Paper by Mark Coeckelbergh: “There is a broad consensus that artificial intelligence should contribute to the common good, but it is not clear what is meant by that. This paper discusses this issue and uses it as a lens for analysing what it calls the “democracy deficit” in current AI governance, which includes a tendency to deny the inherently political character of the issue and to take a technocratic shortcut. It indicates what we may agree on and what is and should be up to (further) deliberation when it comes to AI ethics and AI governance. Inspired by the republican tradition in political theory, it also argues for a more active role of citizens and (end-)users: not only as participants in deliberation but also in ensuring, creatively and communicatively, that AI contributes to the common good…(More)”.
Toward a Polycentric or Distributed Approach to Artificial Intelligence & Science
Article by Stefaan Verhulst: “Even as enthusiasm grows over the potential of artificial intelligence (AI), concerns have arisen in equal measure about a possible domination of the field by Big Tech. Such an outcome would replicate many of the mistakes of preceding decades, when a handful of companies accumulated unprecedented market power and often acted as de facto regulators in the global digital ecosystem. In response, the European Group of Chief Scientific Advisors has recently proposed establishing a “state-of-the-art facility for academic research,” to be called the European Distributed Institute for AI in Science (EDIRAS). According to the Group, the facility would be modeled on Geneva’s high-energy physics lab, CERN, with the goal of creating a “CERN for AI” to counterbalance the growing AI prowess of the US and China.
While the comparison to CERN is flawed in some respects–see below–the overall emphasis on a distributed, decentralized approach to AI is highly commendable. In what follows, we outline three key areas where such an approach can help advance the field. These areas–access to computational resources, access to high quality data, and access to purposeful modeling–represent three current pain points (“friction”) in the AI ecosystem. Addressing them through a distributed approach can not only help address the immediate challenges, but more generally advance the cause of open science and ensure that AI and data serve the broader public interest…(More)”.
AI-enabled Peacekeeping Tech for the Digital Age
Springwise: “There are countless organisations and government agencies working to resolve conflicts around the globe, but they often lack the tools to know if they are making the right decisions. Project Didi is developing those technological tools – helping peacemakers plan appropriately and understand the impact of their actions in real time.
Project Didi Co-founder and CCO Gabe Freund explained to Springwise that the project uses machine learning, big data, and AI to analyse conflicts and “establish a new standard for best practice when it comes to decision-making in the world of peacebuilding.”
In essence, the company is attempting to analyse the many factors that are involved in conflict in order to identify a ‘ripe moment’ when both parties will be willing to negotiate for peace. The tools can track the impact and effect of all actors across a conflict. This allows them to identify and create connections between organisations and people who are doing similar work, amplifying their effects…(More)” See also: Project Didi (Kluz Prize)
Defining AI incidents and related terms
OECD Report: “As AI use grows, so do its benefits and risks. These risks can lead to actual harms (“AI incidents”) or potential dangers (“AI hazards”). Clear definitions are essential for managing and preventing these risks. This report proposes definitions for AI incidents and related terms. These definitions aim to foster international interoperability while providing flexibility for jurisdictions to determine the scope of AI incidents and hazards they wish to address…(More)”.
Building a trauma-informed algorithmic assessment toolkit
Report by Suvradip Maitra, Lyndal Sleep, Suzanna Fay, Paul Henman: “Artificial intelligence (AI) and automated processes provide considerable promise to enhance human wellbeing by fully automating or co-producing services with human service providers. Concurrently, if not well considered, automation also provides ways in which to generate harms at scale and speed. To address this challenge, much discussion to date has focused on principles of ethical AI and accountable algorithms with a groundswell of early work seeking to translate these into practical frameworks and processes to ensure such principles are enacted. AI risk assessment frameworks to detect and evaluate possible harms is one dominant approach, as are a growing body of AI audit frameworks, with concomitant emerging governmental and organisational regulatory settings, and associate professionals.
The research outlined in this report took a different approach. Building on work in social services on trauma-informed practice, researchers identified key principles and a practical framework that framed AI design, development and deployment as a reflective, constructive exercise that resulting in algorithmic supported services to be cognisant and inclusive of the diversity of human experience, and particularly those who have experienced trauma. This study resulted in a practical, co-designed, piloted Trauma Informed Algorithmic Assessment Toolkit.
This Toolkit has been designed to assist organisations in their use of automation in service delivery at any stage of their automation journey: ideation; design; development; piloting; deployment or evaluation. While of particular use for social service organisations working with people who may have experienced past trauma, the tool will be beneficial for any organisation wanting to ensure safe, responsible and ethical use of automation and AI…(More)”.
