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Responsible AI from principles to practice

#artificialintelligence

Over the past several years, the United States and several of its leading allies have expressed a commitment to the responsible development and use of artificial intelligence (AI) for national security. Most notably, the U.S. Department of Defense adopted five principles for the safe and ethical application of AI in 2020, and the U.S. Defense Innovation Unit published a set of Responsible AI Guidelines in fall 2021. Meanwhile, NATO recently released its strategy for the responsible development and use of AI, and the United Kingdom's Ministry of Defense is actively developing ethical principles of its own. On January 31, Brookings will host a virtual event to compare and discuss how the United States and its allies are addressing ethical considerations in their pursuit and integration of military applications of AI-enabled technologies. What are the processes used by countries and international organizations to define applicable AI principles? What lessons have been learned from implementing those principles into practice?


Forecasting: theory and practice

arXiv.org Machine Learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.


The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

arXiv.org Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.


Europe and AI: Leading, Lagging Behind, or Carving Its Own Way?

#artificialintelligence

For its AI ecosystem to thrive, Europe needs to find a way to protect its research base, encourage governments to be early adopters, foster its startup ecosystem, expand international links, and develop AI technologies as well as leverage their use efficiently.


Not smart enough: The poverty of European military thinking on artificial intelligence

#artificialintelligence

"Artificial intelligence" (AI) has become one of the buzzwords of the decade, as a potentially important part of the answer to humanity's biggest challenges in everything from addressing climate change to fighting cancer and even halting the ageing process. It is widely seen as the most important technological development since the mass use of electricity, one that will usher in the next phase of human evolution. At the same time, some warnings that AI could lead to widespread unemployment, rising inequality, the development of surveillance dystopias, or even the end of humanity are worryingly convincing. States would, therefore, be well advised to actively guide AI's development and adoption into their societies. For Europe, 2019 was the year of AI strategy development, as a growing number of EU member states put together expert groups, organised public debates, and published strategies designed to grapple with the possible implications of AI. European countries have developed training programmes, allocated investment, and made plans for cooperation in the area. Next year is likely to be an important one for AI in Europe, as member states and the European Union will need to show that they can fulfil their promises by translating ideas into effective policies. But, while Europeans are doing a lot of work on the economic and societal consequences of the growing use of AI in various areas of life, they generally pay too little attention to one aspect of the issue: the use of AI in the military realm. Strikingly, the military implications of AI are absent from many European AI strategies, as governments and officials appear uncomfortable discussing the subject (with the exception of the debate on limiting "killer robots"). Similarly, the academic and expert discourse on AI in the military also tends to overlook Europe, predominantly focusing on developments in the US, China, and, to some extent, Russia. This is likely because most researchers consider Europe to be an unimportant player in the area.


Understanding artificial intelligence ethics and safety

arXiv.org Artificial Intelligence

A remarkable time of human promise has been ushered in by the convergence of the ever-expanding availability of big data, the soaring speed and stretch of cloud computing platforms, and the advancement of increasingly sophisticated machine learning algorithms. Innovations in AI are already leaving a mark on government by improving the provision of essential social goods and services from healthcare, education, and transportation to food supply, energy, and environmental management. These bounties are likely just the start. The prospect that progress in AI will help government to confront some of its most urgent challenges is exciting, but legitimate worries abound. As with any new and rapidly evolving technology, a steep learning curve means that mistakes and miscalculations will be made and that both unanticipated and harmful impacts will occur. This guide, written for department and delivery leads in the UK public sector and adopted by the British Government in its publication, 'Using AI in the Public Sector,' identifies the potential harms caused by AI systems and proposes concrete, operationalisable measures to counteract them. It stresses that public sector organisations can anticipate and prevent these potential harms by stewarding a culture of responsible innovation and by putting in place governance processes that support the design and implementation of ethical, fair, and safe AI systems. It also highlights the need for algorithmically supported outcomes to be interpretable by their users and made understandable to decision subjects in clear, non-technical, and accessible ways. Finally, it builds out a vision of human-centred and context-sensitive implementation that gives a central role to communication, evidence-based reasoning, situational awareness, and moral justifiability.


Hows and Whys of Artificial Intelligence for Public Sector Decisions: Explanation and Evaluation

arXiv.org Artificial Intelligence

Evaluation has always been a key challenge in the development of artificial intelligence (AI) based software, due to the technical complexity of the software artifact and, often, its embedding in complex sociotechnical processes. Recent advances in machine learning (ML) enabled by deep neural networks has exacerbated the challenge of evaluating such software due to the opaque nature of these ML-based artifacts. A key related issue is the (in)ability of such systems to generate useful explanations of their outputs, and we argue that the explanation and evaluation problems are closely linked. The paper models the elements of a ML-based AI system in the context of public sector decision (PSD) applications involving both artificial and human intelligence, and maps these elements against issues in both evaluation and explanation, showing how the two are related. We consider a number of common PSD application patterns in the light of our model, and identify a set of key issues connected to explanation and evaluation in each case. Finally, we propose multiple strategies to promote wider adoption of AI/ML technologies in PSD, where each is distinguished by a focus on different elements of our model, allowing PSD policy makers to adopt an approach that best fits their context and concerns.