Climate Change and AI: Recommendations for Government

Curated on Posted on by Stefaan Verhulst

Press Release: “A new report, developed by the Centre for AI & Climate and Climate Change AI for the Global Partnership on AI (GPAI), calls for governments to recognise the potential for artificial intelligence (AI) to accelerate the transition to net zero, and to put in place the support needed to advance AI-for-climate solutions. The report is being presented at COP26 today.

The report, Climate Change and AI: Recommendations for Government, highlights 48 specific recommendations for how governments can both support the application of AI to climate challenges and address the climate-related risks that AI poses.

The report was commissioned by the Global Partnership on AI (GPAI), a partnership between 18 countries and the EU that brings together experts from across countries and sectors to help shape the development of AI.

AI is already being used to support climate action in a wide range of use cases, several of which the report highlights. These include:

  • National Grid ESO, which has used AI to double the accuracy of its forecasts of UK electricity demand. Radically improving forecasts of electricity demand and renewable energy generation will be critical in enabling greater proportions of renewable energy on electricity grids.
  • The UN Satellite Centre (UNOSAT), which has developed the FloodAI system that delivers high-frequency flood reports. FloodAI’s reports, which use a combination of satellite data and machine learning, have improved the response to climate-related disasters in Asia and Africa.
  • Climate TRACE, a global coalition of organizations, which has radically improved the transparency and accuracy of emissions monitoring by leveraging AI algorithms and data from more than 300 satellites and 11,000 sensors.

The authors also detail critical bottlenecks that are impeding faster adoption. To address these, the report calls for governments to:

  • Improve data ecosystems in sectors critical to climate transition, including the development of digital twins in e.g. the energy sector.
  • Increase support for research, innovation, and deployment through targeted funding, infrastructure, and improved market designs.
  • Make climate change a central consideration in AI strategies to shape the responsible development of AI as a whole.
  • Support greater international collaboration and capacity building to facilitate the development and governance of AI-for-climate solutions….(More)”.

Why Are We Failing at AI Ethics?

Curated on Posted on by Stefaan Verhulst

Article by Anja Kaspersen and Wendell Wallach: “…Extremely troubling is the fact that the people who are most vulnerable to negative impacts from such rapid expansions of AI systems are often the least likely to be able to join the conversation about these systems, either because they have no or restricted digital access or their lack of digital literacy makes them ripe for exploitation.

Such vulnerable groups are often theoretically included in discussions, but not empowered to take a meaningful part in making decisions. This engineered inequity, alongside human biases, risks amplifying otherness through neglect, exclusion, misinformation, and disinformation.

Society should be deeply concerned that nowhere near enough substantive progress is being made to develop and scale actionable legal, ethical oversight while simultaneously addressing existing inequalities.

So, why hasn’t more been done? There are three main issues at play: 

First, many of the existing dialogues around the ethics of AI and governance are too narrow and fail to understand the subtleties and life cycles of AI systems and their impacts.

Often, these efforts focus only on the development and deployment stages of the technology life cycle, when many of the problems occur during the earlier stages of conceptualization, research, and design. Or they fail to comprehend when and if AI system operates at a level of maturity required to avoid failure in complex adaptive systems.

Or they focus on some aspects of ethics, while ignoring other aspects that are more fundamental and challenging. This is the problem known as “ethics washing” – creating a superficially reassuring but illusory sense that ethical issues are being adequately addressed, to justify pressing forward with systems that end up deepening current patterns.

Let’s be clear: every choice entails tradeoffs. “Ethics talk” is often about underscoring the various tradeoffs entailed in differing courses of action. Once a course has been selected, comprehensive ethical oversight is also about addressing the considerations not accommodated by the options selected, which is essential to any future verification effort. This vital part of the process is often a stumbling block for those trying to address the ethics of AI.

The second major issue is that to date all the talk about ethics is simply that: talk. 

We’ve yet to see these discussions translate into meaningful change in managing the ways in which AI systems are being embedded into various aspect of our lives….

