Governing the Unknown

Curated on Posted on by Stefaan Verhulst

Article by Kaushik Basu: “Technology is changing the world faster than policymakers can devise new ways to cope with it. As a result, societies are becoming polarized, inequality is rising, and authoritarian regimes and corporations are doctoring reality and undermining democracy.

For ordinary people, there is ample reason to be “a little bit scared,” as OpenAI CEO Sam Altman recently put it. Major advances in artificial intelligence raise concerns about education, work, warfare, and other risks that could destabilize civilization long before climate change does. To his credit, Altman is urging lawmakers to regulate his industry.

In confronting this challenge, we must keep two concerns in mind. The first is the need for speed. If we take too long, we may find ourselves closing the barn door after the horse has bolted. That is what happened with the 1968 Nuclear Non-Proliferation Treaty: It came 23 years too late. If we had managed to establish some minimal rules after World War II, the NPT’s ultimate goal of nuclear disarmament might have been achievable.

The other concern involves deep uncertainty. This is such a new world that even those working on AI do not know where their inventions will ultimately take us. A law enacted with the best intentions can still backfire. When America’s founders drafted the Second Amendment conferring the “right to keep and bear arms,” they could not have known how firearms technology would change in the future, thereby changing the very meaning of the word “arms.” Nor did they foresee how their descendants would fail to realize this even after seeing the change.

But uncertainty does not justify fatalism. Policymakers can still effectively govern the unknown as long as they keep certain broad considerations in mind. For example, one idea that came up during a recent Senate hearing was to create a licensing system whereby only select corporations would be permitted to work on AI.

This approach comes with some obvious risks of its own. Licensing can often be a step toward cronyism, so we would also need new laws to deter politicians from abusing the system. Moreover, slowing your country’s AI development with additional checks does not mean that others will adopt similar measures. In the worst case, you may find yourself facing adversaries wielding precisely the kind of malevolent tools that you eschewed. That is why AI is best regulated multilaterally, even if that is a tall order in today’s world…(More)”.

Model evaluation for extreme risks

Curated on Posted on by Stefaan Verhulst

Paper by Toby Shevlane et al: “Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further progress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through “dangerous capability evaluations”) and the propensity of models to apply their capabilities for harm (through “alignment evaluations”). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.

Figure 1 | The theory of change for model evaluations for extreme risk. Evaluations for dangerous capabilities and alignment inform risk assessments, and are in turn embedded into important governance processes…(More)”.

Imagining AI: How the World Sees Intelligent Machines

Curated on Posted on by Stefaan Verhulst

Book edited by Stephen Cave and Kanta Dihal: “AI is now a global phenomenon. Yet Hollywood narratives dominate perceptions of AI in the English-speaking West and beyond, and much of the technology itself is shaped by a disproportionately white, male, US-based elite. However, different cultures have been imagining intelligent machines since long before we could build them, in visions that vary greatly across religious, philosophical, literary and cinematic traditions. This book aims to spotlight these alternative visions.

Imagining AI draws attention to the range and variety of visions of a future with intelligent machines and their potential significance for the research, regulation, and implementation of AI. The book is structured geographically, with each chapter presenting insights into how a specific region or culture imagines intelligent machines. The contributors, leading experts from academia and the arts, explore how the encounters between local narratives, digital technologies, and mainstream Western narratives create new imaginaries and insights in different contexts across the globe. The narratives they analyse range from ancient philosophy to contemporary science fiction, and visual art to policy discourse.

The book sheds new light on some of the most important themes in AI ethics, from the differences between Chinese and American visions of AI, to digital neo-colonialism. It is an essential work for anyone wishing to understand how different cultural contexts interplay with the most significant technology of our time…(More)”.

Generative Artificial Intelligence and Data Privacy: A Primer

Curated on Posted on by Stefaan Verhulst

Report by Congressional Research Service: “Since the public release of Open AI’s ChatGPT, Google’s Bard, and other similar systems, some Members of Congress have expressed interest in the risks associated with “generative artificial intelligence (AI).” Although exact definitions vary, generative AI is a type of AI that can generate new content—such as text, images, and videos—through learning patterns from pre-existing data.
It is a broad term that may include various technologies and techniques from AI and machine learning (ML). Generative AI models have received significant attention and scrutiny due to their potential harms, such as risks involving privacy, misinformation, copyright, and non-consensual sexual imagery. This report focuses on privacy issues and relevant policy considerations for Congress. Some policymakers and stakeholders have raised privacy concerns about how individual data may be used to develop and deploy generative models. These concerns are not new or unique to generative AI, but the scale, scope, and capacity of such technologies may present new privacy challenges for Congress…(More)”.

