Government by algorithm

Government by algorithm (also known as algorithmic regulation, regulation by algorithms, algorithmic governance, algocratic 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][8] The term 'government by algorithm' appeared in academic literature as an alternative for 'algorithmic governance' in 2013.[9] A related term, 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.[10]

Not to be confused with Regulation of algorithms, E-government or Cyberocracy.
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Government by algorithm raises new challenges that are not captured in the e-government literature and the practice of public administration.[11] Some sources equate cyberocracy, which is a hypothetical form of government that rules by the effective use of information,[12][13][14] with algorithmic governance, although algorithms are not the only means of processing information.[15][16] Nello Cristianini and Teresa Scantamburlo argued that the combination of a human society and an algorithmic regulation forms a social machine.[17]

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 information and control of the economy.[18][19] In fact, he proposed to make a network like the modern Internet for the needs of algorithmic governance.

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.[20][2]

Also in the 1960s and 1970s, Herbert A. Simon championed expert systems as tools for rationalization and evaluation of administrative behavior.[21] The automation of rule-based processes was an ambition of tax agencies over many decades resulting in varying success.[22] Early work from this period includes Thorne McCarty's influential TAXMAN project[23] in the US and Ronald Stamper's LEGOL project[24] 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.[25] 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.[26] 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"[27]

Since the 2000s, algorithms have been designed and used to automatically analyze surveillance videos.[28]

Sociologist A. Aneesh used the idea of algorithmic governance in 2002 in his theory of algocracy.[29][30][31] Aneesh differentiated algocratic systems from bureaucratic systems (legal-rational regulation) as well as market-based systems (price-based regulation).[32]

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.[33]

In 2017, Justice Ministry of Ukraine ran experimental government auctions using blockchain technology to ensure transparency and hinder corruption in governmental transactions.[34] "Government by Algorithm?" was the central theme introduced at Data for Policy 2017 conference held in London on 6-7 September 2017 in London, UK.[35]

Benefits
See also: Techno-progressivism

Algorithmic regulation is supposed to be a system of governance where more exact data, collected from citizens via their smart devices and computers, is used to more efficiently organize human life as a collective.[36][37] 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.[38]

Current use

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.[5]

Public opinion

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.[39] Following table shows detailed results:

CountryPercentage
France25%
Germany31%
Ireland29%
Italy28%
Netherlands43%
Portugal19%
Spain26%
UK31%

Examples

Smart cities
Further information: Smart city § Technologies

A smart city is an urban area, where collected surveillance data is used to improve various operations in this area. Increase in computational power allows more automated decision making and replacement of public agencies by algorithmic governance.[40]

Reputation systems
See also: Credit score

Tim O'Reilly suggested that data sources and reputation systems combined in algorithmic regulation can outperform traditional regulations.[33] 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".[33] O'Reilly's suggestion is based on control-theoreric concept of feed-back loop—improvements and disimprovements of reputation enforce desired behavior.[17] The usage of feed-loops for the management of social systems is already been suggested in management cybernetics by Stafford Beer before.[41]

The Chinese Social Credit System is closely related to China's mass surveillance systems such as the Skynet,[42][43][44] which incorporates facial recognition system, big data analysis technology and AI.[45][46][47][48] This system provides assessments of trustworthiness of individuals and businesses.[49][50][51] Among behavior, which is considered as misconduct by the system, jaywalking and failing to correctly sort personal waste are cited.[52][53][54][55][56] Behavior listed as positive factors of credit ratings includes donating blood, donating to charity, volunteering for community services, and so on.[57][58] 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.[59][60][61]

Smart contracts

Cryptocurrencies, Smart Contracts and Decentralized Autonomous Organization are mentioned as means to replace traditional ways of governance.[62][63][8] Cryptocurrencies are currencies, which are enabled by algorithms without a governmental central bank.[64] Smart contracts are self-executable contracts, whose objectives are the reduction of need in trusted governmental intermediators, arbitrations and enforcement costs.[65][66] A decentralized autonomous organization is an organization represented by smart contracts that is transparent, controlled by shareholders and not influenced by a central government.[67][68][69]

