AI Governance

Table for book chapter on Multilateralism and AI:

The objective of this chapter is to discuss how multilateralism, and the United Nations (UN) in particular, can play a role in encouraging further engagement in artificial intelligence (AI) at the global level. In a fragmented landscape, despite numerous initiatives on AI principles by civil society, the industry, and some governments, the international governance of AI lacks coordination and has been plagued by competition. The UN has been active on several fronts, including UNESCO’s Ad Hoc Expert Group on AI ethics and the follow-up of the Secretary-General’s High-Level Panel on Digital Cooperation. The UN is well positioned to take advantage of ongoing initiatives to offer a credible multilateral track, under the auspices of the Organisation, towards ethical, human-centred, and peaceful uses of AI systems. At this juncture, advisory bodies and voluntary platforms can help pave the way for the UN to bridge differences by promoting international cooperation, prevention, and foresight.

How do we regulate a changing technology, with changing uses, in a changing world? This chapter argues that while existing (inter)national AI governance approaches are important, they are often siloed. Technology-centric approaches focus on individual AI applications; law-centric approaches emphasize AI's effects on pre-existing legal fields or doctrines. This chapter argues that to foster a more systematic, functional and effective AI regulatory ecosystem, policy actors should instead complement these approaches with a regulatory perspective that emphasizes how, when, and why AI applications enable patterns of 'sociotechnical change'. Drawing on theories from the emerging field of 'TechLaw', it explores how this perspective can provide informed, more nuanced, and actionable perspectives on AI regulation. A focus on sociotechnical change can help analyse when and why AI applications actually do create a meaningful rationale for new regulation-and how they are consequently best approached as targets for regulatory intervention, considering not just the technology, but also six distinct 'problem logics' that appear around AI issues across domains. The chapter concludes by briefly reviewing concrete institutional and regulatory actions that can draw on this approach in order to improve the regulatory triage, tailoring, timing & responsiveness, and design of AI policy.

Cyber attacks by domestic and foreign threat actors are increasing in frequency and sophistication. Cyber adversaries exploit a cybersecurity skill/knowledge gap and an open society, undermining the information security/privacy of citizens and businesses and eroding trust in governments, thus threatening social and political stability. The use of open digital hacking technologies in ethical hacking in higher education and within broader society raises ethical, technical, social, and political challenges for liberal democracies. Programs teaching ethical hacking in higher education are steadily growing but there is a concern that teaching students hacking skills increases crime risk to society by drawing students toward criminal acts. A cybersecurity skill gap undermines the security/viability of business and government institutions. The thesis presents an examination of opportunities and risks involved in using AI powered intelligence gathering/surveillance technologies in ethical hacking teaching practices in Canada. Taking a qualitative exploratory case study approach, technoethical inquiry theory (Bunge-Luppicini) and Weick’s sensemaking model were applied as a sociotechnical theory (STEI-KW) to explore ethical hacking teaching practices in two Canadian universities. In-depth interviews with ethical hacking university experts, industry practitioners, and policy experts, and a document review were conducted. Findings pointed to a skill/knowledge gap in ethical hacking literature regarding the meanings, ethics, values, skills/knowledge, roles and responsibilities, and practices of ethical hacking and ethical hackers which underlies an identity and legitimacy crisis for professional ethical hacking practitioners; and a Teaching vs Practice cybersecurity skill gap in ethical hacking curricula. Two main S&T innovation risk mitigation initiatives were explored: An OSINT Analyst cybersecurity role and associated body of knowledge foundation framework as an interdisciplinary research area, and a networked centre of excellence of ethical hacking communities of practice as a knowledge management and governance/policy innovation approach focusing on the systematization and standardization of an ethical hacking body of knowledge.

