Government by algorithm

Government by algorithm (also known as Algorithmic regulation, Regulation by algorithms, Algorithmic governance, Algorithmic legal order or Algocracy) is an alternative form of government or social ordering, where the usage of computer algorithms, especially of artificial intelligence and blockchain, is applied to regulations, law enforcement, and generally any aspect of everyday life such as transportation or land registration.[1][2][3][4][5][6][7] Alternatively, algorithmic regulation is defined as setting the standard, monitoring and modification of behaviour by means of computational algorithms — automation of judiciary is in its scope.[8]

Not to be confused with Regulation of algorithms, e-Government or Cyberocracy.

Government by algorithm raises new challenges that are not captured in the e-Government literature and the practice of public administration.[9] Some sources equate cyberocracy, which is a hypothetical form of government that rules by the effective use of information,[10][11][12] with algorithmic governance, although algorithms are not the only means of processing information.[13][14] Nello Cristianini and Teresa Scantamburlo argued that the combination of a human society and an algorithmic regulation forms a social machine.[15]

History

In 1962, head of the Department of technical physics in Kiev, Alexander Kharkevich, published an article in the journal "Communist" about a computer network for processing of information and control of economy.[16][17] In fact, he proposed to make a network like the modern Internet for the needs of algorithmic governance.

In 1971–1973, the Chilean government carried out the 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.[18][2]

Also in the 1960s and 1970s, Herbert A. Simon championed expert systems as tools for rationalization and evaluation of administrative behavior.[19] The automation of rule-based processes was an ambition of tax agencies over many decades resulting in varying success.[20] Early work from this period includes Thorne McCarty's influential TAXMAN project[21] in the US and Ronald Stamper's LEGOL project[22] in the UK. 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.[23] 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" [24]

Since 2000s, algorithms are designed and used to automatically analyze surveillance videos.[25]

In 2017, Justice Ministry of Ukraine run experimental auctions using blockchain technology to ensure transparency and hinder corruption in governmental transactions.[26]

Overview

Written laws are not replaced but stressed to test its efficiency. Algorithmic regulation is supposed to be a system of governance where more exact data collected from citizens via their smart devices and computers are used for more efficiency in organizing human life as a collective.[27][28] As Deloitte estimated in 2017, automation of US government work could save 96.7 million federal hours annually, with a potential savings of $3.3 billion; at the high end, this rises to 1.2 billion hours and potential annual savings of $41.1 billion.[29] 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.[4]

In 2013, algorithmic regulation was coined by Tim O'Reilly, Founder and CEO of O'Reilly Media Inc.:

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.[30]

A 2019 poll made by Center for the Governance of Change at IE University in Spain showed that 25% of citizens from selected European countries are somewhat or totally in favor of letting an artificial intelligence make important decisions about the running of their country.[31] Following table shows detailed results:

Country	Percentage
France	25%
Germany	31%
Ireland	29%
Italy	28%
Netherlands	43%
Portugal	19%
Spain	26%
UK	31%

Examples

Use of AI in government agencies

Main article: Artificial intelligence in government

US federal agencies counted the following numbers of artificial intelligence applications.[4]

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

53% of these applications were produced by in-house experts.[4] Commercial providers of residual applications include Palantir Technologies.[32] From 2012, NOPD started a secretive collaboration with Palantir Technologies in the field of predictive policing.[33] According to the words of James Carville, he was impetus of this project and "[n]o one in New Orleans even knows about this".[33]

AI politicians

See also: Chatbot § Politics

In 2018, an activist named Michihito Matsuda ran for mayor in the Tama city area of Tokyo as a human proxy for an artificial intelligence program.[34] While election posters and campaign material used the term 'robot', and displayed stock images of a feminine android, the 'AI mayor' was in fact a machine learning algorithm trained using Tama city datasets.[35] The project was backed by high-profile executives Tetsuzo Matsumoto of Softbank and Norio Murakami of Google.[36] Michihito Matsuda came third in the election, being defeated by Hiroyuki Abe.[37] Organisers claimed that the 'AI mayor' was programmed to analyze citizen petitions put forward to the city council in a more 'fair and balanced' way than human politicians.[38]

In 2019, AI-powered messenger chatbot SAM participated in the discussions on social media connected to electoral race in New Zealand.[39] The creator of SAM, Nick Gerritsen, believes SAM will be advanced enough to run as a candidate by late 2020, when New Zealand has its next general election.[40]

AI judges

See also: Lawbot

According to the statement of Beijing Internet Court, China is the first country to create an internet court or cyber court.[41][42][43] Chinese AI judge is a virtual recreation of an actual female judge. She "will help the court's judges complete repetitive basic work, including litigation reception, thus enabling professional practitioners to focus better on their trial work".[41]

Also Estonia plans to employ artificial intelligence to decide small-claim cases of less than €7,000.[44]

COMPAS software is used in USA to assess the risk of recidivism in courts.[45][46]

Reputation systems

See also: Credit score

Tim O'Reilly suggested that data sources and reputation systems combined in algorithmic regulation can outperform traditional regulations.[30] 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".[30] O'Reilly's suggestion is based on control-theoreric concept of feed-back loop — improvements and disimprovements of reputation enforce desired behavior.[15] The usage of feed-loops for the management of social systems is already been suggested in management cybernetics by Stafford Beer before.[47]

