The use of AI for improving energy security

Curated on Posted on by Stefaan Verhulst

Rand Report: “Electricity systems around the world are under pressure due to aging infrastructure, rising demand for electricity and the need to decarbonise energy supplies at pace. Artificial intelligence (AI) applications have potential to help address these pressures and increase overall energy security. For example, AI applications can reduce peak demand through demand response, improve the efficiency of wind farms and facilitate the integration of large numbers of electric vehicles into the power grid. However, the widespread deployment of AI applications could also come with heightened cybersecurity risks, the risk of unexplained or unexpected actions, or supplier dependency and vendor lock-in. The speed at which AI is developing means many of these opportunities and risks are not yet well understood.

The aim of this study was to provide insight into the state of AI applications for the power grid and the associated risks and opportunities. Researchers conducted a focused scan of the scientific literature to find examples of relevant AI applications in the United States, the European Union, China and the United Kingdom…(More)”.

Enrolling Citizens: A Primer on Archetypes of Democratic Engagement with AI

Curated on Posted on by Stefaan Verhulst

Paper by Wanheng Hu and Ranjit Singh: “In response to rapid advances in artificial intelligence, lawmakers, regulators, academics, and technologists alike are sifting through technical jargon and marketing hype as they take on the challenge of safeguarding citizens from the technology’s potential harms while maximizing their access to its benefits. A common feature of these efforts is including citizens throughout the stages of AI development and governance. Yet doing so is impossible without a clear vision of what citizens ideally should do. This primer takes up this imperative and asks: What approaches can ensure that citizens have meaningful involvement in the development of AI, and how do these approaches envision the role of a “good citizen”?

The primer highlights three major approaches to involving citizens in AI — AI literacy, AI governance, and participatory AI — each of them premised on the importance of enrolling citizens but envisioning different roles for citizens to play. While recognizing that it is largely impossible to come up with a universal standard for building AI in the public interest, and that all approaches will remain local and situated, this primer invites a critical reflection on the underlying assumptions about technology, democracy, and citizenship that ground how we think about the ethics and role of public(s) in large-scale sociotechnical change. ..(More)”.

The Behavioral Scientists Working Toward a More Peaceful World

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Interview by Heather Graci: “…Nation-level data doesn’t help us understand community-level conflict. Without understanding community-level conflict, it becomes much harder to design policies to prevent it.

Cikara: “So much of the data that we have is at the level of the nation, when our effects are all happening at very local levels. You see these reports that say, “In Germany, 14 percent of the population is immigrants.” It doesn’t matter at the national level, because they’re not distributed evenly across the geography. That means that some communities are going to be at greater risk for conflict than others. But that sort of local variation and sensitivity to it, at least heretofore, has really been missing from the conversation on the research side. Even when you’re in the same place, in the same country within the same state, the same canton, there can still be a ton of variation from neighborhood to neighborhood. 

“The other thing that we know matters a lot is not just the diversity of these neighborhoods but the segregation of them. It turns out that these kinds of prejudices and violence are less likely to break out in those places where it’s both diverse and people are interdigitated with how they live. So it’s not just the numbers, it’s also the spatial organization. 

“For example, in Singapore, because so much of the real estate is state-owned, they make it so that people who are coming from different countries can’t cluster together because they assign them to live separate from one another in order to prevent these sorts of enclaves. All these structural and meta-level organizational features have really, really important inputs for intergroup dynamics and psychology.”..(More)”.

Why policy failure is a prerequisite for innovation in the public sector

Curated on Posted on by Stefaan Verhulst

Blog by Philipp Trein and Thenia Vagionaki: “In our article entitled, “Why policy failure is a prerequisite for innovation in the public sector,” we explore the relationship between policy failure and innovation within public governance. Drawing inspiration from the “Innovator’s Dilemma,”—a theory from the management literature—we argue that the very nature of policymaking, characterized by myopia of voters, blame avoidance by decisionmakers, and the complexity (ill-structuredness) of societal challenges, has an inherent tendency to react with innovation only after failure of existing policies.  

