Government by algorithm encompasses a range of approaches involving the application of computer algorithms to aspects of society and governance. This term, also referred to as algorithmic regulation, regulation by algorithms, algorithmic governance, algocratic governance, algorithmic legal order, or algocracy, describes the use of algorithms to influence or govern sectors such as law enforcement, transportation, and land registration.. The term "government by algorithm" was first introduced in academic literature in 2013 as an alternative for "algorithmic governance". A related term, algorithmic regulation, involves the use of computational algorithms to set standards, monitor, and modify behavior within specific contexts, such as the automation of the judiciary.
Government by algorithm raises challenges that are not fully addressed in the existing e-government literature or public administration practice. Some sources equate cyberocracy, which is a hypothetical form of government that rules by the effective use of information,. with algorithmic governance, although algorithms are not the only means of processing information. Nello Cristianini and Teresa Scantamburlo argued that the combination of a human society and certain regulation algorithms (such as reputation-based scoring) forms a social machine.
History
In 1962, the director of the Institute for Information Transmission Problems of the Russian Academy of Sciences in Moscow (later Kharkevich Institute), Alexander Kharkevich, published an article in the journal "Communist" about a computer network for processing information and control of the economy. He proposed a computer network resembling aspects of the modern Internet for the purposes of algorithmic governance (Project OGAS). This proposal raised concerns among CIA analysts. In particular, Arthur M. Schlesinger Jr. warned that "by 1970 the USSR may have a radically new production technology, involving total enterprises or complexes of industries, managed by closed-loop, feedback control employing self-teaching computers".
Between 1971 and 1973, the Chilean government carried out Project Cybersyn during the presidency of Salvador Allende. This project was aimed at constructing a distributed decision support system to improve the management of the national economy. Elements of the project were used in 1972 to successfully overcome the traffic collapse caused by a CIA-sponsored strike of forty thousand truck drivers. Besides that initial project, its architect, Stafford Beer was involved in similar cybernetic projects across Latin America during the 1980s and 1990s, as the region gradually returned to democracy, particularly in Uruguay and Colombia, in which his Viable System Model's (VSM) was applied.
Also in the 1960s and 1970s, Herbert A. Simon championed expert systems as tools for rationalization and evaluation of administrative behavior. The automation of rule-based processes was an ambition of tax agencies over many decades resulting in varying success. Early work from this period includes Thorne McCarty's influential TAXMAN project in the US and Ronald Stamper's LEGOL project in the UK. In 1993, the computer scientist Paul Cockshott from the University of Glasgow and the economist Allin Cottrell from the Wake Forest University published the book Towards a New Socialism, where they claim to demonstrate the possibility of a democratically planned economy built on modern computer technology. The Honourable Justice Michael Kirby published a paper in 1998, where he expressed optimism that the then-available computer technologies such as legal expert system could evolve to computer systems, which will strongly affect the practice of courts. In 2006, attorney Lawrence Lessig, known for the slogan "Code is law", wrote:
[T]he invisible hand of cyberspace is building an architecture that is quite the opposite of its architecture at its birth. This invisible hand, pushed by government and by commerce, is constructing an architecture that will perfect control and make highly efficient regulation possible
Since the 2000s, algorithms have been designed and used to automatically analyze surveillance videos.
In his 2006 book Virtual Migration, A. Aneesh developed the concept of algocracy — information technologies constrain human participation in public decision making. Aneesh differentiated algocratic systems from bureaucratic systems (legal-rational regulation) as well as market-based systems (price-based regulation).
In 2013, the concept of algorithmic regulation was popularized by Tim O’Reilly, founder and CEO of O’Reilly Media:
Sometimes the "rules" aren't really even rules. Gordon Bruce, the former CIO of the city of Honolulu, explained to me that when he entered government from the private sector and tried to make changes, he was told, "That's against the law." His reply was "OK. Show me the law." "Well, it isn't really a law. It's a regulation." "OK. Show me the regulation." "Well, it isn't really a regulation. It's a policy that was put in place by Mr. Somebody twenty years ago." "Great. We can change that!" [...] Laws should specify goals, rights, outcomes, authorities, and limits. If specified broadly, those laws can stand the test of time. Regulations, which specify how to execute those laws in much more detail, should be regarded in much the same way that programmers regard their code and algorithms, that is, as a constantly updated toolset to achieve the outcomes specified in the laws. [...] It's time for government to enter the age of big data. Algorithmic regulation is an idea whose time has come.
