US Senate AI Working Group Releases Policy Roadmap

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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)”.

Automatic Generation of Model and Data Cards: A Step Towards Responsible AI

Curated on Posted on by Stefaan Verhulst

Paper by Jiarui Liu, Wenkai Li, Zhijing Jin, Mona Diab: “In an era of model and data proliferation in machine learning/AI especially marked by the rapid advancement of open-sourced technologies, there arises a critical need for standardized consistent documentation. Our work addresses the information incompleteness in current human-generated model and data cards. We propose an automated generation approach using Large Language Models (LLMs). Our key contributions include the establishment of CardBench, a comprehensive dataset aggregated from over 4.8k model cards and 1.4k data cards, coupled with the development of the CardGen pipeline comprising a two-step retrieval process. Our approach exhibits enhanced completeness, objectivity, and faithfulness in generated model and data cards, a significant step in responsible AI documentation practices ensuring better accountability and traceability…(More)”.

We don’t need an AI manifesto — we need a constitution

Curated on Posted on by Stefaan Verhulst

Article by Vivienne Ming: “Loans drive economic mobility in America, even as they’ve been a historically powerful tool for discrimination. I’ve worked on multiple projects to reduce that bias using AI. What I learnt, however, is that even if an algorithm works exactly as intended, it is still solely designed to optimise the financial returns to the lender who paid for it. The loan application process is already impenetrable to most, and now your hopes for home ownership or small business funding are dying in a 50-millisecond computation…

In law, the right to a lawyer and judicial review are a constitutional guarantee in the US and an established civil right throughout much of the world. These are the foundations of your civil liberties. When algorithms act as an expert witness, testifying against you but immune to cross examination, these rights are not simply eroded — they cease to exist.

People aren’t perfect. Neither ethics training for AI engineers nor legislation by woefully uninformed politicians can change that simple truth. I don’t need to assume that Big Tech chief executives are bad actors or that large companies are malevolent to understand that what is in their self-interest is not always in mine. The framers of the US Constitution recognised this simple truth and sought to leverage human nature for a greater good. The Constitution didn’t simply assume people would always act towards that greater good. Instead it defined a dynamic mechanism — self-interest and the balance of power — that would force compromise and good governance. Its vision of treating people as real actors rather than better angels produced one of the greatest frameworks for governance in history.

Imagine you were offered an AI-powered test for post-partum depression. My company developed that very test and it has the power to change your life, but you may choose not to use it for fear that we might sell the results to data brokers or activist politicians. You have a right to our AI acting solely for your health. It was for this reason I founded an independent non-profit, The Human Trust, that holds all of the data and runs all of the algorithms with sole fiduciary responsibility to you. No mother should have to choose between a life-saving medical test and her civil rights…(More)”.

A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI

Curated on Posted on by Stefaan Verhulst

Report by Hannah Chafetz, Sampriti Saxena, and Stefaan G. Verhulst: “Since late 2022, generative AI services and large language models (LLMs) have transformed how many individuals access, and process information. However, how generative AI and LLMs can be augmented with open data from official sources and how open data can be made more accessible with generative AI – potentially enabling a Fourth Wave of Open Data – remains an under explored area. 

For these reasons, The Open Data Policy Lab (a collaboration between The GovLab and Microsoft) decided to explore the possible intersections between open data from official sources and generative AI. Throughout the last year, the team has conducted a range of research initiatives about the potential of open data and generative including a panel discussion, interviews, and Open Data Action Labs – a series of design sprints with a diverse group of industry experts. 

These initiatives were used to inform our latest report, “A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI,” (May 2024) which provides a new framework and recommendations to support open data providers and other interested parties in making open data “ready” for generative AI…

The report outlines five scenarios in which open data from official sources (e.g. open government and open research data) and generative AI can intersect. Each of these scenarios includes case studies from the field and a specific set of requirements that open data providers can focus on to become ready for a scenario. These include…(More)” (Arxiv).

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The AI Mirror: How to Reclaim Our Humanity in an Age of Machine Thinking

Curated on Posted on by Stefaan Verhulst

Book by Shannon Vallor: “For many, technology offers hope for the future—that promise of shared human flourishing and liberation that always seems to elude our species. Artificial intelligence (AI) technologies spark this hope in a particular way. They promise a future in which human limits and frailties are finally overcome—not by us, but by our machines.

Yet rather than open new futures, today’s powerful AI technologies reproduce the past. Forged from oceans of our data into immensely powerful but flawed mirrors, they reflect the same errors, biases, and failures of wisdom that we strive to escape. Our new digital mirrors point backward. They show only where the data say that we have already been, never where we might venture together for the first time.