Algorithms in government agencies
See also: Artificial intelligence in government

US federal agencies counted the following number of artificial intelligence applications.[5]

Agency NameNumber of Use Cases
Office of Justice Programs12
Securities and Exchange Commission10
National Aeronautics and Space Administration9
Food and Drug Administration8
United States Geological Survey8
United States Postal Service8
Social Security Administration7
United States Patent and Trademark Office6
Bureau of Labor Statistics5
U.S. Customs and Border Protection4

53% of these applications were produced by in-house experts.[5] Commercial providers of residual applications include Palantir Technologies.[70]

From 2012, NOPD started a collaboration with Palantir Technologies in the field of predictive policing.[71] Besides Palantir's Gotham software, other similar (numerical analysis software) used by police agencies (such as the NCRIC) include SAS.[72]

Some urban areas also use gunfire locators, which are systems that detects and conveys the location of gunfire or other weapon fire using acoustic, optical, or other types of sensors.

Many governments employ automated fingerprint identification systems. They employ fingerprint-matching algorithms.

In the fight against money laundering, FinCEN employs the FinCEN Artificial Intelligence System (FAIS).[73]

National health administration entities and organisations such as AHIMA (American Health Information Management Association) hold medical records. Medical records serve as the central repository for planning patient care and documenting communication among patient and health care provider and professionals contributing to the patient's care. In the EU, work is ongoing on a European Health Data Space which supports the use of health data.[74]

In Estonia, artificial intelligence is used in its e-government to make it more automated and seamless. A virtual assistant will guide citizens through any interactions they have with the government. Automated and proactive services "push" services to citizens at key events of their lives (including births, bereavements, unemployment, ...). One example is the automated registering of babies when they are born. Estonia's X-Road system will also be rebuilt to include even more privacy control and accountability into the way the government uses citizen's data.[75]

In Costa Rica, the possible digitalisation of public procurement activities (i.e. tenders for public works, ...) has been investigated. The paper discussing this possibility mentions that the use of ICT in procurement has several benefits such as increasing transparency, facilitating digital access to public tenders, reducing direct interaction between procurement officials and companies at moments of high integrity risk, increasing outreach and competition, and easier detection of irregularities.[76]

Tenders are also used for obtaining emission reductions (Low-carbon tenders).[77][78]

Besides using e-tenders for regular public works (construction of buildings, roads, ...), e-tenders can also be used for reforestation projects and other carbon sink restoration projects.[79] Carbon sink restoration projects may be part of the nationally determined contributions plans in order to reach the national Paris agreement goals

Some government agencies provide track and trace systems for services they offer. An example is track and trace for applications done by citizens (i.e. driving license procurement).[80]

Some government services use issue tracking system to keep track of ongoing issues[81][82][83][84]Issue tracking systems may show all tasks still to be done by the government (in a waiting queue), finished tasks, tasks in progress, order sequence, ... Finished tasks can also be foreseen with the report, showing what exactly has been done on the issue.

AI for Justice
See also: Lawbot

COMPAS software is used in USA to assess the risk of recidivism in courts.[85][86]

According to the statement of Beijing Internet Court, China is the first country to create an internet court or cyber court.[87][88][89] The 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".[87]

Also Estonia plans to employ artificial intelligence to decide small-claim cases of less than €7,000.[90]

Robot lawyers can perform tasks that are typically done by paralegals or young associates at law firms. One notable legal AI solution for the law firms is ROSS, which has been used by US law firms to assist in legal research,[91] but there are already hundreds of legal AI solutions that operate in multitude of ways varying in sophistication and dependence on scripted algorithms.[92] Another notable legal technology chatbot application is DoNotPay.

