Paper by Linnet Taylor, Siddharth Peter de Souza, Aaron Martin, and Joan López Solano: “The field of data justice has been evolving to take into account the role of data in powering the field of artificial intelligence (AI). In this paper we review the main conceptual bases for governing data and AI: the market-based approach, the personal–non-personal data distinction and strategic sovereignty. We then analyse how these are being operationalised into practical models for governance, including public data trusts, data cooperatives, personal data sovereignty, data collaboratives, data commons approaches and indigenous data sovereignty. We interrogate these models’ potential for just governance based on four benchmarks which we propose as a reformulation of the Data Justice governance agenda identified by Taylor in her 2017 framework. Re-situating data justice at the intersection of data and AI, these benchmarks focus on preserving and strengthening public infrastructures and public goods; inclusiveness; contestability and accountability; and global responsibility. We demonstrate how they can be used to test whether a governance approach will succeed in redistributing power, engaging with public concerns and creating a plural politics of AI…(More)”.
Artificial Intelligence Narratives
A Global Voices Report: “…Framing AI systems as intelligent is further complicated and intertwined with neighboring narratives. In the US, AI narratives often revolve around opposing themes such as hope and fear, often bridging two strong emotions: existential fears and economic aspirations. In either case, they propose that the technology is powerful. These narratives contribute to the hype surrounding AI tools and their potential impact on society. Some examples include:
- An “AI arms race” between the United States and other global powers, particularly China.
- AI is a driver of economic growth and a transformative agent reshaping society.
- AI is a threat to labor.
- AI as a tool for good to benefit society.
- AI is an inevitable force that requires urgent regulation.
- If you don’t use AI, you will fall behind.
- AI is a democratizing force, lowering barriers to entry globally.
Many of these framings often present AI as an unstoppable and accelerating force. While this narrative can generate excitement and investment in AI research, it can also contribute to a sense of technological determinism and a lack of critical engagement with the consequences of widespread AI adoption. Counter-narratives are many and expand on the motifs of surveillance, erosions of trust, bias, job impacts, exploitation of labor, high-risk uses, the concentration of power, and environmental impacts, among others.
These narrative frames, combined with the metaphorical language and imagery used to describe AI, contribute to the confusion and lack of public knowledge about the technology. By positioning AI as a transformative, inevitable, and necessary tool for national success, these narratives can shape public opinion and policy decisions, often in ways that prioritize rapid adoption and commercialization…(More)”
Information Ecosystems and Troubled Democracy
Report by the Observatory on Information and Democracy: “This inaugural meta-analysis provides a critical assessment of the role of information ecosystems in the Global North and Global Majority World, focusing on their relationship with information integrity (the quality of public discourse), the fairness of political processes, the protection of media freedoms, and the resilience of public institutions.
The report addresses three thematic areas with a cross-cutting theme of mis- and disinformation:
- Media, Politics and Trust;
- Artificial Intelligence, Information Ecosystems and Democracy;
- and Data Governance and Democracy.
The analysis is based mainly on academic publications supplemented by reports and other materials from different disciplines and regions (1,664 citations selected among a total corpus of over +2700 resources aggregated). The report showcases what we can learn from landmark research on often intractable challenges posed by rapid changes in information and communication spaces…(More)”.
What’s a Fact, Anyway?
Essay by Fergus McIntosh: “…For journalists, as for anyone, there are certain shortcuts to trustworthiness, including reputation, expertise, and transparency—the sharing of sources, for example, or the prompt correction of errors. Some of these shortcuts are more perilous than others. Various outfits, positioning themselves as neutral guides to the marketplace of ideas, now tout evaluations of news organizations’ trustworthiness, but relying on these requires trusting in the quality and objectivity of the evaluation. Official data is often taken at face value, but numbers can conceal motives: think of the dispute over how to count casualties in recent conflicts. Governments, meanwhile, may use their powers over information to suppress unfavorable narratives: laws originally aimed at misinformation, many enacted during the COVID-19 pandemic, can hinder free expression. The spectre of this phenomenon is fuelling a growing backlash in America and elsewhere.