A third issue at play is that discussions on AI and ethics are still largely confined to the ivory tower.

There is an urgent need for more informed public discourse and serious investment in civic education around the societal impact of the bio-digital revolution. This could help address the first two problems, but most of what the general public currently perceives about AI comes from sci-fi tropes and blockbuster movies.

A few examples of algorithmic bias have penetrated the public discourse. But the most headline-grabbing research on AI and ethics tends to focus on far-horizon existential risks. More effort needs to be invested in communicating to the public that, beyond the hypothetical risks of future AI, there are real and imminent risks posed by why and how we embed AI systems that currently shape everyone’s daily lives….(More)”.

Conceptual and normative approaches to AI governance for a global digital ecosystem supportive of the UN Sustainable Development Goals (SDGs)

Curated on Posted on by Stefaan Verhulst

Paper by Amandeep S. Gill & Stefan Germann: “AI governance is like one of those mythical creatures that everyone speaks of but which no one has seen. Sometimes, it is reduced to a list of shared principles such as transparency, non-discrimination, and sustainability; at other times, it is conflated with specific mechanisms for certification of algorithmic solutions or ways to protect the privacy of personal data. We suggest a conceptual and normative approach to AI governance in the context of a global digital public goods ecosystem to enable progress on the UN Sustainable Development Goals (SDGs). Conceptually, we propose rooting this approach in the human capability concept—what people are able to do and to be, and in a layered governance framework connecting the local to the global. Normatively, we suggest the following six irreducibles: a. human rights first; b. multi-stakeholder smart regulation; c. privacy and protection of personal data; d. a holistic approach to data use captured by the 3Ms—misuse of data, missed use of data and missing data; e. global collaboration (‘digital cooperation’); f. basing governance more in practice, in particular, thinking separately and together about data and algorithms. Throughout the article, we use examples from the health domain particularly in the current context of the Covid-19 pandemic. We conclude by arguing that taking a distributed but coordinated global digital commons approach to the governance of AI is the best guarantee of citizen-centered and societally beneficial use of digital technologies for the SDGs…(More)”.

Understanding Algorithmic Discrimination in Health Economics Through the Lens of Measurement Errors

Curated on Posted on by Stefaan Verhulst

Paper by Anirban Basu, Noah Hammarlund, Sara Khor & Aasthaa Bansal: “There is growing concern that the increasing use of machine learning and artificial intelligence-based systems may exacerbate health disparities through discrimination. We provide a hierarchical definition of discrimination consisting of algorithmic discrimination arising from predictive scores used for allocating resources and human discrimination arising from allocating resources by human decision-makers conditional on these predictive scores. We then offer an overarching statistical framework of algorithmic discrimination through the lens of measurement errors, which is familiar to the health economics audience. Specifically, we show that algorithmic discrimination exists when measurement errors exist in either the outcome or the predictors, and there is endogenous selection for participation in the observed data. The absence of any of these phenomena would eliminate algorithmic discrimination. We show that although equalized odds constraints can be employed as bias-mitigating strategies, such constraints may increase algorithmic discrimination when there is measurement error in the dependent variable….(More)”.

Can data die?

Curated on Posted on by Stefaan Verhulst

Article by Jennifer Ding: “…To me, the crux of the Lenna story is how little power we have over our data and how it is used and abused. This threat seems disproportionately higher for women who are often overrepresented in internet content, but underrepresented in internet company leadership and decision making. Given this reality, engineering and product decisions will continue to consciously (and unconsciously) exclude our needs and concerns.

While social norms are changing towards non-consensual data collection and data exploitation, digital norms seem to be moving in the opposite direction. Advancements in machine learning algorithms and data storage capabilities are only making data misuse easier. Whether the outcome is revenge porn or targeted ads, surveillance or discriminatory AI, if we want a world where our data can retire when it’s outlived its time, or when it’s directly harming our lives, we must create the tools and policies that empower data subjects to have a say in what happens to their data… including allowing their data to die…(More)”

The Age of A.I. And Our Human Future

Curated on Posted on by Stefaan Verhulst

Book by Henry A Kissinger, Eric Schmidt, and Daniel Huttenlocher: “Artificial Intelligence (AI) is transforming human society fundamentally and profoundly. Not since the Enlightenment and the Age of Reason have we changed how we approach knowledge, politics, economics, even warfare. Three of our most accomplished and deep thinkers come together to explore what it means for us all.