In 2017, Ukraine's Ministry of Justice ran experimental government auctions using blockchain technology to ensure transparency and hinder corruption in governmental transactions. "Government by Algorithm?" was the central theme introduced at Data for Policy 2017 conference held on 6–7 September 2017 in London.
Examples Smart cities
A smart city is an urban area where collected surveillance data is used to improve various operations. Increases in computational power allow more automated decision-making and replacement of public agencies by algorithmic governance. In particular, the combined use of artificial intelligence and blockchains for IoT may lead to the creation of sustainable smart city ecosystems. Intelligent street lighting in Glasgow is an example of successful government application of AI algorithms. A study of smart city initiatives in the US shows that it requires public sector as a main organizer and coordinator, the private sector as a technology and infrastructure provider, and universities as expertise contributors.
The cryptocurrency millionaire Jeffrey Berns proposed the operation of local governments in Nevada by tech firms in 2021. Berns bought 67,000 acres (271 km2) in Nevada's rural Storey County (population 4,104) for $170,000,000 (£121,000,000) in 2018 in order to develop a smart city with more than 36,000 residents that could generate an annual output of $4,600,000,000. Cryptocurrency would be allowed for payments. Blockchains, Inc. "Innovation Zone" was canceled in September 2021 after it failed to secure enough water for the planned 36,000 residents, through water imports from a site located 100 miles away in the neighboring Washoe County. A similar water pipeline proposed in 2007 was estimated to cost $100 million and would have taken about 10 years to develop. With additional water rights purchased from Tahoe Reno Industrial General Improvement District, "Innovation Zone" would have acquired enough water for about 15,400 homes – meaning that it would have barely covered its planned 15,000 dwelling units, leaving nothing for the rest of the projected city and its 22 million square-feet of industrial development.
In Saudi Arabia, the planners of The Line assert that it will be monitored by AI to improve life by using data and predictive modeling.
Reputation systems
Tim O'Reilly suggested that data sources and reputation systems combined in algorithmic regulation can outperform traditional regulations. For instance, once taxi-drivers are rated by passengers, the quality of their services will improve automatically and "drivers who provide poor service are eliminated". O'Reilly's suggestion is based on the control-theoretic concept of feed-back loop—improvements and disimprovements of reputation enforce desired behavior. The usage of feedback-loops for the management of social systems has already been suggested in management cybernetics by Stafford Beer before.
These connections are explored by Nello Cristianini and Teresa Scantamburlo, where the reputation-credit scoring system is modeled as an incentive given to the citizens and computed by a social machine, so that rational agents would be motivated to increase their score by adapting their behaviour. Several ethical aspects of that technology are still being discussed.
China's Social Credit System was said to be a mass surveillance effort with a centralized numerical score for each citizen given for their actions, though newer reports say that this is a widespread misconception.
Smart contracts Smart contracts, cryptocurrencies, and decentralized autonomous organization are mentioned as means to replace traditional ways of governance. Cryptocurrencies are currencies which are enabled by algorithms without a governmental central bank. Central bank digital currency often employs similar technology, but is differentiated from the fact that it does use a central bank. It is being explored or implemented by governments and institutions, including the European Union and China. Smart contracts are self-executable contracts, whose objectives are the reduction of need in trusted governmental intermediators, arbitrations and enforcement costs. A decentralized autonomous organization is an organization represented by smart contracts that is transparent, controlled by shareholders and not influenced by a central government.. Smart contracts have been discussed for use in such applications as use in (temporary) employment contracts and automatic transfership of funds and property (i.e. inheritance, upon registration of a death certificate). Some countries such as Georgia and Sweden have already launched blockchain programs focusing on property (land titles and real estate ownership) Ukraine is also looking at other areas too such as state registers.
Algorithms in government agencies
According to a study of Stanford University, 45% of the studied US federal agencies have experimented with AI and related machine learning (ML) tools up to 2020. US federal agencies counted the number of artificial intelligence applications, which are listed below. 53% of these applications were produced by in-house experts. Commercial providers of residual applications include Palantir Technologies.
Agency name Number of use cases Office of Justice Programs 12 Securities and Exchange Commission 10 National Aeronautics and Space Administration 9 Food and Drug Administration 8 United States Geological Survey 8 United States Postal Service 8 Social Security Administration 7 United States Patent and Trademark Office 6 Bureau of Labor Statistics 5 U.S. Customs and Border Protection 4
In 2012, NOPD started a collaboration with Palantir Technologies in the field of predictive policing. Besides Palantir's Gotham software, other similar (numerical analysis software) used by police agencies (such as the NCRIC) include SAS.
In the fight against money laundering, FinCEN employs the FinCEN Artificial Intelligence System (FAIS) since 1995.