The new machinery of government: using machine technology in administrative decision-making

Curated on Posted on by Stefaan Verhulst

Report by New South Wales Ombudsman: “There are many situations in which government agencies could use appropriately-designed machine technologies to assist in the exercise of their functions, which would be compatible with lawful and appropriate conduct. Indeed, in some instances machine technology may improve aspects of good administrative conduct – such as accuracy and consistency in decision-making, as well as mitigating the risk of individual human bias.

However, if machine technology is designed and used in a way that does not accord with administrative law and associated principles of good administrative practice, then its use could constitute or involve maladministration. It could also result in legal challenges, including a risk that administrative decisions or actions may later be held by a court to have been unlawful or invalid.

The New South Wales Ombudsman was prompted to prepare this report after becoming aware of one agency (Revenue NSW) using machine technology for the performance of a discretionary statutory function (the garnisheeing of unpaid fine debts from individuals’ bank accounts), in a way that was having a significant impact on individuals, many of whom were already in situations of financial vulnerability.

The Ombudsman’s experience with Revenue NSW, and a scan of the government’s published policies on the use of artificial intelligence and other digital technologies, suggests that there may be inadequate attention being given to fundamental aspects of public law that are relevant to machine technology adoption….(More)”

Empowering AI Leadership: AI C-Suite Toolkit

Curated on Posted on by Stefaan Verhulst

Toolkit by the World Economic Forum: “Artificial intelligence (AI) is one of the most important technologies for business, the economy and society and a driving force behind the Fourth Industrial Revolution. C-suite executives need to understand its possibilities and risks. This requires a multifaceted approach and holistic grasp of AI, spanning technical, organizational, regulatory, societal and also philosophical aspects. This toolkit provides a one-stop place for corporate executives to identify and understand the multiple and complex issues that AI raises for their business and society. It provides a practical set of tools to help them comprehend AI’s impact on their roles, ask the right questions, identify the key trade-offs and make informed decisions on AI strategy, projects and implementations…(More)”.

The AI Carbon Footprint and Responsibilities of AI Scientists

Curated on Posted on by Stefaan Verhulst

Paper by Guglielmo Tamburrini: “This article examines ethical implications of the growing AI carbon footprint, focusing on the fair distribution of prospective responsibilities among groups of involved actors. First, major groups of involved actors are identified, including AI scientists, AI industry, and AI infrastructure providers, from datacenters to electrical energy suppliers. Second, responsibilities of AI scientists concerning climate warming mitigation actions are disentangled from responsibilities of other involved actors. Third, to implement these responsibilities nudging interventions are suggested, leveraging on AI competitive games which would prize research combining better system accuracy with greater computational and energy efficiency. Finally, in addition to the AI carbon footprint, it is argued that another ethical issue with a genuinely global dimension is now emerging in the AI ethics agenda. This issue concerns the threats that AI-powered cyberweapons pose to the digital command, control, and communication infrastructure of nuclear weapons systems…(More)”.

Are we witnessing the dawn of post-theory science?

Curated on Posted on by Stefaan Verhulst

Essay by Laura Spinney: “Does the advent of machine learning mean the classic methodology of hypothesise, predict and test has had its day?..

Isaac Newton apocryphally discovered his second law – the one about gravity – after an apple fell on his head. Much experimentation and data analysis later, he realised there was a fundamental relationship between force, mass and acceleration. He formulated a theory to describe that relationship – one that could be expressed as an equation, F=ma – and used it to predict the behaviour of objects other than apples. His predictions turned out to be right (if not always precise enough for those who came later).

Contrast how science is increasingly done today. Facebook’s machine learning tools predict your preferences better than any psychologist. AlphaFold, a program built by DeepMind, has produced the most accurate predictions yet of protein structures based on the amino acids they contain. Both are completely silent on why they work: why you prefer this or that information; why this sequence generates that structure.

You can’t lift a curtain and peer into the mechanism. They offer up no explanation, no set of rules for converting this into that – no theory, in a word. They just work and do so well. We witness the social effects of Facebook’s predictions daily. AlphaFold has yet to make its impact felt, but many are convinced it will change medicine.

