Paper by Leonie Rebecca Freise et al: “The rapid evolution of the software development industry challenges developers to manage their diverse tasks effectively. Traditional assistant tools in software development often fall short of supporting developers efficiently. This paper explores how generative artificial intelligence (GAI) tools, such as Github Copilot or ChatGPT, facilitate job crafting—a process where employees reshape their jobs to meet evolving demands. By integrating GAI tools into workflows, software developers can focus more on creative problem-solving, enhancing job satisfaction, and fostering a more innovative work environment. This study investigates how GAI tools influence task, cognitive, and relational job crafting behaviors among software developers, examining its implications for professional growth and adaptability within the industry. The paper provides insights into the transformative impacts of GAI tools on software development job crafting practices, emphasizing their role in enabling developers to redefine their job functions…(More)”.
AI Analysis of Body Camera Videos Offers a Data-Driven Approach to Police Reform
Article by Ingrid Wickelgren: But unless something tragic happens, body camera footage generally goes unseen. “We spend so much money collecting and storing this data, but it’s almost never used for anything,” says Benjamin Graham, a political scientist at the University of Southern California.
Graham is among a small number of scientists who are reimagining this footage as data rather than just evidence. Their work leverages advances in natural language processing, which relies on artificial intelligence, to automate the analysis of video transcripts of citizen-police interactions. The findings have enabled police departments to spot policing problems, find ways to fix them and determine whether the fixes improve behavior.
Only a small number of police agencies have opened their databases to researchers so far. But if this footage were analyzed routinely, it would be a “real game changer,” says Jennifer Eberhardt, a Stanford University psychologist, who pioneered this line of research. “We can see beat-by-beat, moment-by-moment how an interaction unfolds.”
In papers published over the past seven years, Eberhardt and her colleagues have examined body camera footage to reveal how police speak to white and Black people differently and what type of talk is likely to either gain a person’s trust or portend an undesirable outcome, such as handcuffing or arrest. The findings have refined and enhanced police training. In a study published in PNAS Nexus in September, the researchers showed that the new training changed officers’ behavior…(More)”.
Operational Learning
International Red Cross: “Operational learning in emergencies is the lesson learned from managing and dealing with crises, refining protocols for resource allocation, decision-making, communication strategies, and others. The summaries are generated using AI and Large Language Models, based on data coming from Final DREF Reports, Emergency Appeal reports and others…(More)”
The history of AI and power in government
Book chapter by Shirley Kempeneer: “…begins by examining the simultaneous development of statistics and the state. Drawing on the works of notable scholars like Alain Desrosières, Theodore Porter, James Scott, and Michel Foucault, the chapter explores measurement as a product of modernity. It discusses the politics and power of (large) numbers, through their ability to make societies legible and controllable, also in the context of colonialism. The chapter then discusses the shift from data to big data and how AI and the state, just like statistics and the state, are mutually constitutive. It zooms in on shifting power relations, discussing the militarization of society, the outsourcing of the state to tech contractors, the exploitation of human bodies under the guise of ‘automation’, and the oppression of vulnerable citizens. Where news media often focus on the power of AI, that is supposedly escaping our control, this chapter relocates power in AI-systems, building on the work of Kate Crawford, Bruno Latour, and Emily Bender…(More)”
Artificial Intelligence, Scientific Discovery, and Product Innovation
Paper by Aidan Toner-Rodgers: “… studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of “idea-generation” tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives. Together, these findings demonstrate the potential of AI-augmented research and highlight the complementarity between algorithms and expertise in the innovative process. Survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization…(More)”.
Voice and Access in AI: Global AI Majority Participation in Artificial Intelligence Development and Governance
Paper by Sumaya N. Adan et al: “Artificial intelligence (AI) is rapidly emerging as one of the most transformative technologies in human history, with the potential to profoundly impact all aspects of society globally. However, access to AI and participation in its development and governance is concentrated among a few countries with advanced AI capabilities, while the ‘Global AI Majority’ – defined as the population of countries primarily encompassing Africa, Latin America, South and Southeast Asia, and parts of Eastern Europe – is largely excluded. These regions, while diverse, share common challenges in accessing and influencing advanced AI technologies.
This white paper investigates practical remedies to increase voice in and access to AI governance and capabilities for the Global AI Majority, while addressing the security and commercial concerns of frontier AI states. We examine key barriers facing the Global AI Majority, including limited access to digital and compute infrastructure, power concentration in AI development, Anglocentric data sources, and skewed talent distributions. The paper also explores the dual-use dilemma of AI technologies and how it motivates frontier AI states to implement restrictive policies.