A Hiring Law Blazes a Path for A.I. Regulation

Curated on Posted on by Stefaan Verhulst

Article by Steve Lohr: “European lawmakers are finishing work on an A.I. act. The Biden administration and leaders in Congress have their plans for reining in artificial intelligence. Sam Altman, the chief executive of OpenAI, maker of the A.I. sensation ChatGPT, recommended the creation of a federal agency with oversight and licensing authority in Senate testimony last week. And the topic came up at the Group of 7 summit in Japan.

Amid the sweeping plans and pledges, New York City has emerged as a modest pioneer in A.I. regulation.

The city government passed a law in 2021 and adopted specific rules last month for one high-stakes application of the technology: hiring and promotion decisions. Enforcement begins in July.

The city’s law requires companies using A.I. software in hiring to notify candidates that an automated system is being used. It also requires companies to have independent auditors check the technology annually for bias. Candidates can request and be told what data is being collected and analyzed. Companies will be fined for violations.

New York City’s focused approach represents an important front in A.I. regulation. At some point, the broad-stroke principles developed by governments and international organizations, experts say, must be translated into details and definitions. Who is being affected by the technology? What are the benefits and harms? Who can intervene, and how?

“Without a concrete use case, you are not in a position to answer those questions,” said Julia Stoyanovich, an associate professor at New York University and director of its Center for Responsible A.I.

But even before it takes effect, the New York City law has been a magnet for criticism. Public interest advocates say it doesn’t go far enough, while business groups say it is impractical.

The complaints from both camps point to the challenge of regulating A.I., which is advancing at a torrid pace with unknown consequences, stirring enthusiasm and anxiety.

Uneasy compromises are inevitable.

Ms. Stoyanovich is concerned that the city law has loopholes that may weaken it. “But it’s much better than not having a law,” she said. “And until you try to regulate, you won’t learn how.”…(More)” – See also AI Localism: Governing AI at the Local Level

Boston Isn’t Afraid of Generative AI

Curated on Posted on by Stefaan Verhulst

Article by Beth Simone Noveck: “After ChatGPT burst on the scene last November, some government officials raced to prohibit its use. Italy banned the chatbot. New York City, Los Angeles Unified, Seattle, and Baltimore School Districts either banned or blocked access to generative AI tools, fearing that ChatGPT, Bard, and other content generation sites could tempt students to cheat on assignments, induce rampant plagiarism, and impede critical thinking. This week, US Congress heard testimony from Sam Altman, CEO of OpenAI, and AI researcher Gary Marcus as it weighed whether and how to regulate the technology.

In a rapid about-face, however, a few governments are now embracing a less fearful and more hands-on approach to AI. New York City Schools chancellor David Banks announced yesterday that NYC is reversing its ban because “the knee jerk fear and risk overlooked the potential of generative AI to support students and teachers, as well as the reality that our students are participating in and will work in a world where understanding generative AI is crucial.” And yesterday, City of Boston chief information officer Santiago Garces sent guidelines to every city official encouraging them to start using generative AI “to understand their potential.” The city also turned on use of Google Bard as part of the City of Boston’s enterprise-wide use of Google Workspace so that all public servants have access.

The “responsible experimentation approach” adopted in Boston—the first policy of its kind in the US—could, if used as a blueprint, revolutionize the public sector’s use of AI across the country and cause a sea change in how governments at every level approach AI. By promoting greater exploration of how AI can be used to improve government effectiveness and efficiency, and by focusing on how to use AI for governance instead of only how to govern AI, the Boston approach might help to reduce alarmism and focus attention on how to use AI for social good…(More)”.

How to design an AI ethics board

Curated on Posted on by Stefaan Verhulst


Paper by Jonas Schuett, Anka Reuel, Alexis Carlier: “Organizations that develop and deploy artificial intelligence (AI) systems need to take measures to reduce the associated risks. In this paper, we examine how AI companies could design an AI ethics board in a way that reduces risks from AI. We identify five high-level design choices: (1) What responsibilities should the board have? (2) What should its legal structure be? (3) Who should sit on the board? (4) How should it make decisions and should its decisions be binding? (5) What resources does it need? We break down each of these questions into more specific sub-questions, list options, and discuss how different design choices affect the board’s ability to reduce risks from AI. Several failures have shown that designing an AI ethics board can be challenging. This paper provides a toolbox that can help AI companies to overcome these challenges…(More)”.

