CEPS Task Force on Artificial Intelligence and Cybersecurity Technology, Governance and Policy Challenges Task Force Evaluation of the HLEG Trustworthy AI Assessment List (Pilot Version). CEPS Task Force Report 22 January 2020

The Centre for European Policy Studies launched a Task Force on Artificial Intelligence (AI) and Cybersecurity in September 2019. The goal of this Task Force is to bring attention to the market, technical, ethical and governance challenges posed by the intersection of AI and cybersecurity, focusing both on AI for cybersecurity but also cybersecurity for AI. The Task Force is multi-stakeholder by design and composed of academics, industry players from various sectors, policymakers and civil society. The Task Force is currently discussing issues such as the state and evolution of the application of AI in cybersecurity and cybersecurity for AI; the debate on the role that AI could play in the dynamics between cyber attackers and defenders; the increasing need for sharing information on threats and how to deal with the vulnerabilities of AI-enabled systems; options for policy experimentation; and possible EU policy measures to ease the adoption of AI in cybersecurity in Europe. As part of such activities, this report aims at assessing the High-Level Expert Group (HLEG) on AI Ethics Guidelines for Trustworthy AI, presented on April 8, 2019. In particular, this report analyses and makes suggestions on the Trustworthy AI Assessment List (Pilot version), a non-exhaustive list aimed at helping the public and the private sector in operationalising Trustworthy AI. The list is composed of 131 items that are supposed to guide AI designers and developers throughout the process of design, development, and deployment of AI, although not intended as guidance to ensure compliance with the applicable laws. The list is in its piloting phase and is currently undergoing a revision that will be finalised in early 2020. This report would like to contribute to this revision by addressing in particular the interplay between AI and cybersecurity. This evaluation has been made according to specific criteria: whether and how the items of the Assessment List refer to existing legislation (e.g. GDPR, EU Charter of Fundamental Rights); whether they refer to moral principles (but not laws); whether they consider that AI attacks are fundamentally different from traditional cyberattacks; whether they are compatible with different risk levels; whether they are flexible enough in terms of clear/easy measurement, implementation by AI developers and SMEs; and overall, whether they are likely to create obstacles for the industry. The HLEG is a diverse group, with more than 50 members representing different stakeholders, such as think tanks, academia, EU Agencies, civil society, and industry, who were given the difficult task of producing a simple checklist for a complex issue. The public engagement exercise looks successful overall in that more than 450 stakeholders have signed in and are contributing to the process. The next sections of this report present the items listed by the HLEG followed by the analysis and suggestions raised by the Task Force (see list of the members of the Task Force in Annex 1)

The Need for International AI Activities Monitoring

This paper focuses primarily on the need to monitor the risks arising from the dual-use of Artificial Intelligence (AI). Dual-use AI technology capability makes it applicable for defense systems and consequently may pose significant security risks, both intentional and unintentional, with the national and international scope of effects. While domestic use of AI remains the prerogative of individual countries, the unregulated and nonmonitored use of AI with international implications presents a specific concern. An international organization tasked with monitoring potential threats of AI activities could help defuse AI-associated risks and promote global cooperation in developing and deploying AI technology. The paper reviews factors involved in the international monitoring of AI activities, strategies of dual-use technologies regulation, challenges, and potential solutions

Review of Research on Human Trust in Artificial Intelligence

Artificial Intelligence (AI) represents today\u27s most advanced technologies that aim to imitate human intelligence. Whether AI can successfully be integrated into society depends on whether it can gain users’ trust. We conduct a comprehensive review of recent research on human trust in AI and uncover the significant role of AI’s transparency, reliability, performance, and anthropomorphism in developing trust. We also review how trust is diversely built and calibrated, and how human and environmental factors affect human trust in AI. Based on the review, the most promising future research directions are proposed

A review on AI Safety in highly automated driving

Remarkable progress in the fields of machine learning (ML) and artificial intelligence (AI) has led to an increased number of applications of (data-driven) AI systems for the partial or complete control of safety-critical systems. Recently, ML solutions have been particularly popular. Such approaches are often met with concerns regarding their correct and safe execution, which is often caused by missing knowledge or intransparency of their exact functionality. The investigation and derivation of methods for the safety assessment of AI systems are thus of great importance. Among others, these issues are addressed in the field of AI Safety. The aim of this work is to provide an overview of this field by means of a systematic literature review with special focus on the area of highly automated driving, as well as to present a selection of approaches and methods for the safety assessment of AI systems. Particularly, validation, verification, and testing are considered in light of this context. In the review process, two distinguished classes of approaches have been identified: On the one hand established methods, either referring to already published standards or well-established concepts from multiple research areas outside ML and AI. On the other hand newly developed approaches, including methods tailored to the scope of ML and AI which gained importance only in recent years

Human-agent collectives

We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People’s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented

Software Engineering meets Artificial Intelligence

With the increasing use of AI in classic software systems, two worlds are coming closer and closer to each other that were previously rather alien to each other, namely the established discipline of software engineering and the world of AI. On the one hand, there are the data scientists, who try to extract as many insights as possible from the data using various tools, a lot of freedom and creativity. On the other hand, the software engineers, who have learned over years and decades to deliver the highest quality software possible and to manage release statuses. When developing software systems that include AI components, these worlds collide. This article shows which aspects come into play here, which problems can occur, and how solutions to these problems might look like. Beyond that, software engineering itself can benefit from the use of AI methods. Thus, we will also look at the emerging research area AI for software engineering