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)”.
Artificial intelligence and the local government: A five-decade scientometric analysis on the evolution, state-of-the-art, and emerging trends
Paper by Tan Yigitcanlar et al: “In recent years, the rapid advancement of artificial intelligence (AI) technologies has significantly impacted various sectors, including public governance at the local level. However, there exists a limited understanding of the overarching narrative surrounding the adoption of AI in local governments and its future. Therefore, this study aims to provide a comprehensive overview of the evolution, current state-of-the-art, and emerging trends in the adoption of AI in local government. A comprehensive scientometric analysis was conducted on a dataset comprising 7112 relevant literature records retrieved from the Scopus database in October 2023, spanning over the last five decades. The study findings revealed the following key insights: (a) exponential technological advancements over the last decades ushered in an era of AI adoption by local governments; (b) the primary purposes of AI adoption in local governments include decision support, automation, prediction, and service delivery; (c) the main areas of AI adoption in local governments encompass planning, analytics, security, surveillance, energy, and modelling; and (d) under-researched but critical research areas include ethics of and public participation in AI adoption in local governments. This study informs research, policy, and practice by offering a comprehensive understanding of the literature on AI applications in local governments, providing valuable insights for stakeholders and decision-makers…(More)”.
Brazil hires OpenAI to cut costs of court battles
Article by Marcela Ayres and Bernardo Caram: “Brazil’s government is hiring OpenAI to expedite the screening and analysis of thousands of lawsuits using artificial intelligence (AI), trying to avoid costly court losses that have weighed on the federal budget.
The AI service will flag to government the need to act on lawsuits before final decisions, mapping trends and potential action areas for the solicitor general’s office (AGU).
AGU told Reuters that Microsoft would provide the artificial intelligence services from ChatGPT creator OpenAI through its Azure cloud-computing platform. It did not say how much Brazil will pay for the services.
Court-ordered debt payments have consumed a growing share of Brazil’s federal budget. The government estimated it would spend 70.7 billion reais ($13.2 billion) next year on judicial decisions where it can no longer appeal. The figure does not include small-value claims, which historically amount to around 30 billion reais annually.
The combined amount of over 100 billion reais represents a sharp increase from 37.3 billion reais in 2015. It is equivalent to about 1% of gross domestic product, or 15% more than the government expects to spend on unemployment insurance and wage bonuses to low-income workers next year.
AGU did not provide a reason for Brazil’s rising court costs…(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)”
Designing for AI Transparency in Public Services: A User-Centred Study of Citizens’ Preferences
Paper by Stefan Schmager, Samrat Gupta, Ilias Pappas & Polyxeni Vassilakopoulou: “Enhancing transparency in AI enabled public services has the potential to improve their adoption and service delivery. Hence, it is important to identify effective design strategies for AI transparency in public services. To this end, we conduct this empirical qualitative study providing insights for responsible deployment of AI in practice by public organizations. We design an interactive prototype for a Norwegian public welfare service organization which aims to use AI to support sick leaves related services. Qualitative analysis of citizens’ data collected through survey, think-aloud interactions with the prototype, and open-ended questions revealed three key themes related to: articulating information in written form, representing information in graphical form, and establishing the appropriate level of information detail for improving AI transparency in public service delivery. This study advances research pertaining to design of public service portals and has implications for AI implementation in the public sector…(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)”.
Blueprints for Learning
Report by the Data Foundation: “The Foundations for Evidence-Based Policymaking Act of 2018 (Evidence Act) required the creation of learning agendas for the largest federal agencies. These agendas outline how agencies will identify and answer priority questions through data and evidence-building activities. The Data Foundation undertook an analysis of the agendas to understand how they were developed and plans for implementation as part of the 5-Year milestone of the Evidence Act.
The analysis reveals both progress and areas for improvement in the development and use of learning agendas. All but one large agency produced a publicly-available learning agenda, demonstrating a significant initial effort. However, several challenges were identified:
- Limited detail on execution and use: Many learning agendas lacked specifics on how the identified priority questions would be addressed or how the evidence generated would be used.
- Variation in quality: Agencies diverged in the comprehensiveness and clarity of their agendas, with some providing more detailed plans than others.
- Resource constraints: The analysis suggests that a lack of dedicated resources may be hindering some agencies’ capacity to fully implement their learning agendas…(More)”.
Are We Ready for the Next Pandemic? Navigating the First and Last Mile Challenges in Data Utilization
Blog by Stefaan Verhulst, Daniela Paolotti, Ciro Cattuto and Alessandro Vespignani:
“Public health officials from around the world are gathering this week in Geneva for a weeklong meeting of the 77th World Health Assembly. A key question they are examining is: Are we ready for the next pandemic? As we have written elsewhere, regarding access to and re-use of data, particularly non-traditional data, for pandemic preparedness and response: we are not. Below, we list ten recommendations to advance access to and reuse of non-traditional data for pandemics, drawing on input from a high-level workshop, held in Brussels, within the context of the ESCAPE program…(More)”
As