Data Statements: From Technical Concept to Community Practice


Paper by Angelina McMillan-Major, Emily M. Bender, and Batya Friedman: “Responsible computing ultimately requires that technical communities develop and adopt tools, processes, and practices that mitigate harms and support human flourishing. Prior efforts toward the responsible development and use of datasets, machine learning models, and other technical systems have led to the creation of documentation toolkits to facilitate transparency, diagnosis, and inclusion. This work takes the next step: to catalyze community uptake, alongside toolkit improvement. Specifically, starting from one such proposed toolkit specialized for language datasets, data statements for natural language processing, we explore how to improve the toolkit in three senses: (1) the content of the toolkit itself, (2) engagement with professional practice, and (3) moving from a conceptual proposal to a tested schema that the intended community of use may readily adopt. To achieve these goals, we first conducted a workshop with natural language processing practitioners to identify gaps and limitations of the toolkit as well as to develop best practices for writing data statements, yielding an interim improved toolkit. Then we conducted an analytic comparison between the interim toolkit and another documentation toolkit, datasheets for datasets. Based on these two integrated processes, we present our revised Version 2 schema and best practices in a guide for writing data statements. Our findings more generally provide integrated processes for co-evolving both technology and practice to address ethical concerns within situated technical communities…(More)”

Green Light


Google Research: “Road transportation is responsible for a significant amount of global and urban greenhouse gas emissions. It is especially problematic at city intersections where pollution can be 29 times higher than on open roads.  At intersections, half of these emissions come from traffic accelerating after stopping. While some amount of stop-and-go traffic is unavoidable, part of it is preventable through the optimization of traffic light timing configurations. To improve traffic light timing, cities need to either install costly hardware or run manual vehicle counts; both of these solutions are expensive and don’t provide all the necessary information. 

Green Light uses AI and Google Maps driving trends, with one of the strongest understandings of global road networks, to model traffic patterns and build intelligent recommendations for city traffic engineers to optimize traffic flow. Early numbers indicate a potential for up to 30% reduction in stops and 10% reduction in greenhouse gas emissions (1). By optimizing each intersection, and coordinating between adjacent intersections, we can create waves of green lights and help cities further reduce stop-and-go traffic. Green Light is now live in 70 intersections in 12 cities, 4 continents, from Haifa, Israel to Bangalore, India to Hamburg, Germany – and in these intersections we are able to save fuel and lower emissions for up to 30M car rides monthly. Green Light reflects Google Research’s commitment to use AI to address climate change and improve millions of lives in cities around the world…(More)”

Effects of Open Access. Literature study on empirical research 2010–2021


Paper by David Hopf, Sarah Dellmann, Christian Hauschke, and Marco Tullney: “Open access — the free availability of scholarly publications — intuitively offers many benefits. At the same time, some academics, university administrators, publishers, and political decision-makers express reservations. Many empirical studies on the effects of open access have been published in the last decade. This report provides an overview of the state of research from 2010 to 2021. The empirical results on the effects of open access help to determine the advantages and disadvantages of open access and serve as a knowledge base for academics, publishers, research funding and research performing institutions, and policy makers. This overview of current findings can inform decisions about open access and publishing strategies. In addition, this report identifies aspects of the impact of open access that are potentially highly relevant but have not yet been sufficiently studied…(More)”.

Superconvergence


Book by Jamie Metzl: “…explores how artificial intelligence, genome sequencing, gene editing, and other revolutionary technologies are transforming our lives, world, and future. These accelerating and increasingly interconnected technologies have the potential to improve our health, feed billions of people, supercharge our economies, store essential information for millions of years, and save our planet, but they can also―if we are not careful―do immeasurable harm.

The challenge we face is that while our ability to engineer the world around us is advancing exponentially, our processes for understanding the scope, scale, and implications of these changes, and for managing our godlike powers wisely, are only inching forward glacially…(More)”.