For chemists, the AI revolution has yet to happen

Curated on Posted on by Stefaan Verhulst

Editorial Team at Nature: “Many people are expressing fears that artificial intelligence (AI) has gone too far — or risks doing so. Take Geoffrey Hinton, a prominent figure in AI, who recently resigned from his position at Google, citing the desire to speak out about the technology’s potential risks to society and human well-being.

But against those big-picture concerns, in many areas of science you will hear a different frustration being expressed more quietly: that AI has not yet gone far enough. One of those areas is chemistry, for which machine-learning tools promise a revolution in the way researchers seek and synthesize useful new substances. But a wholesale revolution has yet to happen — because of the lack of data available to feed hungry AI systems.

Any AI system is only as good as the data it is trained on. These systems rely on what are called neural networks, which their developers teach using training data sets that must be large, reliable and free of bias. If chemists want to harness the full potential of generative-AI tools, they need to help to establish such training data sets. More data are needed — both experimental and simulated — including historical data and otherwise obscure knowledge, such as that from unsuccessful experiments. And researchers must ensure that the resulting information is accessible. This task is still very much a work in progress…(More)”.

China’s new AI rules protect people — and the Communist Party’s power

Curated on Posted on by Stefaan Verhulst

Article by Johanna M. Costigan: “In April, in an effort to regulate rapidly advancing artificial intelligence technologies, China’s internet watchdog introduced draft rules on generative AI. They cover a wide range of issues — from how data is trained to how users interact with generative AI such as chatbots. 

Under the new regulations, companies are ultimately responsible for the “legality” of the data they use to train AI models. Additionally, generative AI providers must not share personal data without permission, and must guarantee the “veracity, accuracy, objectivity, and diversity” of their pre-training data. 

These strict requirements by the Cyberspace Administration of China (CAC) for AI service providers could benefit Chinese users, granting them greater protections from private companies than many of their global peers. Article 11 of the regulations, for instance, prohibits providers from “conducting profiling” on the basis of information gained from users. Any Instagram user who has received targeted ads after their smartphone tracked their activity would stand to benefit from this additional level of privacy.  

Another example is Article 10 — it requires providers to employ “appropriate measures to prevent users from excessive reliance on generated content,” which could help prevent addiction to new technologies and increase user safety in the long run. As companion chatbots such as Replika become more popular, companies should be responsible for managing software to ensure safe use. While some view social chatbots as a cure for loneliness, depression, and social anxiety, they also present real risks to users who become reliant on them…(More)”.

AI-assisted diplomatic decision-making during crises—Challenges and opportunities

Curated on Posted on by Stefaan Verhulst

Article by Neeti Pokhriyal and Till Koebe: “Recent academic works have demonstrated the efficacy of employing or integrating “non-traditional” data (e.g., social media, satellite imagery, etc) for situational awareness tasks…

Despite these successes, we identify four critical challenges unique to the area of diplomacy that needs to be considered within the growing AI and diplomacy community going ahead:

1. First, decisions during crises are almost always taken using limited or incomplete information. There may be deliberate misuse and obfuscation of data/signals between different parties involved. At the start of a crisis, information is usually limited and potentially biased, especially along socioeconomic and rural-urban lines as crises are known to exacerbate the vulnerabilities already existing in the populations. This requires AI tools to quantify and visualize calibrated uncertainty in their outputs in an appropriate manner.

2. Second, in many cases, human lives and livelihoods are at stake. Therefore, any forecast, reasoning, or recommendation provided by AI assistance needs to be explainable and transparent for authorized users, but also secure against unauthorized access as diplomatic information is often highly sensitive. The question of accountability in case of misleading AI assistance needs to be addressed beforehand.

3. Third, in complex situations with high stakes but limited information, cultural differences and value-laden judgment driven by personal experiences play a central role in diplomatic decision-making. This calls for the use of learning techniques that can incorporate domain knowledge and experience.

4. Fourth, diplomatic interests during crises are often multifaceted, resulting in deep mistrust in and strategic misuse of information. Social media data, when used for consular tasks, has been shown to be susceptible to various d-/misinformation campaigns, some by the public, others by state actors for strategic manipulation…(More)”