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Regulation of algorithms, or algorithmic regulation, is the creation of laws, rules and public sector policies for promotion and regulation of algorithms, particularly in artificial intelligence and machine learning. [1] [2] [3] For the subset of AI algorithms, the term regulation of artificial intelligence is used. The regulatory and policy landscape for artificial intelligence (AI) is an emerging issue in jurisdictions globally, including in the European Union. [4] Regulation of AI is considered necessary to both encourage AI and manage associated risks, but challenging. [5] Another emerging topic is the regulation of blockchain algorithms (Use of the smart contracts must be regulated) and is mentioned along with regulation of AI algorithms. [6] Many countries have enacted regulations of high frequency trades, which is shifting due to technological progress into the realm of AI algorithms. [7]
The motivation for regulation of algorithms is the apprehension of losing control over the algorithms, whose impact on human life increases. Multiple countries have already introduced regulations in case of automated credit score calculation—right to explanation is mandatory for those algorithms. [8] [9] For example, The IEEE has begun developing a new standard to explicitly address ethical issues and the values of potential future users. [10] Bias, transparency, and ethics concerns have emerged with respect to the use of algorithms in diverse domains ranging from criminal justice [11] to healthcare [12] —many fear that artificial intelligence could replicate existing social inequalities along race, class, gender, and sexuality lines.
In 2016, Joy Buolamwini founded Algorithmic Justice League after a personal experience with biased facial detection software in order to raise awareness of the social implications of artificial intelligence through art and research. [13]
In 2017 Elon Musk advocated regulation of algorithms in the context of the existential risk from artificial general intelligence. [14] [15] [16] 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 civilisation." [14]
In response, some politicians expressed skepticism about the wisdom of regulating a technology that is still in development. [15] 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 artificial intelligence is in its infancy and that it is too early to regulate the technology. [16] Instead of trying to regulate the technology itself, some scholars suggest to rather develop common norms including requirements for the testing and transparency of algorithms, possibly in combination with some form of warranty. [17] One suggestion has been for the development of a global governance board to regulate AI development. [18] In 2020, the European Union published its draft strategy paper for promoting and regulating AI. [19]
Algorithmic tacit collusion is a legally dubious antitrust practise committed by means of algorithms, which the courts are not able to prosecute. [20] This danger concerns scientists and regulators in EU, US and beyond. [20] European Commissioner Margrethe Vestager mentioned an early example of algorithmic tacit collusion in her speech on "Algorithms and Collusion" on March 16, 2017, described as follows: [21]
"A few years ago, two companies were selling a textbook called The Making of a Fly. One of those sellers used an algorithm which essentially matched its rival’s price. That rival had an algorithm which always set a price 27% higher than the first. The result was that prices kept spiralling upwards, until finally someone noticed what was going on, and adjusted the price manually. By that time, the book was selling – or rather, not selling – for 23 million dollars a copy."
In 2018, the Netherlands employed an algorithmic system SyRI (Systeem Risico Indicatie) to detect citizens perceived being high risk for committing welfare fraud, which quietly flagged thousands of people to investigators. [22] This caused a public protest. The district court of Hague shut down SyRI referencing Article 8 of the European Convention on Human Rights (ECHR). [23]
In 2020, algorithms assigning exam grades to students in the UK sparked open protest under the banner "Fuck the algorithm." [24] This protest was successful and the grades were taken back. [25]
AI law and regulations can be divided into three main topics, namely governance of autonomous intelligence systems, responsibility and accountability for the systems, and privacy and safety issues. [5] The development of public sector strategies for management and regulation of AI has been increasingly deemed necessary at the local, national, [26] and international levels [19] and in a variety of fields, from public service management [27] to law enforcement, [19] the financial sector, [26] robotics, [28] the military, [29] and international law. [30] [31] There are many concerns that there is not enough visibility and monitoring of AI in these sectors. [32] In the financial sector, for example, there have been calls for the Consumer Financial Protection Bureau to more closely examine source code and algorithms when conducting audits of financial institutions' non-public data. [33]
In the United States, on January 7, 2019, following an Executive Order on 'Maintaining American Leadership in Artificial Intelligence', the White House's Office of Science and Technology Policy released a draft Guidance for Regulation of Artificial Intelligence Applications, which includes ten principles for United States agencies when deciding whether and how to regulate AI. [34] [35] In response, the National Institute of Standards and Technology has released a position paper, [36] the National Security Commission on Artificial Intelligence has published an interim report, [37] and the Defense Innovation Board has issued recommendations on the ethical use of AI. [38]
In April 2016, for the first time in more than two decades, the European Parliament adopted a set of comprehensive regulations for the collection, storage, and use of personal information, the General Data Protection Regulation (GDPR)1 (European Union, Parliament and Council 2016).[6] The GDPR's policy on the right of citizens to receive an explanation for algorithmic decisions highlights the pressing importance of human interpretability in algorithm design. [39]