Artificial Intelligence Applications for Social Science Research


Report by Megan Stubbs-Richardson et al: “Our team developed a database of 250 Artificial Intelligence (AI) applications useful for social science research. To be included in our database, the AI tool had to be useful for: 1) literature reviews, summaries, or writing, 2) data collection, analysis, or visualizations, or 3) research dissemination. In the database, we provide a name, description, and links to each of the AI tools that were current at the time of publication on September 29, 2023. Supporting links were provided when an AI tool was found using other databases. To help users evaluate the potential usefulness of each tool, we documented information about costs, log-in requirements, and whether plug-ins or browser extensions are available for each tool. Finally, as we are a team of scientists who are also interested in studying social media data to understand social problems, we also documented when the AI tools were useful for text-based data, such as social media. This database includes 132 AI tools that may have use for literature reviews or writing; 146 tools that may have use for data collection, analyses, or visualizations; and 108 that may be used for dissemination efforts. While 170 of the AI tools within this database can be used for general research purposes, 18 are specific to social media data analyses, and 62 can be applied to both. Our database thus offers some of the recently published tools for exploring the application of AI to social science research…(More)”

The Deliberative Turn in Democratic Theory


Book by Antonino Palumbo: “Thirty years of developments in deliberative democracy (DD) have consolidated this subfield of democratic theory. The acquired disciplinary prestige has made theorist and practitioners very confident about the ability of DD to address the legitimacy crisis experienced by liberal democracies at present at both theoretical and practical levels. The book advance a critical analysis of these developments that casts doubts on those certainties — current theoretical debates are reproposing old methodological divisions, and are afraid to move beyond the minimalist model of democracy advocated by liberal thinkers; democratic experimentation at the micro-level seems to have no impact at the macro-level, and remain sets of isolated experiences. The book indicates that those defects are mainly due to the liberal minimalist frame of reference within which reflection in democratic theory and practice takes place. Consequently, it suggests to move beyond liberal understandings of democracy as a game in need of external rules, and adopt instead a vision of democracy as a self-correcting metagame…(More)”.

Using Artificial Intelligence to Accelerate Collective Intelligence


Paper by Róbert Bjarnason, Dane Gambrell and Joshua Lanthier-Welch: “In an era characterized by rapid societal changes and complex challenges, institutions’ traditional methods of problem-solving in the public sector are increasingly proving inadequate. In this study, we present an innovative and effective model for how institutions can use artificial intelligence to enable groups of people to generate effective solutions to urgent problems more efficiently. We describe a proven collective intelligence method, called Smarter Crowdsourcing, which is designed to channel the collective intelligence of those with expertise about a problem into actionable solutions through crowdsourcing. Then we introduce Policy Synth, an innovative toolkit which leverages AI to make the Smarter Crowdsourcing problem-solving approach both more scalable, more effective and more efficient. Policy Synth is crafted using a human-centric approach, recognizing that AI is a tool to enhance human intelligence and creativity, not replace it. Based on a real-world case study comparing the results of expert crowdsourcing alone with expert sourcing supported by Policy Synth AI agents, we conclude that Smarter Crowdsourcing with Policy Synth presents an effective model for integrating the collective wisdom of human experts and the computational power of AI to enhance and scale up public problem-solving processes.

The potential for artificial intelligence to enhance the performance of groups of people has been a topic of great interest among scholars of collective intelligence. Though many AI toolkits exist, they too often are not fitted to the needs of institutions and policymakers. While many existing approaches view AI as a tool to make crowdsourcing and deliberative processes better and more efficient, Policy Synth goes a step further, recognizing that AI can also be used to synthesize the findings from engagements together with research to develop evidence-based solutions and policies. This study contributes significantly to the fields of collective intelligence, public problem-solving, and AI. The study offers practical tools and insights for institutions looking to engage communities effectively in addressing urgent societal challenges…(More)”