Somewhere between Newton and Mark Zuckerberg, theory took a back seat. In 2008, Chris Anderson, the then editor-in-chief of Wired magazine, predicted its demise. So much data had accumulated, he argued, and computers were already so much better than us at finding relationships within it, that our theories were being exposed for what they were – oversimplifications of reality. Soon, the old scientific method – hypothesise, predict, test – would be relegated to the dustbin of history. We’d stop looking for the causes of things and be satisfied with correlations.

With the benefit of hindsight, we can say that what Anderson saw is true (he wasn’t alone). The complexity that this wealth of data has revealed to us cannot be captured by theory as traditionally understood. “We have leapfrogged over our ability to even write the theories that are going to be useful for description,” says computational neuroscientist Peter Dayan, director of the Max Planck Institute for Biological Cybernetics in Tübingen, Germany. “We don’t even know what they would look like.”

But Anderson’s prediction of the end of theory looks to have been premature – or maybe his thesis was itself an oversimplification. There are several reasons why theory refuses to die, despite the successes of such theory-free prediction engines as Facebook and AlphaFold. All are illuminating, because they force us to ask: what’s the best way to acquire knowledge and where does science go from here?…(More)”

Our Common AI Future – A Geopolitical Analysis and Road Map, for AI Driven Sustainable Development, Science and Data Diplomacy

Curated on Posted on by Stefaan Verhulst

(Open Access) Book by Francesco Lapenta: “The premise of this concise but thorough book is that the future, while uncertain and open, is not arbitrary, but the result of a complex series of competing decisions, actors, and events that began in the past, have reached a certain configuration in the present, and will continue to develop into the future. These past and present conditions constitute the basis and origin of future developments that have the potential to shape into a variety of different possible, probable, undesirable or desirable future scenarios. The realisation that these future scenarios cannot be totally arbitrary gives scope to the study of the past, indispensable to fully understand the facts and actors and forces that contributed to the formation of the present, and how certain systems, or dominant models, came to be established (I). The relative openness of future scenarios gives scope to the study of what competing forces and models might exist, their early formation, actors, and initiatives (II) and how they may act as catalysts for alternative theories, models (III and IV) and actions that can influence our future and change its path (V)…

The analyses in the book, which are loosely divided into three phases, move from the past to the present, and begin with identifying best practices and some of the key initiatives that have attempted to achieve these global collaborative goals over the last few decades. Then, moving forward, they describe a roadmap to a possible future based on already existing and developing theories, initiatives, and tools that could underpin these global collaborative efforts in the specific areas of AI and Sustainable Development. In the Road Map for AI Driven Sustainable Development, the analyses identify and stand on the shoulders of a number of past and current global initiatives that have worked for decades to lay the groundwork for this alternative evolutionary and collaborative model. The title of this book directs, acknowledges, and encourages readers to engage with one of these pivotal efforts, the “Our Common Future” report, the Brundtland’s commission report which was published in 1987 by the World Commission on Environment and Development (WCED). Building on the report’s ambitious humanistic and socioeconomic landscape and ambitions, the analyses investigate a variety of existing and developing best practices that could lead to, or inspire, a shared scientific collaborative model for AI development. Based on the understanding that, despite political rivalry and competition, governments should collaborate on at least two fundamental issues: One, to establish a set of global “Red Lines” to prohibit the development and use of AIs in specific applications that might pose an ethical or existential threat to humanity and the planet. And two, create a set of “Green Zones” for scientific diplomacy and cooperation in order to capitalize on the opportunities that the impending AIs era may represent in confronting major collective challenges such as the health and climate crises, the energy crisis, and the sustainable development goals identified in the report and developed by other subsequent global initiatives…(More)”.

If AI Is Predicting Your Future, Are You Still Free?

Curated on Posted on by Stefaan Verhulst

Essay by Carissa Veliz” “…Today, prediction is mostly done through machine learning algorithms that use statistics to fill in the blanks of the unknown. Text algorithms use enormous language databases to predict the most plausible ending to a string of words. Game algorithms use data from past games to predict the best possible next move. And algorithms that are applied to human behavior use historical data to infer our future: what we are going to buy, whether we are planning to change jobs, whether we are going to get sick, whether we are going to commit a crime or crash our car. Under such a model, insurance is no longer about pooling risk from large sets of people. Rather, predictions have become individualized, and you are increasingly paying your own way, according to your personal risk scores—which raises a new set of ethical concerns.