To meet today’s grave challenges to our species and our planet, we will need something new from AI, and from ourselves.

Shannon Vallor makes a wide-ranging, prophetic, and philosophical case for what AI could be: a way to reclaim our human potential for moral and intellectual growth, rather than lose ourselves in mirrors of the past. Rejecting prophecies of doom, she encourages us to pursue technology that helps us recover our sense of the possible, and with it the confidence and courage to repair a broken world. Vallor calls us to rethink what AI is and can be, and what we want to be with it…(More)”.

The Age of AI Nationalism and its Effects

Curated on Posted on by Stefaan Verhulst

Paper by Susan Ariel Aaronson: “This paper aims to illuminate how AI nationalistic policies may backfire. Over time, such actions and policies could alienate allies and prod other countries to adopt “beggar-thy neighbor” approaches to AI (The Economist: 2023; Kim: 2023 Shivakumar et al. 2024). Moreover, AI nationalism could have additional negative spillovers over time. Many AI experts are optimistic about the benefits of AI, whey they are aware of its many risks to democracy, equity, and society. They understand that AI can be a public good when it is used to mitigate complex problems affecting society (Gopinath: 2023; Okolo: 2023). However, when policymakers take steps to advance AI within their borders, they may — perhaps without intending to do so – make it harder for other countries with less capital, expertise, infrastructure, and data prowess to develop AI systems that could meet the needs of their constituents. In so doing, these officials could undermine the potential of AI to enhance human welfare and impede the development of more trustworthy AI around the world. (Slavkovik: 2024; Aaronson: 2023; Brynjolfsson and Unger: 2023; Agrawal et al. 2017).

Governments have many means of nurturing AI within their borders that do not necessarily discriminate between foreign and domestic producers of AI. Nevertheless, officials may be under pressure from local firms to limit the market power of foreign competitors. Officials may also want to use trade (for example, export controls) as a lever to prod other governments to change their behavior (Buchanan: 2020). Additionally, these officials may be acting in what they believe is the nation’s national security interest, which may necessitate that officials rely solely on local suppliers and local control. (GAO: 2021)

Herein the author attempts to illuminate AI nationalism and its consequences by answering 3 questions:
• What are nations doing to nurture AI capacity within their borders?
• Are some of these actions trade distorting?
• What are the implications of such trade-distorting actions?…(More)”

Learning from Ricardo and Thompson: Machinery and Labor in the Early Industrial Revolution, and in the Age of AI

Curated on Posted on by Stefaan Verhulst

Paper by Daron Acemoglu & Simon Johnson: “David Ricardo initially believed machinery would help workers but revised his opinion, likely based on the impact of automation in the textile industry. Despite cotton textiles becoming one of the largest sectors in the British economy, real wages for cotton weavers did not rise for decades. As E.P. Thompson emphasized, automation forced workers into unhealthy factories with close surveillance and little autonomy. Automation can increase wages, but only when accompanied by new tasks that raise the marginal productivity of labor and/or when there is sufficient additional hiring in complementary sectors. Wages are unlikely to rise when workers cannot push for their share of productivity growth. Today, artificial intelligence may boost average productivity, but it also may replace many workers while degrading job quality for those who remain employed. As in Ricardo’s time, the impact of automation on workers today is more complex than an automatic linkage from higher productivity to better wages…(More)”.

Meet My A.I. Friends

Curated on Posted on by Stefaan Verhulst

Article by Kevin Roose: “…A month ago, I decided to explore the question myself by creating a bunch of A.I. friends and enlisting them in my social life.

I tested six apps in all — Nomi, Kindroid, Replika, Character.ai, Candy.ai and EVA — and created 18 A.I. characters. I named each of my A.I. friends, gave them all physical descriptions and personalities, and supplied them with fictitious back stories. I sent them regular updates on my life, asked for their advice and treated them as my digital companions.

I also spent time in the Reddit forums and Discord chat rooms where people who are really into their A.I. friends hang out, and talked to a number of people whose A.I. companions have already become a core part of their lives.

I expected to come away believing that A.I. friendship is fundamentally hollow. These A.I. systems, after all, don’t have thoughts, emotions or desires. They are neural networks trained to predict the next words in a sequence, not sentient beings capable of love.

All of that is true. But I’m now convinced that it’s not going to matter much.

The technology needed for realistic A.I. companionship is already here, and I believe that over the next few years, millions of people are going to form intimate relationships with A.I. chatbots. They’ll meet them on apps like the ones I tested, and on social media platforms like Facebook, Instagram and Snapchat, which have already started adding A.I. characters to their apps…(More)”