Inside the New Nonprofit AI Initiatives Seeking to Aid Teachers and Farmers in Rural Africa

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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)”.

Conversational Swarms of Humans and AI Agents enable Hybrid Collaborative Decision-making

Curated on Posted on by Stefaan Verhulst

Paper by Louis Rosenberg et al: “Conversational Swarm Intelligence (CSI) is an AI-powered communication and collaboration technology that allows large, networked groups (of potentially unlimited size) to hold thoughtful conversational deliberations in real-time. Inspired by the efficient decision-making dynamics of fish schools, CSI divides a human population into a set of small subgroups connected by AI agents. This enables the full group to hold a unified conversation. In this study, groups of 25 participants were tasked with selecting a roster of players in a real Fantasy Baseball contest. A total of 10 trials were run using CSI. In half the trials, each subgroup was augmented with a fact-providing AI agent referred to herein as an Infobot. The Infobot was loaded with a wide range of MLB statistics. The human participants could query the Infobot the same way they would query other persons in their subgroup. Results show that when using CSI, the 25-person groups outperformed 72% of individually surveyed participants and showed significant intelligence amplification versus the mean score (p=0.016). The CSI-enabled groups also significantly outperformed the most popular picks across the collected surveys for each daily contest (p<0.001). The CSI sessions that used Infobots scored slightly higher than those that did not, but it was not statistically significant in this study. That said, 85% of participants agreed with the statement ‘Our decisions were stronger because of information provided by the Infobot’ and only 4% disagreed. In addition, deliberations that used Infobots showed significantly less variance (p=0.039) in conversational content across members. This suggests that Infobots promoted more balanced discussions in which fewer members dominated the dialog. This may be because the infobot enabled participants to confidently express opinions with the support of factual data…(More)”.

Effective Data Stewardship in Higher Education: Skills, Competences, and the Emerging Role of Open Data Stewards

Curated on Posted on by Stefaan Verhulst

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)”.

Enabling Digital Innovation in Government

Curated on Posted on by Stefaan Verhulst

OECD Report: “…presents the OECD’s definition of GovTech (Chapter 2) and sets out the GovTech Policy Framework (Chapter 3). The framework is designed to guide governments on how to establish the conditions for successful, sustainable, and effective GovTech.

The framework consists of two parts: the GovTech Building Blocks and the GovTech Enablers. The building blocks (Chapter 3) represent the foundations at the micro-level needed to establish impactful GovTech practices within public sectors by introducing more agile practices, mitigating risks, and building meaningful collaboration with the GovTech ecosystem. These building blocks include:

  • Mature digital government infrastructure: including the necessary technology, infrastructure, tools, and data governance to enable both GovTech collaborations and the digital solutions they develop.
  • Capacities for collaboration and experimentation: within the public sector, including the digital skills and multidisciplinary teams; agile processes, tools, and methodologies; and a culture that encourages experimentation and accepts failure. 
  • Resources and implementation support: considering how to make funding available, how to evolve procurement approaches, and how to scale successful pilots across organisations and internationally.
  • Availability and maturity of GovTech partners: including acceleration programmes to support start-ups growth by facilitating access to capital, the scaling up of solutions, and minimising barriers to access procurement opportunities.

At the macro-level, the enablers (Chapter 4) instead create an environment that fosters the development of GovTech and facilitates good practices. This is done at the:

  • Strategic layer: where governments could use GovTech strategies and champions in senior leadership positions to mobilise support and set a clear direction for GovTech.
  • Institutional layer: where governments could seek collaboration and knowledge-sharing across institutions at the national, regional, or policy levels.
  • Network layer: where both governments and GovTech actors should seek to mobilise the network collectively to strengthen the GovTech practice and garner broader support from communities…(More)”

Annoyed Redditors tanking Google Search results illustrates perils of AI scrapers

Curated on Posted on by Stefaan Verhulst

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)”.

Rediscovering the Pleasures of Pluralism: The Potential of Digitally Mediated Civic Participation

Curated on Posted on by Stefaan Verhulst

Essay by Lily L. Tsai and Alex Pentland: “Human society developed when most collective decision-making was limited to small, geographically concentrated groups such as tribes or extended family groups. Discussions about community issues could take place among small numbers of people with similar concerns. As coordination across larger distances evolved, the costs of travel required representatives from each clan or smaller group to participate in deliberations and decision-making involving multiple local communities. Divergence in the interests of representatives and their constituents opened up opportunities for corruption and elite capture.