Our analysis implies that we need to be more critical of what the policy process can achieve in terms of public sector innovation. Cognitive limitations tend to lead to a misperception of problems and inaccurate assessment of risks by decision makers according to the “Innovator’s Dilemma”.  This problem implies that true innovation (non-trivial policy changes) are unlikely to happen before an existing policy has failed visibly. However, our perspective does not want to paint a gloomy picture for public policy making but rather offers a more realistic interpretation of what public sector innovation can achieve. As a consequence, learning from experts in the policy process should be expected to correct failures in public sector problem-solving during the political process, rather than raise expectations beyond what is possible. 

The potential impact of our findings is profound. For practitioners and policymakers, this insight offers a new lens through which to evaluate the failure and success of public policies. Our work advocates a paradigm shift in how we perceive, manage, and learn from policy failures in the public sector, and for the expectations we have towards learning and the use of evidence in policymaking. By embracing the limitations of innovation in public policy, we can better manage expectations and structure the narrative regarding the capacity of public policy to address collective problems…(More)”.


The Character of Consent

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Book by Meg Leta Jones about The History of Cookies and the Future of Technology Policy: “Consent pop-ups continually ask us to download cookies to our computers, but is this all-too-familiar form of privacy protection effective? No, Meg Leta Jones explains in The Character of Consent, rather than promote functionality, privacy, and decentralization, cookie technology has instead made the internet invasive, limited, and clunky. Good thing, then, that the cookie is set for retirement in 2024. In this eye-opening book, Jones tells the little-known story of this broken consent arrangement, tracing it back to the major transnational conflicts around digital consent over the last twenty-five years. What she finds is that the policy controversy is not, in fact, an information crisis—it’s an identity crisis.

Instead of asking how people consent, Jones asks who exactly is consenting and to what. Packed into those cookie pop-ups, she explains, are three distinct areas of law with three different characters who can consent. Within (mainly European) data protection law, the data subject consents. Within communication privacy law, the user consents. And within consumer protection law, the privacy consumer consents. These areas of law have very different histories, motivations, institutional structures, expertise, and strategies, so consent—and the characters who can consent—plays a unique role in those areas of law….(More)”.

Framework for Governance of Indigenous Data (GID)

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Framework by The National Indigenous Australians Agency (NIAA): “Australian Public Service agencies now have a single Framework for working with Indigenous data.

The National Indigenous Australians Agency will collaborate across the Australian Public Service to implement the Framework for Governance of Indigenous Data in 2024.

Commonwealth agencies are expected to develop a seven-year implementation plan, guided by four principles:

  1. Partner with Aboriginal and Torres Strait Islander people
  2. Build data-related capabilities
  3. Provide knowledge of data assets
  4. Build an inclusive data system

The Framework represents the culmination of over 18 months of co-design effort between the Australian Government and Aboriginal and Torres Strait Islander partners. While we know we have some way to go, the Framework serves as a significant step forward to improve the collection, use and disclosure of data, to better serve Aboriginal and Torres Strait Islander priorities.

The Framework places Aboriginal and Torres Strait Islander peoples at its core. Recognising the importance of authentic engagement, it emphasises the need for First Nations communities to have a say in decisions affecting them, including the use of data in government policy-making.

Acknowledging data’s significance in self-determination, the Framework provides a stepping stone towards greater awareness and acceptance by Australian Government agencies of the principles of Indigenous Data Sovereignty.

It offers practical guidance on implementing key aspects of data governance aligned with both Indigenous Data Sovereignty principles and the objectives of the Australian Government…(More)”.

Now we are all measuring impact — but is anything changing?

Curated on Posted on by Stefaan Verhulst

Article by Griffith Centre for Systems Innovation: “…Increasingly the landscape of Impact Measurement is crowded, dynamic and contains a diversity of frameworks and approaches — which can mean we end up feeling like we’re looking at alphabet soup.

As we’ve traversed this landscape we’ve tried to make sense of it in various ways, and have begun to explore a matrix to represent the constellation of frameworks, approaches and models we’ve encountered in the process. As shown below, the matrix has two axes:

The horizontal axis provides us with a “time” delineation. Dividing the left and right sides between retrospective (ex post) and prospective (ex-ante) approaches to measuring impact.

More specifically the retrospective quadrants include approaches/frameworks/models that ask about events in the past: What impact did we have? While the prospective quadrants include approaches that ask about the possible future: What impact will we have?

