OECD working paper: “…explores the role of data governance in advancing people-centred justice systems. It outlines the objectives, values, and practices necessary to harness data effectively, drawing on OECD policy instruments. The paper provides actionable insights for policymakers aiming to implement data-driven justice reforms. It also addresses the challenges and opportunities presented by digital transformation in the justice sector, advocating for a strategic approach that balances innovation with the protection of fundamental rights. It incorporates lessons from data governance activities and experiences in justice and other relevant sectors. This paper is essential reading for those involved in modernisation of justice and data governance…(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)”.
Federated Data Infrastructures for Scientific Use
Policy paper by the German Council for Scientific Information Infrastructures: “…provides an overview and a comparative in-depth analysis of the emerging research (and research related) data infrastructures NFDI, EOSC, Gaia-X and the European Data Spaces. In addition, the Council makes recommendations for their future development and coordination. The RfII notes that access to genuine high-quality research data and related core services is a matter of basic public supply and strongly advises to achieve coherence between the various initiatives and approaches…(More)”.
Critical Datafication Literacy
Book by Ina Sander: “Despite the increasing influence of data technologies on our world, many people still lack a profound understanding of what this ›datafication‹ means for their lives and our societies. Ina Sander argues that this knowledge gap cannot be addressed by digital skills alone, but that more critical and empowering approaches are needed. Through a review of existing literacies, an analysis of established education concepts, and empirical research on online educational resources about datafication, she develops a framework for »critical datafication literacy«. Novel insights on the design strategies, pedagogical methods and challenges of practitioners who foster such education add to her analysis…(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)”.
Unlocking Green Deal Data: Innovative Approaches for Data Governance and Sharing in Europe
JRC Report: “Drawing upon the ambitious policy and legal framework outlined in the Europe Strategy for Data (2020) and the establishment of common European data spaces, this Science for Policy report explores innovative approaches for unlocking relevant data to achieve the objectives of the European Green Deal.
The report focuses on the governance and sharing of Green Deal data, analysing a variety of topics related to the implementation of new regulatory instruments, namely the Data Governance Act and the Data Act, as well as the roles of various actors in the data ecosystem. It provides an overview of the current incentives and disincentives for data sharing and explores the existing landscape of Data Intermediaries and Data Altruism Organizations. Additionally, it offers insights from a private sector perspective and outlines key data governance and sharing practices concerning Citizen-Generated Data (CGD).
The main conclusions build upon the concept of “Systemic Data Justice,” which emphasizes equity, accountability, and fair representation to foster stronger connections between the supply and demand of data for a more effective and sustainable data economy. Five policy recommendations outline a set of main implications and actionable points for the revision of the INSPIRE Directive (2007) within the context of the common European Green Deal data space, and toward a more sustainable and fair data ecosystem. However, the relevance of these recommendations spills over Green Deal data only, as they outline key elements to ensure that any data ecosystem is both just and impact-oriented…(More)”.
Effective Data Stewardship in Higher Education: Skills, Competences, and the Emerging Role of Open Data Stewards
Paper by Panos Fitsilis et al: “The significance of open data in higher education stems from the changing tendencies towards open science, and open research in higher education encourages new ways of making scientific inquiry more transparent, collaborative and accessible. This study focuses on the critical role of open data stewards in this transition, essential for managing and disseminating research data effectively in universities, while it also highlights the increasing demand for structured training and professional policies for data stewards in academic settings. Building upon this context, the paper investigates the essential skills and competences required for effective data stewardship in higher education institutions by elaborating on a critical literature review, coupled with practical engagement in open data stewardship at universities, provided insights into the roles and responsibilities of data stewards. In response to these identified needs, the paper proposes a structured training framework and comprehensive curriculum for data stewardship, a direct response to the gaps identified in the literature. It addresses five key competence categories for open data stewards, aligning them with current trends and essential skills and knowledge in the field. By advocating for a structured approach to data stewardship education, this work sets the foundation for improved data management in universities and serves as a critical step towards professionalizing the role of data stewards in higher education. The emphasis on the role of open data stewards is expected to advance data accessibility and sharing practices, fostering increased transparency, collaboration, and innovation in academic research. This approach contributes to the evolution of universities into open ecosystems, where there is free flow of data for global education and research advancement…(More)”.
Annoyed Redditors tanking Google Search results illustrates perils of AI scrapers
Article by Scharon Harding: “A trend on Reddit that sees Londoners giving false restaurant recommendations in order to keep their favorites clear of tourists and social media influencers highlights the inherent flaws of Google Search’s reliance on Reddit and Google’s AI Overview.
In May, Google launched AI Overviews in the US, an experimental feature that populates the top of Google Search results with a summarized answer based on an AI model built into Google’s web rankings. When Google first debuted AI Overview, it quickly became apparent that the feature needed work with accuracy and its ability to properly summarize information from online sources. AI Overviews are “built to only show information that is backed up by top web results,” Liz Reid, VP and head of Google Search, wrote in a May blog post. But as my colleague Benj Edwards pointed out at the time, that setup could contribute to inaccurate, misleading, or even dangerous results: “The design is based on the false assumption that Google’s page-ranking algorithm favors accurate results and not SEO-gamed garbage.”
As Edwards alluded to, many have complained about Google Search results’ quality declining in recent years, as SEO spam and, more recently, AI slop float to the top of searches. As a result, people often turn to the Reddit hack to make Google results more helpful. By adding “site:reddit.com” to search results, users can hone their search to more easily find answers from real people. Google seems to understand the value of Reddit and signed an AI training deal with the company that’s reportedly worth $60 million per year…(More)”.