Technologies now enable very large numbers of people to communicate, coordinate, and make collective decisions on the same platform. We have new opportunities for digitally enabled civic participation and direct democracy that scale for both the smallest and largest groups of people. Quantitative experiments, sometimes including tens of millions of individuals, have examined inclusiveness and efficiency in decision-making via digital networks. Their findings suggest that large networks of nonexperts can make practical, productive decisions and engage in collective action under certain (1) conditions. (2) These conditions include shared knowledge among individuals and communities with similar concerns, and information about their recent actions and outcomes…(More)”

Exploring the Intersections of Open Data and Generative AI: Recent Additions to the Observatory

Curated on Posted on by Stefaan Verhulst

Blog by Roshni Singh, Hannah Chafetz, Andrew Zahuranec, Stefaan Verhulst: “The Open Data Policy Lab’s Observatory of Examples of How Open Data and Generative AI Intersect provides real-world use cases of where open data from official sources intersects with generative artificial intelligence (AI), building from the learnings from our report, “A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI.” 

The Observatory includes over 80 examples from several domains and geographies–ranging from supporting administrative work within the legal department of the Government of France to assisting researchers across the African continent in navigating cross-border data sharing laws. The examples include generative AI chatbots to improve access to services, conversational tools to help analyze data, datasets to improve the quality of the AI output, and more. A key feature of the Observatory is its categorization across our Spectrum of Scenarios framework, shown below. Through this effort, we aim to bring together the work already being done and identify ways to use generative AI for the public good.

Screenshot 2024 10 25 at 10.50.23 am

This Observatory is an attempt to grapple with the work currently being done to apply generative AI in conjunction with official open data. It does not make a value judgment on their efficacy or practices. Many of these examples have ethical implications, which merit further attention and study. 

From September through October, we added to the Observatory:

  • Bayaan Platform: A conversational tool by the Statistics Centre Abu Dhabi that provides decision makers with data analytics and visualization support.
  • Berufsinfomat: A generative AI tool for career coaching in Austria.
  • ChatTCU: A chatbot for Brazil’s Federal Court of Accounts.
  • City of Helsinki’s AI Register: An initiative aimed at leveraging open city data to enhance civic services and facilitate better engagement with residents.
  • Climate Q&A: A generative AI chatbot that provides information about climate change based on scientific reports.
  • DataLaw.Bot: A generative AI tool that disseminates data sharing regulations with researchers across several African countries…(More)”.

South Korea leverages open government data for AI development

Curated on Posted on by Stefaan Verhulst

Article by Si Ying Thian: “In South Korea, open government data is powering artificial intelligence (AI) innovations in the private sector.

Take the case of TTCare which may be the world’s first mobile application to analyse eye and skin disease symptoms in pets.

AI Hub allows users to search by industry, data format and year (top row), with the data sets made available based on the particular search term “pet” (bottom half of the page). Image: AI Hub, provided by courtesy of Baek

The AI model was trained on about one million pieces of data – half of the data coming from the government-led AI Hub and the rest collected by the firm itself, according to the Korean newspaper Donga.

AI Hub is an integrated platform set up by the government to support the country’s AI infrastructure.

TTCare’s CEO Heo underlined the importance of government-led AI training data in improving the model’s ability to diagnose symptoms. The firm’s training data is currently accessible through AI Hub, and any Korean citizen can download or use it.

Pushing the boundaries of open data

Over the years, South Korea has consistently come up top in the world’s rankings for Open, Useful, and Re-usable data (OURdata) Index.

The government has been pushing the boundaries of what it can do with open data – beyond just making data usable by providing APIs. Application Programming Interfaces, or APIs, make it easier for users to tap on open government data to power their apps and services.

There is now rising interest from public sector agencies to tap on such data to train AI models, said South Korea’s National Information Society Agency (NIA)’s Principal Manager, Dongyub Baek, although this is still at an early stage.

Baek sits in NIA’s open data department, which handles policies, infrastructure such as the National Open Data Portal, as well as impact assessments of the government initiatives…(More)”