An important characteristic of predictions is that they do not describe reality. Forecasting is about the future, not the present, and the future is something that has yet to become real. A prediction is a guess, and all sorts of subjective assessments and biases regarding risk and values are built into it. There can be forecasts that are more or less accurate, to be sure, but the relationship between probability and actuality is much more tenuous and ethically problematic than some assume.

Institutions today, however, often try to pass off predictions as if they were a model of objective reality. And even when AI’s forecasts are merely probabilistic, they are often interpreted as deterministic in practice—partly because human beings are bad at understanding probability and partly because the incentives around avoiding risk end up reinforcing the prediction. (For example, if someone is predicted to be 75 percent likely to be a bad employee, companies will not want to take the risk of hiring them when they have candidates with a lower risk score)…(More)”.

The state of AI in 2021

Curated on Posted on by Stefaan Verhulst

McKinsey Global Survey on AI: “..indicate that AI adoption continues to grow and that the benefits remain significant— though in the COVID-19 pandemic’s first year, they were felt more strongly on the cost-savings front than the top line. As AI’s use in business becomes more common, the tools and best practices to make the most out of AI have also become more sophisticated. We looked at the practices of the companies seeing the biggest earnings boost from AI and found that they are not only following more of both the core and advanced practices, including machine-learning operations (MLOps), that underpin success but also spending more efficiently on AI and taking more advantage of cloud technologies. Additionally, they are more likely than other organizations to engage in a range of activities to mitigate their AI-related risks—an area that continues to be a shortcoming for many companies’ AI efforts…(More)”.

The role of artificial intelligence in disinformation

Curated on Posted on by Stefaan Verhulst

Paper by Noémi Bontridder and Yves Poullet: “Artificial intelligence (AI) systems are playing an overarching role in the disinformation phenomenon our world is currently facing. Such systems boost the problem not only by increasing opportunities to create realistic AI-generated fake content, but also, and essentially, by facilitating the dissemination of disinformation to a targeted audience and at scale by malicious stakeholders. This situation entails multiple ethical and human rights concerns, in particular regarding human dignity, autonomy, democracy, and peace. In reaction, other AI systems are developed to detect and moderate disinformation online. Such systems do not escape from ethical and human rights concerns either, especially regarding freedom of expression and information. Having originally started with ascending co-regulation, the European Union (EU) is now heading toward descending co-regulation of the phenomenon. In particular, the Digital Services Act proposal provides for transparency obligations and external audit for very large online platforms’ recommender systems and content moderation. While with this proposal, the Commission focusses on the regulation of content considered as problematic, the EU Parliament and the EU Council call for enhancing access to trustworthy content. In light of our study, we stress that the disinformation problem is mainly caused by the business model of the web that is based on advertising revenues, and that adapting this model would reduce the problem considerably. We also observe that while AI systems are inappropriate to moderate disinformation content online, and even to detect such content, they may be more appropriate to counter the manipulation of the digital ecosystem….(More)”.

Human-Centered AI

Curated on Posted on by Stefaan Verhulst

Book by Ben Shneiderman: “The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology.

In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist’s guide to how artificial intelligence can be used to augment and enhance humans’ lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity…(More)”.

Group Backed by Top Companies Moves to Combat A.I. Bias in Hiring

Curated on Posted on by Stefaan Verhulst

Steve Lohr at The New York Times: “Artificial intelligence software is increasingly used by human resources departments to screen résumés, conduct video interviews and assess a job seeker’s mental agility.

Now, some of the largest corporations in America are joining an effort to prevent that technology from delivering biased results that could perpetuate or even worsen past discrimination.

The Data & Trust Alliance, announced on Wednesday, has signed up major employers across a variety of industries, including CVS Health, Deloitte, General Motors, Humana, IBM, Mastercard, Meta (Facebook’s parent company), Nike and Walmart.

The corporate group is not a lobbying organization or a think tank. Instead, it has developed an evaluation and scoring system for artificial intelligence software.

The Data & Trust Alliance, tapping corporate and outside experts, has devised a 55-question evaluation, which covers 13 topics, and a scoring system. The goal is to detect and combat algorithmic bias.“This is not just adopting principles, but actually implementing something concrete,” said Kenneth Chenault, co-chairman of the group and a former chief executive of American Express, which has agreed to adopt the anti-bias tool kit…(More)”.