Although some categories of information may come to be considered inherently trustworthy, these, too, are in flux. For decades, the technical difficulty of editing photographs and videos allowed them to be treated, by most people, as essentially incontrovertible. With the advent of A.I.-based editing software, footage and imagery have swiftly become much harder to credit. Similar tools are already used to spoof voices based on only seconds of recorded audio. For anyone, this might manifest in scams (your grandmother calls, but it’s not Grandma on the other end), but for a journalist it also puts source calls into question. Technologies of deception tend to be accompanied by ones of detection or verification—a battery of companies, for example, already promise that they can spot A.I.-manipulated imagery—but they’re often locked in an arms race, and they never achieve total accuracy. Though chatbots and A.I.-enabled search engines promise to help us with research (when a colleague “interviewed” ChatGPT, it told him, “I aim to provide information that is as neutral and unbiased as possible”), their inability to provide sourcing, and their tendency to hallucinate, looks more like a shortcut to nowhere, at least for now. The resulting problems extend far beyond media: election campaigns, in which subtle impressions can lead to big differences in voting behavior, feel increasingly vulnerable to deepfakes and other manipulations by inscrutable algorithms. Like everyone else, journalists have only just begun to grapple with the implications.
In such circumstances, it becomes difficult to know what is true, and, consequently, to make decisions. Good journalism offers a way through, but only if readers are willing to follow: trust and naïveté can feel uncomfortably close. Gaining and holding that trust is hard. But failure—the end point of the story of generational decay, of gold exchanged for dross—is not inevitable. Fact checking of the sort practiced at The New Yorker is highly specific and resource-intensive, and it’s only one potential solution. But any solution must acknowledge the messiness of truth, the requirements of attention, the way we squint to see more clearly. It must tell you to say what you mean, and know that you mean it…(More)”.
Governance of Indigenous data in open earth systems science
Paper by Lydia Jennings et al: “In the age of big data and open science, what processes are needed to follow open science protocols while upholding Indigenous Peoples’ rights? The Earth Data Relations Working Group (EDRWG), convened to address this question and envision a research landscape that acknowledges the legacy of extractive practices and embraces new norms across Earth science institutions and open science research. Using the National Ecological Observatory Network (NEON) as an example, the EDRWG recommends actions, applicable across all phases of the data lifecycle, that recognize the sovereign rights of Indigenous Peoples and support better research across all Earth Sciences…(More)”
Facing & mitigating common challenges when working with real-world data: The Data Learning Paradigm
Paper by Jake Lever et al: “The rapid growth of data-driven applications is ubiquitous across virtually all scientific domains, and has led to an increasing demand for effective methods to handle data deficiencies and mitigate the effects of imperfect data. This paper presents a guide for researchers encountering real-world data-driven applications, and the respective challenges associated with this. This article proposes the concept of the Data Learning Paradigm, combining the principles of machine learning, data science and data assimilation to tackle real-world challenges in data-driven applications. Models are a product of the data upon which they are trained, and no data collected from real world scenarios is perfect due to natural limitations of sensing and collection. Thus, computational modelling of real world systems is intrinsically limited by the various deficiencies encountered in real data. The Data Learning Paradigm aims to leverage the strengths of data improvement to enhance the accuracy, reliability, and interpretability of data-driven models. We outline a range of methods which are currently being implemented in the field of Data Learning involving machine learning and data science methods, and discuss how these mitigate the various problems associated with data-driven models, illustrating improved results in a multitude of real world applications. We highlight examples where these methods have led to significant advancements in fields such as environmental monitoring, planetary exploration, healthcare analytics, linguistic analysis, social networks, and smart manufacturing. We offer a guide to how these methods may be implemented to deal with general types of limitations in data, alongside their current and potential applications…(More)”.
Sortition: Past and Present
Introduction to the Journal of Sortition: “Since ancient times sortition (random selection by lot) has been used both to distribute political office and as a general prophylactic against factionalism and corruption in societies as diverse as classical-era Athens and the Most Serene Republic of Venice. Lotteries have also been employed for the allocation of scarce goods such as social housing and school places to eliminate bias and ensure just distribution, along with drawing lots in circumstances where unpopular tasks or tragic choices are involved (as some situations are beyond rational human decision-making). More recently, developments in public opinion polling using random sampling have led to the proliferation of citizens’ assemblies selected by lot. Some activists have even proposed such bodies as an alternative to elected representatives. The Journal of Sortition benefits from an editorial board with a wide range of expertise and perspectives in this area. In this introduction to the first issue, we have invited our editors to explain why they are interested in sortition, and to outline the benefits (and pitfalls) of the recent explosion of interest in the topic…(More)”.
