Background
In 2017 Elon Musk called for regulation of AI development. According to NPR, the Tesla CEO was "clearly not thrilled" to be advocating for government scrutiny that could impact his own industry, but believed the risks of going completely without oversight are too high: "Normally the way regulations are set up is when a bunch of bad things happen, there's a public outcry, and after many years a regulatory agency is set up to regulate that industry. It takes forever. That, in the past, has been bad but not something which represented a fundamental risk to the existence of civilization." In response, some politicians expressed skepticism about the wisdom of regulating a technology that is still in development. Responding both to Musk and to February 2017 proposals by European Union lawmakers to regulate AI and robotics, Intel CEO Brian Krzanich has argued that AI is in its infancy and that it is too early to regulate the technology. Instead of trying to regulate the technology itself, some scholars suggested developing common norms including requirements for the testing and transparency of algorithms, possibly in combination with some form of warranty.Perspectives
The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating AI. Regulation is now generally considered necessary to both encourage AI and manage associated risks. Public administration and policy considerations generally focus on the technical and economic implications and on trustworthy and human-centered AI systems, although regulation of artificial superintelligences is also considered. The basic approach to regulation focuses on the risks and biases of AI's underlying technology, i.e., machine-learning algorithms, at the level of the input data, algorithm testing, and the decision model, as well as whether explanations of biases in the code can be understandable for prospective recipients of the technology, and technically feasible for producers to convey. AI regulation could derive from basic principles. A 2020 Berkman Klein Center for Internet & Society meta-review of existing sets of principles, such as the Asilomar Principles and the Beijing Principles, identified eights such basic principles: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and respect for human values. AI law and regulations have been divided into three main topics, namely governance of autonomous intelligence systems, responsibility and accountability for the systems, and privacy and safety issues. A public administration approach sees a relationship between AI law and regulation, the ethics of AI, and 'AI society', defined as workforce substitution and transformation, social acceptance and trust in AI, and the transformation of human to machine interaction. The development of public sector strategies for management and regulation of AI is deemed necessary at the local, national, and international levels and in a variety of fields, from public service management and accountability to law enforcement, healthcare (especially the concept of a Human Guarantee), the financial sector, robotics, autonomous vehicles, the military and national security, and international law. Henry Kissinger,As a response to the AI control problem
Regulation of AI can be seen as positive social means to manage the AI control problem, i.e., the need to insure long-term beneficial AI, with other social responses such as doing nothing or banning being seen as impractical, and approaches such as enhancing human capabilities through transhumanism techniques like brain-computer interfaces being seen as potentially complementary. Regulation of research into artificial general intelligence (AGI) focuses on the role of review boards, from university or corporation to international levels, and on encouraging research into safe AI, together with the possibility of differential intellectual progress (prioritizing risk-reducing strategies over risk-taking strategies in AI development) or conducting international mass surveillance to perform AGI arms control. For instance, the 'AGI Nanny' is a proposed strategy, potentially under the control of humanity, for preventing the creation of a dangerous superintelligence as well as for addressing other major threats to human well-being, such as subversion of the global financial system, until a true superintelligence can be safely created. It entails the creation of a smarter-than-human, but not superintelligent, AGI system connected to a large surveillance network, with the goal of monitoring humanity and protecting it from danger." Regulation of conscious, ethically aware AGIs focuses on integrating them with existing human society and can be divided into considerations of their legal standing and of their moral rights. Regulation of AI has been seen as restrictive, with a risk of preventing the development of AGI.Global guidance