The Chinese Social Credit System is closely related to China's mass surveillance systems such as the Skynet,[48][49][50] which incorporates facial recognition system, big data analysis technology and AI.[51][52][53][54] This system provides assessments of trustworthiness of individuals and businesses.[55][56][57] Among behavior, which is considered as misconduct by the system, jaywalking and failing to correctly sort personal waste are cited.[58][59][60][61][62] Behavior listed as positive factors of credit ratings includes donating blood, donating to charity, volunteering for community services, and so on.[63][64] Chinese Social Credit System enables punishments of "untrustworthy" citizens like denying purchase of tickets and rewards for "trustworthy" citizen like less waiting time in hospitals and government agencies.[65][66][67]

Management of infection

See also: Contact tracing

See also: Disease surveillance

See also: Predictive analytics

In February 2020, China launched a mobile app to deal with Coronavirus outbreak.[68] Users are asked to enter their name and ID number. The app is able to detect 'close contact' using surveillance data and therefore a potential risk of infection. Every user can also check the status of three other users. If a potential risk is detected, the app not only recommends self-quarantine, it also alerts local health officials.[69]

Cellphone data is used to locate infected patients in South Korea, Taiwan, Singapore and other countries.[70][71] In March 2020, the Israeli government enabled security agencies to track mobile phone data of people supposed to have coronavirus. The measure was taken to enforce quarantine and protect those who may come into contact with infected citizens.[72] Also in March 2020, Deutsche Telekom shared private cellphone data with the federal government agency, Robert Koch Institute, in order to research and prevent the spread of the virus.[73] Russia deployed facial recognition technology to detect quarantine breakers.[74] Italian regional health commissioner Giulio Gallera said that "40% of people are continuing to move around anyway", as he has been informed by mobile phone operators.[75] In USA, Europe and UK, Palantir Technologies is taken in charge to provide COVID-19 tracking services.[76]

Prevention of environmental disasters

See also: Predictive analytics

Tsunami's can be detected by Tsunami warning systems. They can make use of AI.[77][78] Locust breeding areas can be approximated using machine learning, which could help to stop locust swarms in an early phase.[79]

Blockchain

Cryptocurrencies, Smart Contracts and Decentralized Autonomous Organization are mentioned as means to replace traditional ways of governance.[80][81][7] Cryptocurrencies are currencies, which are enabled by algorithms without a governmental central bank.[82] Smart contracts are self-executable contracts, whose objectives are the reduction of need in trusted governmental intermediators, arbitrations and enforcement costs.[83][84] A decentralized autonomous organization is an organization represented by smart contracts that is transparent, controlled by shareholders and not influenced by a central government.[85][86][87]

Criticism

The are potential risks associated with the use of algorithms in government. Those include algorithms becoming susceptible to bias,[88] a lack of transparency in how an algorithm may make decisions,[89] and the accountability for any such decisions.[89] There is also a serious concern that gaming by the regulated parties might occur, once more transparency is brought into the decision making by algorithmic governance, regulated parties might try to manipulate their outcome in own favor and even use adversarial machine learning.[4][15] According to Harari, the conflict between democracy and dictatorship is seen as a conflict of two different data-processing systems — AI and algorithms may swing the advantage toward the latter by processing enormous amounts of information centrally.[90] Also, the contributors in the 2019's documentary iHuman express apprehension of "infinitely stable dictatorships" being created by governmental use of AI.[91]

Regulation of algorithmic governance

See also: Regulation of algorithms

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.[92] This caused a public protest. The district court of Hague shut down SyRI referencing Article 8 of the European Convention on Human Rights (ECHR).[93]

In the USA, multiple states implement predictive analytics as part of their child protection system. Illinois and Los Angeles shut these algorithms down due to a high rate of false positives.[94]

In popular culture

The novels Daemon and Freedom™ by Daniel Suarez describe a fictional scenario of global algorithmic regulation.[95]

See also

• Artificial intelligence in government
• Code for America
• Cyberpunk
• Health information technology
• Internet of things
• Lawbot
• Legal informatics
• Management cybernetics
• Multivac
• Predictive analytics
• Sharing economy
• Smart contract
• Smart city
• Social Credit System
• Technoutopianism

External links

• Government by Algorithm? by Data for Policy 2017 Conference
• Government by Algorithm by Stanford University
• A governance framework for algorithmic accountability and transparency by European parlament
• Algorithmic Government by Zeynep Engin and Philip Treleaven, University College London
• Algorithmic Government by Prof. Philip C. Treleaven of University College London
• Artificial Intelligence for Citizen Services and Government by Hila Mehr of Harvard University
• Yeung, Karen; Lodge, Martin (2019). Algorithmic Regulation. Oxford University Press. ISBN 9780198838494.
• Code: Version 2.0 (Basic Books, 2006) ISBN 978-0-465-03914-2
• iHuman (Documentary, 2019) by Tonje Hessen Schei

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Zeynep Engin, Philip Treleaven, Algorithmic Government: Automating Public Services and Supporting Civil Servants in using Data Science Technologies, The Computer Journal, Volume 62, Issue 3, March 2019, Pages 448–460, https://doi.org/10.1093/comjnl/bxy082