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, [30] and leading to proposals for global regulation. [40] In the United States, steering on regulating security-related AI is provided by the National Security Commission on Artificial Intelligence. [41]
In 2017, the U.K. Vehicle Technology and Aviation Bill imposes liability on the owner of an uninsured automated vehicle when driving itself and makes provisions for cases where the owner has made “unauthorized alterations” to the vehicle or failed to update its software. Further ethical issues arise when, e.g., a driverless car swerves to avoid a pedestrian and causes a fatal accident. [42]
In 2021, the European Commission proposed the Artificial Intelligence Act. [43]
There is a concept of algorithm certification emerging as a method of regulating algorithms. Algorithm certification involves auditing whether the algorithm used during the life cycle 1) conforms to the protocoled requirements (e.g., for correctness, completeness, consistency, and accuracy); 2) satisfies the standards, practices, and conventions; and 3) solves the right problem (e.g., correctly model physical laws), and satisfies the intended use and user needs in the operational environment. [10]
Blockchain systems provide transparent and fixed records of transactions and hereby contradict the goal of the European GDPR, which is to give individuals full control of their private data. [44] [45]
By implementing the Decree on Development of Digital Economy, Belarus has become the first-ever country to legalize smart contracts. Belarusian lawyer Denis Aleinikov is considered to be the author of a smart contract legal concept introduced by the decree. [46] [47] [48] There are strong arguments that the existing US state laws are already a sound basis for the smart contracts' enforceability — Arizona, Nevada, Ohio and Tennessee have amended their laws specifically to allow for the enforceability of blockchain-based contracts nevertheless. [49]
There have been proposals to regulate robots and autonomous algorithms. These include:
In 1942, author Isaac Asimov addressed regulation of algorithms by introducing the fictional Three Laws of Robotics:
The main alternative to regulation is a ban, and the banning of algorithms is presently highly unlikely. However, in Frank Herbert's Dune universe, thinking machines is a collective term for artificial intelligence, which were completely destroyed and banned after a revolt known as the Butlerian Jihad: [51]
JIHAD, BUTLERIAN: (see also Great Revolt) — the crusade against computers, thinking machines, and conscious robots begun in 201 B.G. and concluded in 108 B.G. Its chief commandment remains in the O.C. Bible as "Thou shalt not make a machine in the likeness of a human mind." [52]
Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of humans or other animals. It is a field of study in computer science that develops and studies intelligent machines. Such machines may be called AIs.
An AI takeover is a hypothetical scenario in which artificial intelligence (AI) becomes the dominant form of intelligence on Earth, as computer programs or robots effectively take control of the planet away from the human species. Possible scenarios include replacement of the entire human workforce, takeover by a superintelligent AI, and the popular notion of a robot uprising. Stories of AI takeovers are very popular throughout science fiction. Some public figures, such as Stephen Hawking and Elon Musk, have advocated research into precautionary measures to ensure future superintelligent machines remain under human control.
Robot ethics, sometimes known as "roboethics", concerns ethical problems that occur with robots, such as whether robots pose a threat to humans in the long or short run, whether some uses of robots are problematic, and how robots should be designed such that they act 'ethically'. Alternatively, roboethics refers specifically to the ethics of human behavior towards robots, as robots become increasingly advanced. Robot ethics is a sub-field of ethics of technology, specifically information technology, and it has close links to legal as well as socio-economic concerns. Researchers from diverse areas are beginning to tackle ethical questions about creating robotic technology and implementing it in societies, in a way that will still ensure the safety of the human race.
The ethics of artificial intelligence is the branch of the ethics of technology specific to artificially intelligent systems. It is sometimes divided into a concern with the moral behavior of humans as they design, make, use and treat artificially intelligent systems, and a concern with the behavior of machines, in machine ethics.
Data portability is a concept to protect users from having their data stored in "silos" or "walled gardens" that are incompatible with one another, i.e. closed platforms, thus subjecting them to vendor lock-in and making the creation of data backups or moving accounts between services difficult.
Machine ethics is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence, otherwise known as artificial intelligent agents. Machine ethics differs from other ethical fields related to engineering and technology. Machine ethics should not be confused with computer ethics, which focuses on human use of computers. It should also be distinguished from the philosophy of technology, which concerns itself with the grander social effects of technology.
Fintech, a portmanteau of "financial technology", refers to firms using new technology to compete with traditional financial methods in the delivery of financial services. Artificial intelligence, blockchain, cloud computing, and big data are regarded as the "ABCD" of fintech. The use of smartphones for mobile banking, investing, borrowing services, and cryptocurrency are examples of technologies designed to make financial services more accessible to the general public. Fintech companies consist of both startups and established financial institutions and technology companies trying to replace or enhance the usage of financial services provided by existing financial companies.