The vertical axis provides us with a “purpose” delineation. Dividing the upper and lower parts between Impact Measurement + Management and Evaluation

The top-level quadrants, Impact Measurement + Management, focus on methods that count quantifiable data (i.e. time, dollars, widgets). These frameworks tend to measure outputs from activities/interventions. They tend to ask the question what happened or what could happen and rely significantly on quantitative data.

The bottom-level Evaluation quadrants include a range of approaches that look at a broader range of questions beyond counting. They include questions like: what changed and why? What was or might the interrelationships between changes be? They tend to draw on a mixture of quantitative and qualitative data to create a more cohesive understanding of changes that occurred, are occurring or could occur.

A word of warning: As with all frameworks, this matrix is a “construct” — a way for us to engage in sense-making and to critically discuss how impact measurement is being undertaken in our current context. We are sharing this as a starting point for a broader discussion. We welcome feedback, reflections, and challenges around how we have represented different approaches — we are not seeking a ‘true representation’, but rather, a starting point for dialogue about how all the methods that now abound are connected, entangled and constructed…(More)”

Can Artificial Intelligence Bring Deliberation to the Masses?

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Chapter by Hélène Landemore: “A core problem in deliberative democracy is the tension between two seemingly equally important conditions of democratic legitimacy: deliberation, on the one hand, and mass participation, on the other. Might artificial intelligence help bring quality deliberation to the masses? The answer is a qualified yes. The chapter first examines the conundrum in deliberative democracy around the trade-off between deliberation and mass participation by returning to the seminal debate between Joshua Cohen and Jürgen Habermas. It then turns to an analysis of the 2019 French Great National Debate, a low-tech attempt to involve millions of French citizens in a two-month-long structured exercise of collective deliberation. Building on the shortcomings of this process, the chapter then considers two different visions for an algorithm-powered form of mass deliberation—Mass Online Deliberation (MOD), on the one hand, and Many Rotating Mini-publics (MRMs), on the other—theorizing various ways artificial intelligence could play a role in them. To the extent that artificial intelligence makes the possibility of either vision more likely to come to fruition, it carries with it the promise of deliberation at the very large scale….(More)”

Embracing the Social in Social Science

Curated on Posted on by Stefaan Verhulst

Article by Jay Lloyd: “In a world where science is inextricably intermixed with society, the social sciences are essential to building trust in the scientific enterprise.

To begin thinking about why all the sciences should embrace the social in social science, I would like to start with cupcakes.

In my research, context is a recurring theme, so let me give you some context for cupcakes as metaphor. A few months ago, when I was asked to respond to an article in this magazine, I wrote: “In the production of science, social scientists can often feel like sprinkles on a cupcake: not essential. Social science is not the egg, the flour, or the sugar. Sprinkles are neither in the batter, nor do they see the oven. Sprinkles are a late addition. No matter the stylistic or aesthetic impact, they never alter the substance of the ‘cake’ in the cupcake.”

In writing these sentences, I was, and still am, hopeful that all kinds of future scientific research will make social science a key component of the scientific “batter” and bake social scientific knowledge, skill, and expertise into twenty-first-century scientific “cupcakes.”

But there are tensions and power differentials in the ways interdisciplinary science can be done. Most importantly, the formation of questions itself is a site of power. The questions we as a society ask science to address both reflect and create the values and power dynamics of social systems, whether the scientific disciplines recognize this influence or not. And some of those knowledge systems do not embrace the importance of insights from the social sciences because many institutions of science work hard to insulate the practice of science from the contingencies of society.

Moving forward, how do we, as researchers, develop questions that not only welcome intellectual variety within the sciences but also embrace the diversity represented in societies? As science continues to more powerfully blend, overlap, and intermix with society, embracing what social science can bring to the entire scientific enterprise is necessary. In order to accomplish these important goals, social concerns must be a key ingredient of the whole cupcake—not an afterthought, or decoration, but among the first thoughts…(More)”

Artificial Intelligence Opportunities for State and Local Departments Of Transportation

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Report by the National Academies of Sciences, Engineering, and Medicine: “Artificial intelligence (AI) has revolutionized various areas in departments of transportation (DOTs), such as traffic management and optimization. Through predictive analytics and real-time data processing, AI systems show promise in alleviating congestion, reducing travel times, and enhancing overall safety by alerting drivers to potential hazards. AI-driven simulations are also used for testing and improving transportation systems, saving time and resources that would otherwise be needed for physical tests…(More)”.