An A.I. that learned to play chess discovered moves that no human champion would have conceived of. Driverless cars edge forward at red lights, just like impatient humans, and so far, nobody can explain why it happens. Artificial intelligence is being put to use in sports, medicine, education, and even (frighteningly) how we wage war.

In this book, three of our most accomplished and deep thinkers come together to explore how A.I. could affect our relationship with knowledge, impact our worldviews, and change society and politics as profoundly as the ideas of the Enlightenment…(More)”.

The AI Localism Canvas: A Framework to Assess the Emergence of Governance of AI within Cities

Curated on Posted on by Stefaan Verhulst

Paper by Verhulst, Stefaan, Andrew Young, and Mona Sloane: “AI Localism focuses on governance innovation surrounding the use of AI on a local level….As it stands, however, the decision-making processes involved in the local governance of AI systems are not very systematized or well understood. Scholars and local decision-makers lack an adequate evidence base and analytical framework to help guide their thinking. In order to address this shortcoming, we have developed the below “AI Localism Canvas” which can help identify, categorize and assess the different areas of AI Localism specific to a city or region, in the process aid decision-makers in weighing risk and opportunity. The overall goal of the canvas is to rapidly assess and iterate local governance innovation about AI to ensure citizens’ interests and rights are respected….(More)”.

Beyond pilots: sustainable implementation of AI in public services

Curated on Posted on by Stefaan Verhulst

Report by AI Watch: “Artificial Intelligence (AI) is a peculiar case of General Purpose Technology that differs from other examples in history because it embeds specific uncertainties or ambiguous character that may lead to a number of risks when used to support transformative solutions in the public sector. AI has extremely powerful and, in many cases, disruptive effects on the internal management, decision-making and service provision processes of public administration….

This document first introduces the concept of AI appropriation in government, seen as a sequence of two logically distinct phases, respectively named adoption and implementation of related technologies in public services and processes. Then, it analyses the situation of AI governance in the US and China and contrasts it to an emerging, truly European model, rooted in a systemic vision and with an emphasis on the revitalised role of the member states in the EU integration process, Next, it points out some critical challenges to AI implementation in the EU public sector, including: the generation of a critical mass of public investments, the availability of widely shared and suitable datasets, the improvement of AI literacy and skills in the involved staff, and the threats associated with the legitimacy of decisions taken by AI algorithms alone. Finally, it draws a set of common actions for EU decision-makers willing to undertake the systemic approach to AI governance through a more advanced equilibrium between AI promotion and regulation.

The three main recommendations of this work include a more robust integration of AI with data policies, facing the issue of so-called “explainability of AI” (XAI), and broadening the current perspectives of both Pre-Commercial Procurement (PCP) and Public Procurement of Innovation (PPI) at the service of smart AI purchasing by the EU public administration. These recommendations will represent the baseline for a generic implementation roadmap for enhancing the use and impact of AI in the European public sector….(More)”.

The Uselessness of Useful Knowledge

Curated on Posted on by Stefaan Verhulst

Maggie Chiang for Quanta Magazine: “Is artificial intelligence the new alchemy? That is, are the powerful algorithms that control so much of our lives — from internet searches to social media feeds — the modern equivalent of turning lead into gold? Moreover: Would that be such a bad thing?

According to the prominent AI researcher Ali Rahimi and others, today’s fashionable neural networks and deep learning techniques are based on a collection of tricks, topped with a good dash of optimism, rather than systematic analysis. Modern engineers, the thinking goes, assemble their codes with the same wishful thinking and misunderstanding that the ancient alchemists had when mixing their magic potions.