Existential risk from artificial general intelligence is the idea that substantial progress in artificial general intelligence (AGI) could result in human extinction or an irreversible global catastrophe.
Artificial intelligence in healthcare is a term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to copy human cognition in the analysis, presentation, and understanding of complex medical and health care data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data.
Explainable AI (XAI), often overlapping with Interpretable AI, or Explainable Machine Learning (XML), either refers to an AI system over which it is possible for humans to retain intellectual oversight, or to the methods to achieve this. The main focus is usually on the reasoning behind the decisions or predictions made by the AI which are made more understandable and transparent. XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision.
In the regulation of algorithms, particularly artificial intelligence and its subfield of machine learning, a right to explanation is a right to be given an explanation for an output of the algorithm. Such rights primarily refer to individual rights to be given an explanation for decisions that significantly affect an individual, particularly legally or financially. For example, a person who applies for a loan and is denied may ask for an explanation, which could be "Credit bureau X reports that you declared bankruptcy last year; this is the main factor in considering you too likely to default, and thus we will not give you the loan you applied for."
A military artificial intelligence arms race is an arms race between two or more states to develop and deploy lethal autonomous weapons systems (LAWS). Since the mid-2010s, many analysts have noted the emergence of such an arms race between global superpowers for better military AI, driven by increasing geopolitical and military tensions. An AI arms race is sometimes placed in the context of an AI Cold War between the US and China.
Algorithmic entities refer to autonomous algorithms that operate without human control or interference. Recently, attention is being given to the idea of algorithmic entities being granted legal personhood. Professor Shawn Bayern and Professor Lynn M. LoPucki popularized through their papers the idea of having algorithmic entities that obtain legal personhood and the accompanying rights and obligations.
A data economy is a global digital ecosystem in which data is gathered, organized, and exchanged by a network of companies, individuals, and institutions to create economic value. The raw data is collected by a variety of actors, including search engines, social media websites, online vendors, brick and mortar vendors, payment gateways, software as a service (SaaS) purveyors, and an increasing number of firms deploying connected devices on the Internet of Things (IoT). Once collected, this data is typically passed on to individuals or firms, often for a fee. In the United States, the Consumer Financial Protection Bureau and other agencies have developed early models to regulate the data economy.
Sandra Wachter is a professor and senior researcher in data ethics, artificial intelligence, robotics, algorithms and regulation at the Oxford Internet Institute. She is a former Fellow of The Alan Turing Institute.
Government by algorithm is an alternative form of government or social ordering where the usage of computer algorithms is applied to regulations, law enforcement, and generally any aspect of everyday life such as transportation or land registration. The term "government by algorithm" has appeared in academic literature as an alternative for "algorithmic governance" in 2013. A related term, algorithmic regulation, is defined as setting the standard, monitoring and modifying behaviour by means of computational algorithms – automation of judiciary is in its scope. In the context of blockchain, it is also known as blockchain governance.
The regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence (AI); it is therefore related to the broader regulation of algorithms. The regulatory and policy landscape for AI is an emerging issue in jurisdictions globally, including in the European Union and in supra-national bodies like the IEEE, OECD and others. Since 2016, a wave of AI ethics guidelines have been published in order to maintain social control over the technology. Regulation is considered necessary to both encourage AI and manage associated risks. In addition to regulation, AI-deploying organizations need to play a central role in creating and deploying trustworthy AI in line with the principles of trustworthy AI, and take accountability to mitigate the risks. Regulation of AI through mechanisms such as review boards can also be seen as social means to approach the AI control problem.
Artificial intelligence (AI) in hiring involves the use of technology to automate aspects of the hiring process. Advances in artificial intelligence, such as the advent of machine learning and the growth of big data, enable AI to be utilized to recruit, screen, and predict the success of applicants. Proponents of artificial intelligence in hiring claim it reduces bias, assists with finding qualified candidates, and frees up human resource workers' time for other tasks, while opponents worry that AI perpetuates inequalities in the workplace and will eliminate jobs. Despite the potential benefits, the ethical implications of AI in hiring remain a subject of debate, with concerns about algorithmic transparency, accountability, and the need for ongoing oversight to ensure fair and unbiased decision-making throughout the recruitment process.
The Artificial Intelligence Act is a European Union regulation on artificial intelligence in the European Union. Proposed by the European Commission on 21 April 2021 and not yet enforced, it aims to introduce a common regulatory and legal framework for artificial intelligence.
Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM involves large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics. The increasing use of automated decision-making systems (ADMS) across a range of contexts presents many benefits and challenges to human society requiring consideration of the technical, legal, ethical, societal, educational, economic and health consequences.
This is an exact transcription of the laws. They also appear in the front of the book, and in both places there is no "to" in the 2nd law.