audience (through conversational interfaces such as chats), fostering innovation, and streamlining decision-making to serve the public interest. Generative AI also stands out in the realm of data governance due to its ability to reuse data at a massive scale, which has been a persistent challenge in many open data initiatives. On the other hand, generative AI raises uncomfortable questions related to equitable access, sustainability, and the ethical re-use of shared data resources. Further, without the right guardrails, funding models and enabling governance frameworks, data commons risk becoming data graveyards — vast repositories of unused, and largely unusable, data.

Ten part framework to rethink Data Commons

In what follows, we lay out some of the challenges and opportunities posed by generative AI for data commons. We then turn to a ten-part framework to set the stage for a broader exploration on how to reimagine and reinvigorate data commons for the generative AI era. This framework establishes a landscape for further investigation; our goal is not so much to define what an updated data commons would look like but to lay out pathways that would lead to a more meaningful assessment of the design requirements for resilient data commons in the age of generative AI…(More)”

5 Ways AI Could Shake Up Democracy

Curated on Posted on by Stefaan Verhulst

Article by Shane Snider: “Tech luminary, author and Harvard Kennedy School lecturer Bruce Schneier on Tuesday offered his take on the promises and perils of artificial intelligence in key aspects of democracy.

In just two years, generative artificial intelligence (GenAI) has sparked a race to adopt (and defend against) the technology in government and the enterprise. It seems every aspect of life will soon be impacted — if not already feeling AI’s influence. A global race to place regulatory guardrails is taking shape even as companies and governments are spending billions of dollars implementing new AI technologies.

Schneier contends that five major areas of our democracy will likely see profound changes, including politics, lawmaking, administration, the legal system, and to citizens themselves.

“I don’t think it’s an exaggeration to predict that artificial intelligence will affect every aspect of our society, not necessarily by doing new things, but mostly by doing things that already or could be done by humans, are now replacing humans … There are potential changes in four dimensions: speed, scale, scope, and sophistication.”..(More)”.