Digitalizing sewage: The politics of producing, sharing, and operationalizing data from wastewater-based surveillance
Paper by Josie Wittmer, Carolyn Prouse, and Mohammed Rafi Arefin: “Expanded during the COVID-19 pandemic, Wastewater-Based Surveillance (WBS) is now heralded by scientists and policy makers alike as the future of monitoring and governing urban health. The expansion of WBS reflects larger neoliberal governance trends whereby digitalizing states increasingly rely on producing big data as a ‘best practice’ to surveil various aspects of everyday life. With a focus on three South Asian cities, our paper investigates the transnational pathways through which WBS data is produced, made known, and operationalized in ‘evidence-based’ decision-making in a time of crisis. We argue that in South Asia, wastewater surveillance data is actively produced through fragile but power-laden networks of transnational and local knowledge, funding, and practices. Using mixed qualitative methods, we found these networks produced artifacts like dashboards to communicate data to the public in ways that enabled claims to objectivity, ethical interventions, and transparency. Interrogating these representations, we demonstrate how these artifacts open up messy spaces of translation that trouble linear notions of objective data informing accountable, transparent, and evidence-based decision-making for diverse urban actors. By thinking through the production of precarious biosurveillance infrastructures, we respond to calls for more robust ethical and legal frameworks for the field and suggest that the fragility of WBS infrastructures has important implications for the long-term trajectories of urban public health governance in the global South…(More)”
Will Artificial Intelligence Replace Us or Empower Us?
Article by Peter Coy: “…But A.I. could also be designed to empower people rather than replace them, as I wrote a year ago in a newsletter about the M.I.T. Shaping the Future of Work Initiative.
Which of those A.I. futures will be realized was a big topic at the San Francisco conference, which was the annual meeting of the American Economic Association, the American Finance Association and 65 smaller groups in the Allied Social Science Associations.
Erik Brynjolfsson of Stanford was one of the busiest economists at the conference, dashing from one panel to another to talk about his hopes for a human-centric A.I. and his warnings about what he has called the “Turing Trap.”
Alan Turing, the English mathematician and World War II code breaker, proposed in 1950 to evaluate the intelligence of computers by whether they could fool someone into thinking they were human. His “imitation game” led the field in an unfortunate direction, Brynjolfsson argues — toward creating machines that behaved as much like humans as possible, instead of like human helpers.
Henry Ford didn’t set out to build a car that could mimic a person’s walk, so why should A.I. experts try to build systems that mimic a person’s mental abilities? Brynjolfsson asked at one session I attended.
Other economists have made similar points: Daron Acemoglu of M.I.T. and Pascual Restrepo of Boston University use the term “so-so technologies” for systems that replace human beings without meaningfully increasing productivity, such as self-checkout kiosks in supermarkets.
People will need a lot more education and training to take full advantage of A.I.’s immense power, so that they aren’t just elbowed aside by it. “In fact, for each dollar spent on machine learning technology, companies may need to spend nine dollars on intangible human capital,” Brynjolfsson wrote in 2022, citing research by him and others…(More)”.
AI Is Bad News for the Global South
Article by Rachel Adams: “…AI’s adoption in developing regions is also limited by its design. AI designed in Silicon Valley on largely English-language data is not often fit for purpose outside of wealthy Western contexts. The productive use of AI requires stable internet access or smartphone technology; in sub-Saharan Africa, only 25 percent of people have reliable internet access, and it is estimated that African women are 32 percent less likely to use mobile internet than their male counterparts.
Generative AI technologies are also predominantly developed using the English language, meaning that the outputs they produce for non-Western users and contexts are oftentimes useless, inaccurate, and biased. Innovators in the global south have to put in at least twice the effort to make their AI applications work for local contexts, often by retraining models on localized datasets and through extensive trial and error practices.
Where AI is designed to generate profit and entertainment only for the already privileged, it will not be effective in addressing the conditions of poverty and in changing the lives of groups that are marginalized from the consumer markets of AI. Without a high level of saturation across major industries, and without the infrastructure in place to enable meaningful access to AI by all people, global south nations are unlikely to see major economic benefits from the technology.
As AI is adopted across industries, human labor is changing. For poorer countries, this is engendering a new race to the bottom where machines are cheaper than humans and the cheap labor that was once offshored to their lands is now being onshored back to wealthy nations. The people most impacted are those with lower education levels and fewer skills, whose jobs can be more easily automated. In short, much of the population in lower- and middle-income countries may be affected, severely impacting the lives of millions of people and threatening the capacity of poorer nations to prosper…(More)”.