It’s true that we have little fundamental understanding of the inner workings of self-learning algorithms, or of the limits of their applications. These new forms of AI are very different from traditional computer codes that can be understood line by line. Instead, they operate within a black box, seemingly unknowable to humans and even to the machines themselves.

This discussion within the AI community has consequences for all the sciences. With deep learning impacting so many branches of current research — from drug discovery to the design of smart materials to the analysis of particle collisions — science itself may be at risk of being swallowed by a conceptual black box. It would be hard to have a computer program teach chemistry or physics classes. By deferring so much to machines, are we discarding the scientific method that has proved so successful, and reverting to the dark practices of alchemy?

Not so fast, says Yann LeCun, co-recipient of the 2018 Turing Award for his pioneering work on neural networks. He argues that the current state of AI research is nothing new in the history of science. It is just a necessary adolescent phase that many fields have experienced, characterized by trial and error, confusion, overconfidence and a lack of overall understanding. We have nothing to fear and much to gain from embracing this approach. It’s simply that we’re more familiar with its opposite.

After all, it’s easy to imagine knowledge flowing downstream, from the source of an abstract idea, through the twists and turns of experimentation, to a broad delta of practical applications. This is the famous “usefulness of useless knowledge,” advanced by Abraham Flexner in his seminal 1939 essay (itself a play on the very American concept of “useful knowledge” that emerged during the Enlightenment).

A canonical illustration of this flow is Albert Einstein’s general theory of relativity. It all began with the fundamental idea that the laws of physics should hold for all observers, independent of their movements. He then translated this concept into the mathematical language of curved space-time and applied it to the force of gravity and the evolution of the cosmos. Without Einstein’s theory, the GPS in our smartphones would drift off course by about 7 miles a day…(More)”.

Strengthening international cooperation on AI

Curated on Posted on by Stefaan Verhulst

Report by Cameron F. Kerry, Joshua P. Meltzer, Andrea Renda, Alex Engler, and Rosanna Fanni: “Since 2017, when Canada became the first country to adopt a national AI strategy, at least 60 countries have adopted some form of policy for artificial intelligence (AI). The prospect of an estimated boost of 16 percent, or US$13 trillion, to global output by 2030 has led to an unprecedented race to promote AI uptake across industry, consumer markets, and government services. Global corporate investment in AI has reportedly reached US$60 billion in 2020 and is projected to more than double by 2025.

At the same time, the work on developing global standards for AI has led to significant developments in various international bodies. These encompass both technical aspects of AI (in standards development organizations (SDOs) such as the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC), and the Institute of Electrical and Electronics Engineers (IEEE) among others) and the ethical and policy dimensions of responsible AI. In addition, in 2018 the G-7 agreed to establish the Global Partnership on AI, a multistakeholder initiative working on projects to explore regulatory issues and opportunities for AI development. The Organization for Economic Cooperation and Development (OECD) launched the AI Policy Observatory to support and inform AI policy development. Several other international organizations have become active in developing proposed frameworks for responsible AI development.

In addition, there has been a proliferation of declarations and frameworks from public and private organizations aimed at guiding the development of responsible AI. While many of these focus on general principles, the past two years have seen efforts to put principles into operation through fully-fledged policy frameworks. Canada’s directive on the use of AI in government, Singapore’s Model AI Governance Framework, Japan’s Social Principles of Human-Centric AI, and the U.K. guidance on understanding AI ethics and safety have been frontrunners in this sense; they were followed by the U.S. guidance to federal agencies on regulation of AI and an executive order on how these agencies should use AI. Most recently, the EU proposal for adoption of regulation on AI has marked the first attempt to introduce a comprehensive legislative scheme governing AI.

In exploring how to align these various policymaking efforts, we focus on the most compelling reasons for stepping up international cooperation (the “why”); the issues and policy domains that appear most ready for enhanced collaboration (the “what”); and the instruments and forums that could be leveraged to achieve meaningful results in advancing international AI standards, regulatory cooperation, and joint R&D projects to tackle global challenges (the “how”). At the end of this report, we list the topics that we propose to explore in our forthcoming group discussions….(More)”