The development of a global governance board to regulate AI development was suggested at least as early as 2017. In December 2018, Canada and France announced plans for a G7-backed International Panel on Artificial Intelligence, modeled on the International Panel on Climate Change, to study the global effects of AI on people and economies and to steer AI development. In 2019, the Panel was renamed the Global Partnership on AI. The Global Partnership on Artificial Intelligence was launched in June 2020, stating a need for AI to be developed in accordance with human rights and democratic values, to ensure public confidence and trust in the technology, as outlined in the OECD ''Principles on Artificial Intelligence'' (2019). The founding members of the Global Partnership on Artificial Intelligence are Australia, Canada, the European Union, France, Germany, India, Italy, Japan, Rep. Korea, Mexico, New Zealand, Singapore, Slovenia, the USA and the UK. The GPAI Secretariat is hosted by the OECD in Paris, France. GPAI’s mandate covers four themes, two of which are supported by the International Centre of Expertise in Montréal for the Advancement of Artificial Intelligence, namely, responsible AI and data governance. A corresponding centre of excellence in Paris, yet to be identified, will support the other two themes on the future of work and innovation, and commercialization. GPAI will also investigate how AI can be leveraged to respond to the Covid-19 pandemic. The OECD Recommendations on AI were adopted in May 2019, and the G20 AI Principles in June 2019. In September 2019 the World Economic Forum issued ten 'AI Government Procurement Guidelines'. In February 2020, the European Union published its draft strategy paper for promoting and regulating AI. At the United Nations (UN), several entities have begun to promote and discuss aspects of AI regulation and policy, including theRegional and national regulation
Canada
The ''Pan-Canadian Artificial Intelligence Strategy'' (2017) is supported by federal funding of Can $125 million with the objectives of increasing the number of outstanding AI researchers and skilled graduates in Canada, establishing nodes of scientific excellence at the three major AI centres, developing ‘global thought leadership’ on the economic, ethical, policy and legal implications of AI advances and supporting a national research community working on AI. The Canada CIFAR AI Chairs Program is the cornerstone of the strategy. It benefits from funding of Can$86.5 million over five years to attract and retain world-renowned AI researchers. The federal government appointed an Advisory Council on AI in May 2019 with a focus on examining how to build on Canada’s strengths to ensure that AI advancements reflect Canadian values, such as human rights, transparency and openness. The Advisory Council on AI has established a working group on extracting commercial value from Canadian-owned AI and data analytics. In 2020, the federal government and Government of Quebec announced the opening of the International Centre of Expertise in Montréal for the Advancement of Artificial Intelligence, which will advance the cause of responsible development of AI. In 2022, the Canadian Federal Government tabled a bill for the Artificial Intelligence and Data Act. In November 2022, Canada has introduced the Digital Charter Implementation Act (Bill C-27), which proposes three acts that have been described as a holistic package of legislation for trust and privacy: the Consumer Privacy Protection Act, the Personal Information and Data Protection Tribunal Act, and the Artificial Intelligence & Data Act (AIDA).China
The regulation of AI in China is mainly governed by theCouncil of Europe
TheEuropean Union
Most European Union (EU) countries have their own national strategies towards regulating AI, but these are largely convergent. The European Union is guided by a European Strategy on Artificial Intelligence, supported by a High-Level Expert Group on Artificial Intelligence. In April 2019, the European Commission published its ''Ethics Guidelines for Trustworthy Artificial Intelligence (AI)'', following this with its ''Policy and investment recommendations for trustworthy Artificial Intelligence'' in June 2019. The EU Commission’s High Level Expert Group on Artificial Intelligence carries out work on Trustworthy AI, and the Commission has issued reports on the Safety and Liability Aspects of AI and on the Ethics of Automated Vehicles. In 2020 the EU Commission sought views on a proposal for AI specific legislation, and that process is ongoing. On February 2, 2020, the European Commission published its ''White Paper on Artificial Intelligence - A European approach to excellence and trust''. The White Paper consists of two main building blocks, an ‘ecosystem of excellence’ and a ‘ecosystem of trust’. The latter outlines the EU's approach for a regulatory framework for AI. In its proposed approach, the Commission differentiates between 'high-risk' and 'non-high-risk' AI applications. Only the former should be in the scope of a future EU regulatory framework. Whether this would be the case could in principle be determined by two cumulative criteria, concerning critical sectors and critical use. Following key requirements are considered for high-risk AI applications: requirements for training data; data and record-keeping; informational duties; requirements for robustness and accuracy; human oversight; and specific requirements for specific AI applications, such as those used for purposes of remote biometric identification. AI applications that do not qualify as ‘high-risk’ could be governed by a voluntary labeling scheme. As regards compliance and enforcement, the Commission considers prior conformity assessments which could include 'procedures for testing, inspection or certification' and/or 'checks of the algorithms and of the data sets used in the development phase'. A European governance structure on AI in the form of a framework for cooperation of national competent authorities could facilitate the implementation of the regulatory framework. A January 2021 draft was leaked online on April 14, 2021, before the Commission ultimately presented their official "Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act)" a week later. Shortly after, theUnited Kingdom
The UK supported the application and development of AI in business via thUnited States