The objective of this chapter is to discuss how multilateralism, and the United Nations (UN) in particular, can play a role in encouraging further engagement in artificial intelligence (AI) at the global level. In a fragmented landscape, despite numerous initiatives on AI principles by civil society, the industry, and some governments, the international governance of AI lacks coordination and has been plagued by competition. The UN has been active on several fronts, including UNESCO's Ad Hoc Expert Group on AI ethics and the follow-up of the Secretary-General's High-Level Panel on Digital Cooperation. The UN is well positioned to take advantage of ongoing initiatives to offer a credible multilateral track, under the auspices of the Organisation, towards ethical, human-centred, and peaceful uses of AI systems. At this juncture, advisory bodies and voluntary platforms can help pave the way for the UN to bridge differences by promoting international cooperation, prevention, and foresight.

This report presents the outcomes of the Open Loop policy prototyping program on Automated Decision Impact Assessment (ADIA) in Europe. Open Loop (www.openloop.org) is a global program that connects policymakers and technology companies to help develop effective and evidence-based policies around AI and other emerging technologies.

In this particular case, Open Loop partnered with 10 European AI companies to co-create an ADIA framework (policy prototype) that
those companies could test by applying it to their own
AI applications. The policy prototype was structured into
two parts: the prototype law, which was drafted as legal text, and the prototype guidance, which was drafted as a playbook. The latter provided participants with additional guidance on procedural and substantive aspects of performing the ADIA through:
- A step-by-step risk assessment methodology;
- An overview of values often associated with AI applications;
- A taxonomy of harms;
- Examples of mitigating measures.

The prototype was tested against the following three
criteria: 1) policy understanding; 2) policy effectiveness; 3) policy costs.The goal was to derive evidence-based recommendations relevant to ongoing policy debates around the future of AI regulation.

Based on the results of the prototyping exercise and the feedback on the prototype law and playbook, the report advises lawmakers formulating requirements for AI risk assessments to take the following recommendations into account:

- Focus on procedure instead of prescription as a way to determine high-risk AI applications;
- Leverage a procedural risk assessment approach to determine what is the right set of regulatory requirements that apply to organisations deploying AI applications;
- Provide specific and detailed guidance on how to implement an ADIA process, and release it alongside the law;
- Be as specific as possible in the definition of risks within regulatory scope;
- Improve documentation of risk assessment and decision-making processes by including justifications for mitigation choices;
- Develop a sound taxonomy of the different AI actors involved in risk assessment;
- Specify, as much as possible, the set of values that may be impacted by AI/ADM and provide guidance on how they may be in tension with one another;
- Don’t reinvent the wheel; combine new processes with established ones, improving the overall approach.

UPDATED—March 3, 2020. How do you turn a broad amorphous concept like human morality into something that can be instilled in a new system? In the case of artificial intelligence, a critical inquiry will happen in the long run. The only way to better prepare for the future and prevent the unfortunate consequences AGI and superintelligence will bring is delving into the critical questions AI raises.
For instance, the ultimate challenge of machine ethics: how do you build an AI which, when it executes, becomes more ethical than you; and the AI governance problem: the problem of devising global norms, policies, and institutions to best ensure the beneficial development and use of advanced AI.
In this paper, these two points will be taken through an introduction for a normative theory I will propose that is fit for the new world order transformative technologies like artificial intelligence bring about.

[Copy of full thesis manuscript is available for request to the author.]

With the introduction of Artificial Intelligence (AI) and related technologies in our daily lives, fear and anxiety about their misuse as well as their inherent biases, incorporated during their creation, have led to a demand for governance and associated regulation. Yet regulating an innovation process that is not well understood may stifle this process and reduce benefits that society may gain from the generated technology, even under the best intentions. Instruments to shed light on such processes are thus needed as they can ensure that imposed policies achieve the ambitions for which they were designed. Starting from a game-theoretical model that captures the fundamental dynamics of a race for domain supremacy using AI technology, we show how socially unwanted outcomes may be produced when sanctioning is applied unconditionally to risk-taking, i.e. potentially unsafe, behaviours. We demonstrate here the potential of a regulatory approach that combines a voluntary commitment approach reminiscent of soft law, wherein technologists have the freedom of choice between independently pursuing their course of actions or establishing binding agreements to act safely, with either a peer or governmental sanctioning system of those that do not abide by what they pledged. As commitments are binding and sanctioned, they go beyond the classic view of soft law, akin more closely to actual law-enforced regulation. Overall, this work reveals how voluntary but sanctionable commitments generate socially beneficial outcomes in all scenarios envisageable in a short-term race towards domain supremacy through AI technology. These results provide an original dynamic systems perspective of the governance potential of enforceable soft law techniques or co-regulatory mechanisms, showing how they may impact the ambitions of developers in the context of the AIbased applications.