The tensions of data sharing for human rights: A modern slavery case study


Paper by Jamie Hancock et al: “There are calls for greater data sharing to address human rights issues. Advocates claim this will provide an evidence-base to increase transparency, improve accountability, enhance decision-making, identify abuses, and offer remedies for rights violations. However, these well-intentioned efforts have been found to sometimes enable harms against the people they seek to protect. This paper shows issues relating to fairness, accountability, or transparency (FAccT) in and around data sharing can produce such ‘ironic’ consequences. It does so using an empirical case study: efforts to tackle modern slavery and human trafficking in the UK. We draw on a qualitative analysis of expert interviews, workshops, ecosystem mapping exercises, and a desk-based review. The findings show how, in the UK, a large ecosystem of data providers, hubs, and users emerged to process and exchange data from across the country. We identify how issues including legal uncertainties, non-transparent sharing procedures, and limited accountability regarding downstream uses of data may undermine efforts to tackle modern slavery and place victims of abuses at risk of further harms. Our findings help explain why data sharing activities can have negative consequences for human rights, even within human rights initiatives. Moreover, our analysis offers a window into how FAccT principles for technology relate to the human rights implications of data sharing. Finally, we discuss why these tensions may be echoed in other areas where data sharing is pursued for human rights concerns, identifying common features which may lead to similar results, especially where sensitive data is shared to achieve social goods or policy objectives…(More)”.

The revolution shall not be automated: On the political possibilities of activism through data & AI


Article by Isadora Cruxên: “Every other day now, there are headlines about some kind of artificial intelligence (AI) revolution that is taking place. If you read the news or check social media regularly, you have probably come across these too: flashy pieces either trumpeting or warning against AI’s transformative potential. Some headlines promise that AI will fundamentally change how we work and learn or help us tackle critical challenges such as biodiversity conservation and climate change. Others question its intelligence, point to its embedded biases, and draw attention to its extractive labour record and high environmental costs.

Scrolling through these headlines, it is easy to feel like the ‘AI revolution’ is happening to us — or perhaps blowing past us at speed — while we are enticed to take the backseat and let AI-powered chat-boxes like ChatGPT do the work. But the reality is that we need to take the driver’s seat.

If we want to leverage this technology to advance social justice and confront the intersecting socio-ecological challenges before us, we need to stop simply wondering what the AI revolution will do to us and start thinking collectively about how we can produce data and AI models differently. As Mimi Ọnụọha and Mother Cyborg put it in A People’s Guide to AI, “the path to a fair future starts with the humans behind the machines, not the machines themselves.”

Sure, this might seem easier said than done. Most AI research and development is being driven by big tech corporations and start-ups. As Lauren Klein and Catherine D’Ignazio discuss in “Data Feminism for AI” (see “Further reading” at the end for all works cited), the results are models, tools, and platforms that are opaque to users, and that cater to the tech ambitions and profit motives of private actors, with broader societal needs and concerns becoming afterthoughts. There is excellent critical work that explores the extractive practices and unequal power relations that underpin AI production, including its relationship to processes of dataficationcolonial data epistemologies, and surveillance capitalism (to link but a few). Interrogating, illuminating, and challenging these dynamics is paramount if we are to take the driver’s seat and find alternative paths…(More)”.

Inclusive by default: strategies for more inclusive participation


Article by Luiza Jardim and Maria Lucien: “…The systemic challenges that marginalised groups face are pressing and require action. The global average age of parliamentarians is 53, highlighting a gap in youth representation. Young people already face challenges like poverty, lack of education, unemployment and multiple forms of discrimination. Additionally, some participatory formats are often unappealing to young people and pose a challenge for engaging them. Gender equity research highlights the underrepresentation of women at all levels of decision-making and governance. Despite recent improvements, gender parity in governance worldwide is still decades or even centuries away. Meanwhile, ongoing global conflicts in Ukraine, Sudan, Gaza and elsewhere, as well as the impacts of a changing climate, have driven the recent increase in the number of forcibly displaced people to more than 100 million. The engagement of these individuals in decision-making can vary greatly depending on their specific circumstances and the nature of their displacement.

Participatory and deliberative democracy can have transformative impacts on historically marginalised communities but only if they are intentionally included in program design and implementation. To start with, it’s possible to reduce the barriers to participation, such as the cost and time of transport to the participation venue, or burdens imposed by social and cultural roles in society, like childcare. During the process, mindful and attentive facilitation can help balance power dynamics and encourage participation from traditionally excluded people. This is further strengthened if the facilitation team includes and trains members of priority communities in facilitation and session planning…(More)”.