AI for social good: Improving lives and protecting the planet
McKinsey Report: “…Challenges in scaling AI for social-good initiatives are persistent and tough. Seventy-two percent of the respondents to our expert survey observed that most efforts to deploy AI for social good to date have focused on research and innovation rather than adoption and scaling. Fifty-five percent of grants for AI research and deployment across the SDGs are $250,000 or smaller, which is consistent with a focus on targeted research or smaller-scale deployment, rather than large-scale expansion. Aside from funding, the biggest barriers to scaling AI continue to be data availability, accessibility, and quality; AI talent availability and accessibility; organizational receptiveness; and change management. More on these topics can be found in the full report.
While overcoming these challenges, organizations should also be aware of strategies to address the range of risks, including inaccurate outputs, biases embedded in the underlying training data, the potential for large-scale misinformation, and malicious influence on politics and personal well-being. As we have noted in multiple recent articles, AI tools and techniques can be misused, even if the tools were originally designed for social good. Experts identified the top risks as impaired fairness, malicious use, and privacy and security concerns, followed by explainability (Exhibit 2). Respondents from not-for-profits expressed relatively more concern about misinformation, talent issues such as job displacement, and effects of AI on economic stability compared with their counterparts at for-profits, who were more often concerned with IP infringement…(More)”
Brave New Words: How AI Will Revolutionize Education (and Why That’s a Good Thing)
Book by Salman Khan: “…explores how artificial intelligence and GPT technology will transform learning, and offers a road map for teachers, parents, and students to navigate this exciting (and sometimes intimidating) new world.
A pioneer in the field of education technology, Khan examines the ins and outs of these cutting-edge tools and how they will revolutionize the way we learn and teach. For parents concerned about their children’s success, Khan illustrates how AI can personalize learning by adapting to each student’s individual pace and style, identifying strengths and areas for improvement, and offering tailored support and feedback to complement traditional classroom instruction. Khan emphasizes that embracing AI in education is not about replacing human interaction but enhancing it with customized and accessible learning tools that encourage creative problem-solving skills and prepare students for an increasingly digital world.
But Brave New Words is not just about technology—it’s about what this technology means for our society, and the practical implications for administrators, guidance counselors, and hiring managers who can harness the power of AI in education and the workplace. Khan also delves into the ethical and social implications of AI and large language models, offering thoughtful insights into how we can use these tools to build a more accessible education system for students around the world…(More)”.
US Senate AI Working Group Releases Policy Roadmap
Article by Gabby Miller: “On Wednesday, May 15, 2024, a bipartisan US Senate working group led by Majority Leader Sen. Chuck Schumer (D-NY), Sen. Mike Rounds (R-SD), Sen. Martin Heinrich (D-NM), and Sen. Todd Young (R-IN) released a report titled “Driving U.S. Innovation in Artificial Intelligence: A Roadmap for Artificial Intelligence Policy in the United States Senate.” The 31-page report follows a series of off-the-record “educational briefings,” including “the first ever all-senators classified briefing focused solely on AI,” and nine “AI Insight Forums” hosted in the fall of 2023 that drew on the participation of more than 150 experts from industry, academia, and civil society.
The report makes a number of recommendations on funding priorities, the development of new legislation, and areas that require further exploration. It also encourages the executive branch to share information “in a timely fashion and on an ongoing basis” about its AI priorities and “any AI-related Memorandums of Understanding with other countries and the results from any AI-related studies in order to better inform the legislative process.”…(More)”.
Artificial Intelligence and the Skill Premium
Paper by David E. Bloom et al: “How will the emergence of ChatGPT and other forms of artificial intelligence (AI) affect the skill premium? To address this question, we propose a nested constant elasticity of substitution production function that distinguishes among three types of capital: traditional physical capital (machines, assembly lines), industrial robots, and AI. Following the literature, we assume that industrial robots predominantly substitute for low-skill workers, whereas AI mainly helps to perform the tasks of high-skill workers. We show that AI reduces the skill premium as long as it is more substitutable for high-skill workers than low-skill workers are for high-skill workers…(More)”
Artificial intelligence and complex sustainability policy problems: translating promise into practice
Paper by Ruby O’Connor et al: “Addressing sustainability policy challenges requires tools that can navigate complexity for better policy processes and outcomes. Attention on Artificial Intelligence (AI) tools and expectations for their use by governments have dramatically increased over the past decade. We conducted a narrative review of academic and grey literature to investigate how AI tools are being used and adapted for policy and public sector decision-making. We found that academics, governments, and consultants expressed positive expectations about AI, arguing that AI could or should be used to address a wide range of policy challenges. However, there is much less evidence of how public decision makers are actually using AI tools or detailed insight into the outcomes of use. From our findings we draw four lessons for translating the promise of AI into practice: 1) Document and evaluate AI’s application to sustainability policy problems in the real-world; 2) Focus on existing and mature AI technologies, not speculative promises or external pressures; 3) Start with the problem to be solved, not the technology to be applied; and 4) Anticipate and adapt to the complexity of sustainability policy problems…(More)”.