The international governance of artificial intelligence (AI) is at a crossroads: should it remain fragmented or be centralised? We draw on the history of environment, trade, and security regimes to identify advantages and disadvantages in centralising AI governance. Some considerations, such as efficiency and political power, speak for centralisation. The risk of creating a slow and brittle institution, and the difficulty of pairing deep rules with adequate participation, speak against it. Other considerations depend on the specific design. A centralised body may be able to deter forum shopping and ensure policy coordination. However, forum shopping can be beneficial, and fragmented institutions could self-organise. In sum, these trade-offs should inform development of the AI governance architecture, which is only now emerging. We apply the trade-offs to the case of the potential development of high-level machine intelligence. We conclude with two recommendations. First, the outcome will depend on the exact design of a central institution. A well-designed centralised regime covering a set of coherent issues could be beneficial. But locking-in an inadequate structure may pose a fate worse than fragmentation. Second, fragmentation will likely persist for now. The developing landscape should be monitored to see if it is self-organising or simply inadequate.

Rapid technological advancements in Artificial Intelligence (AI), as well as the growing deployment of intelligent technologies in new application domains, have generated serious anxiety and a fear of missing out among different stake-holders, fostering a racing narrative. Whether real or not, the belief in such a race for domain supremacy through AI, can make it real simply from its consequences, as put forward by the Thomas theorem. These consequences may be negative, as racing for technological supremacy creates a complex ecology of choices that could push stake-holders to underestimate or even ignore ethical and safety procedures. As a consequence, different actors are urging to consider both the normative and social impact of these technological advancements, contemplating the use of the precautionary principle in AI innovation and research. Yet, given the breadth and depth of AI and its advances, it is difficult to assess which technology needs regulation and when. As there is no easy access to data describing this alleged AI race, theoretical models are necessary to understand its potential dynamics, allowing for the identification of when procedures need to be put in place to favour outcomes beneficial for all. We show that, next to the risks of setbacks and being reprimanded for unsafe behaviour, the timescale in which domain supremacy can be achieved plays a crucial role. When this can be achieved in a short term, those who completely ignore the safety precautions are bound to win the race but at a cost to society, apparently requiring regulatory actions. Our analysis reveals that imposing regulations for all risk and timing conditions may not have the anticipated effect as only for specific conditions a dilemma arises between what is individually preferred and globally beneficial. Similar observations can be made for the long-term development case. Yet different from the short-term situation, conditions can be identified that require the promotion of risk-taking as opposed to compliance with safety regulations in order to improve social welfare. These results remain robust both when two or several actors are involved in the race and when collective rather than individual setbacks are produced by risk-taking behaviour. When defining codes of conduct and regulatory policies for applications of AI, a clear understanding of the timescale of the race is thus required, as this may induce important non-trivial effects.

This chapter argues that the time has come for Artificial Intelligence (AI) and robotics researchers to welcome the contributions to good product design that ethical review adds to the research and development process. The argument against AI and robotics ethics boards largely turns on a failure to appreciate the distinction between AI entities and intelligent agents as well as the difference between human moral agents vs. artificial moral agents (AMAs). AI and robotics applications have a number of applications that are already raising ethical concerns in areas as diverse as the home, the workplace, medical, military, and police work. Ethics boards of the more distant future will have the unenviable position of being one of the last locations where humans can significantly control these artificial entities. The chapter argues that AI and Robotics researchers seek some professional ethical guidance as they create some of the most potentially disruptive technologies in human history.