We evaluate a spectrum of AI development initiatives, ranging from domestic model creation to structured access to deployed models, assessing their feasibility for the Global AI Majority. To resolve governance dilemmas, we propose three key approaches: interest alignment, participatory architecture, and safety assurance…(More)”.
The Rise of AI-Generated Content in Wikipedia
Paper by Creston Brooks, Samuel Eggert, and Denis Peskoff: “The rise of AI-generated content in popular information sources raises significant concerns about accountability, accuracy, and bias amplification. Beyond directly impacting consumers, the widespread presence of this content poses questions for the long-term viability of training language models on vast internet sweeps. We use GPTZero, a proprietary AI detector, and Binoculars, an open-source alternative, to establish lower bounds on the presence of AI-generated content in recently created Wikipedia pages. Both detectors reveal a marked increase in AI-generated content in recent pages compared to those from before the release of GPT-3.5. With thresholds calibrated to achieve a 1% false positive rate on pre-GPT-3.5 articles, detectors flag over 5% of newly created English Wikipedia articles as AI-generated, with lower percentages for German, French, and Italian articles. Flagged Wikipedia articles are typically of lower quality and are often self-promotional or partial towards a specific viewpoint on controversial topics…(More)”
AI and Data Science for Public Policy
Introduction to Special Issue by Kenneth Benoit: “Artificial intelligence (AI) and data science are reshaping public policy by enabling more data-driven, predictive, and responsive governance, while at the same time producing profound changes in knowledge production and education in the social and policy sciences. These advancements come with ethical and epistemological challenges surrounding issues of bias, transparency, privacy, and accountability. This special issue explores the opportunities and risks of integrating AI into public policy, offering theoretical frameworks and empirical analyses to help policymakers navigate these complexities. The contributions explore how AI can enhance decision-making in areas such as healthcare, justice, and public services, while emphasising the need for fairness, human judgment, and democratic accountability. The issue provides a roadmap for harnessing AI’s potential responsibly, ensuring it serves the public good and upholds democratic values…(More)”.
Inside the New Nonprofit AI Initiatives Seeking to Aid Teachers and Farmers in Rural Africa
Article by Andrew R. Chow: “Over the past year, rural farmers in Malawi have been seeking advice about their crops and animals from a generative AI chatbot. These farmers ask questions in Chichewa, their native tongue, and the app, Ulangizi, responds in kind, using conversational language based on information taken from the government’s agricultural manual. “In the past we could wait for days for agriculture extension workers to come and address whatever problems we had on our farms,” Maron Galeta, a Malawian farmer, told Bloomberg. “Just a touch of a button we have all the information we need.”
The nonprofit behind the app, Opportunity International, hopes to bring similar AI-based solutions to other impoverished communities. In February, Opportunity ran an acceleration incubator for humanitarian workers across the world to pitch AI-based ideas and then develop them alongside mentors from institutions like Microsoft and Amazon. On October 30, Opportunity announced the three winners of this program: free-to-use apps that aim to help African farmers with crop and climate strategy, teachers with lesson planning, and school leaders with administration management. The winners will each receive about $150,000 in funding to pilot the apps in their communities, with the goal of reaching millions of people within two years.
Greg Nelson, the CTO of Opportunity, hopes that the program will show the power of AI to level playing fields for those who previously faced barriers to accessing knowledge and expertise. “Since the mobile phone, this is the biggest democratizing change that we have seen in our lifetime,” he says…(More)”.
The Routledge Handbook of Artificial Intelligence and Philanthropy
Open Access Book edited by Giuseppe Ugazio and Milos Maricic: “…acts as a catalyst for the dialogue between two ecosystems with much to gain from collaboration: artificial intelligence (AI) and philanthropy. Bringing together leading academics, AI specialists, and philanthropy professionals, it offers a robust academic foundation for studying both how AI can be used and implemented within philanthropy and how philanthropy can guide the future development of AI in a responsible way.
The contributors to this Handbook explore various facets of the AI‑philanthropy dynamic, critically assess hurdles to increased AI adoption and integration in philanthropy, map the application of AI within the philanthropic sector, evaluate how philanthropy can and should promote an AI that is ethical, inclusive, and responsible, and identify the landscape of risk strategies for their limitations and/or potential mitigation. These theoretical perspectives are complemented by several case studies that offer a pragmatic perspective on diverse, successful, and effective AI‑philanthropy synergies.
As a result, this Handbook stands as a valuable academic reference capable of enriching the interactions of AI and philanthropy, uniting the perspectives of scholars and practitioners, thus building bridges between research and implementation, and setting the foundations for future research endeavors on this topic…(More)”.