Discussions on regulation of AI in the United States have included topics such as the timeliness of regulating AI, the nature of the federal regulatory framework to govern and promote AI, including what agency should lead, the regulatory and governing powers of that agency, and how to update regulations in the face of rapidly changing technology, as well as the roles of state governments and courts. As early as 2016, the Obama administration had begun to focus on the risks and regulations for artificial intelligence. In a report titledBrazil
On September 30, 2021, the Brazilian Chamber of Deputies approved the Brazilian Legal Framework for Artificial Intelligence, Marco Legal da Inteligência Artificial, in regulatory efforts for the development and usage of AI technologies and to further stimulate research and innovation in AI solutions aimed at ethics, culture, justice, fairness, and accountability. This 10 article bill outlines objectives including missions to contribute to the elaboration of ethical principles, promote sustained investments in research, and remove barriers to innovation. Specifically, in article 4, the bill emphasizes the avoidance of discriminatory AI solutions, plurality, and respect for human rights. Furthermore, this act emphasizes the importance of the equality principle in deliberate decision-making algorithms, especially for highly diverse and multiethnic societies like that of Brazil. When the bill was first released to the public, it faced substantial criticism, alarming the government for critical provisions. The underlying issue is that this bill fails to thoroughly and carefully address accountability, transparency, and inclusivity principles. Article VI establishes subjective liability, meaning any individual that is damaged by an AI system and is wishing to receive compensation must specify the stakeholder and prove that there was a mistake in the machine’s life cycle. Scholars emphasize that it is out of legal order to assign an individual responsible for proving algorithmic errors given the high degree of autonomy, unpredictability, and complexity of AI systems. This also drew attention to the currently occurring issues with face recognition systems in Brazil leading to unjust arrests by the police, which would then imply that when this bill is adopted, individuals would have to prove and justify these machine errors. The main controversy of this draft bill was directed to three proposed principles. First, the non-discrimination principle, suggests that AI must be developed and used in a way that merely mitigates the possibility of abusive and discriminatory practices. Secondly, the pursuit of neutrality principle lists recommendations for stakeholders to mitigate biases; however, with no obligation to achieve this goal. Lastly, the transparency principle states that a system’s transparency is only necessary when there is a high risk of violating fundamental rights. As easily observed, the Brazilian Legal Framework for Artificial Intelligence lacks binding and obligatory clauses and is rather filled with relaxed guidelines. In fact, experts emphasize that this bill may even make accountability for AI discriminatory biases even harder to achieve. Compared to the EU’s proposal of extensive risk-based regulations, the Brazilian Bill has 10 articles proposing vague and generic recommendations. Compared to the multistakeholder participation approach taken previously in the 2000s when drafting the Brazilian Internet Bill of Rights, Marco Civil da Internet, the Brazilian Bill is assessed to significantly lack perspective. Multistakeholderism, more commonly referred to as Multistakeholder Governance, is defined as the practice of bringing multiple stakeholders to participate in dialogue, decision-making, and implementation of responses to jointly perceived problems. In the context of regulatory AI, this multistakeholder perspective captures the trade-offs and varying perspectives of different stakeholders with specific interests, which helps maintain transparency and broader efficacy. On the contrary, the legislative proposal for AI regulation did not follow a similar multistakeholder approach. Future steps may include, expanding upon the multistakeholder perspective. There has been a growing concern about the inapplicability of the framework of the bill, which highlights that the one-shoe-fits-all solution may not be suitable for the regulation of AI and calls for subjective and adaptive provisions.Regulation of fully autonomous weapons
Legal questions related to lethal autonomous weapons systems (LAWS), in particular compliance with the laws of armed conflict, have been under discussion at the United Nations since 2013, within the context of the Convention on Certain Conventional Weapons. Notably, informal meetings of experts took place in 2014, 2015 and 2016 and a Group of Governmental Experts (GGE) was appointed to further deliberate on the issue in 2016. A set of guiding principles on LAWS affirmed by the GGE on LAWS were adopted in 2018. In 2016, China published a position paper questioning the adequacy of existing international law to address the eventuality of fully autonomous weapons, becoming the first permanent member of the U.N. Security Council to broach the issue, and leading to proposals for global regulation. The possibility of a moratorium or preemptive ban of the development and use of LAWS has also been raised on several occasions by other national delegations to the Convention on Certain Conventional Weapons and is strongly advocated for by theSee also
* Artificial intelligence * Artificial intelligence arms race *References
{{reflist Existential risk from artificial general intelligence Computer law