AI in education
Further information: Ofqual exam results algorithm

Due to the COVID-19 pandemic in 2020, in-person final exams were impossible for thousands of students.[93] The public high school Westminster High imployed algorithms to assign grades. UK's Department for Education also employed a statistical calculus to assign final grades in A-levels, due to the pandemic.[94]

Besides use in grading, software systems and AI are also optimizing coursework and are used in preparation for college entrance exams.[95]

AI teaching assistants are being developed and used for education (i.e. Georgia Tech's Jill Watson)[96][97] and there is also an ongoing debate on whether perhaps teachers can be entirely replaced by AI systems (i.e. in homeschooling).[98]

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.[99] 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.[100] The project was backed by high-profile executives Tetsuzo Matsumoto of Softbank and Norio Murakami of Google.[101] Michihito Matsuda came third in the election, being defeated by Hiroyuki Abe.[102] 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.[103]

In 2019, AI-powered messenger chatbot SAM participated in the discussions on social media connected to an electoral race in New Zealand.[104] 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.[105]

Management of infection
See also: Digital contact tracing, Disease surveillance, and COVID-19 apps

In February 2020, China launched a mobile app to deal with the Coronavirus outbreak[106] called "close-contact-detector".[107] Users are asked to enter their name and ID number. The app is able to detect "close contact" using surveillance data (i.e. using public transport records, including trains and flights)[107] and therefore a potential risk of infection. Every user can also check the status of three other users. To make this inquiry users scan a Quick Response (QR) code on their smartphones using apps like Alipay or WeChat.[108] The close contact detector can be accessed via popular mobile apps including Alipay. If a potential risk is detected, the app not only recommends self-quarantine, it also alerts local health officials.[109]

Alipay also has the Alipay Health Code which is used to keep citizens safe. This system generates a QR code in one of three colors (green, yellow, or red) after users fill in a form on Alipay with personal details. A green code enables the holder to move around unrestricted. A yellow code requires the user to stay at home for seven days and red means a two-week quarantine. In some cities such as Hangzhou, it has become nearly impossible to get around without showing your Alipay code.[110]

In Cannes, France, monitoring software has been used on footage shot by CCTV cameras, allowing to monitor their compliance to local social distancing and mask wearing during the COVID-19 pandemic. The system does not store identifying data, but rather allows to alert city authorities and police where breaches of the mask and mask wearing rules are spotted (allowing fining to be carried out where needed). The algorithms used by the monitoring software can be incorporated into existing surveillance systems in public spaces (hospitals, stations, airports, shopping centres, ...) [111]

Cellphone data is used to locate infected patients in South Korea, Taiwan, Singapore and other countries.[112][113] 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.[114] 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.[115] Russia deployed facial recognition technology to detect quarantine breakers.[116] 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.[117] In USA, Europe and UK, Palantir Technologies is taken in charge to provide COVID-19 tracking services.[118]

Prevention and management of environmental disasters
See also: Early warning system

Tsunamis can be detected by Tsunami warning systems. They can make use of AI.[119][120] Floodings can also be detected using AI systems.[121] Locust breeding areas can be approximated using machine learning, which could help to stop locust swarms in an early phase.[122] Wildfires can be predicted using AI systems.[123][124] Also, wildfire detection is possible by AI systems (i.e. through satellite data, aerial imagery, and personnel position).[125][126][127] and they can also help in evacuation of people during wildfires.[128]

Criticism

There are potential risks associated with the use of algorithms in government. Those include algorithms becoming susceptible to bias,[129] a lack of transparency in how an algorithm may make decisions,[130] and the accountability for any such decisions.[130]

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.[5][17] 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.[131]

The contributors of the 2019 documentary iHuman expressed apprehension of "infinitely stable dictatorships" created by government AI.[132]

Algorithmic bias and transparency
Main article: Algorithmic bias § Solutions

An initial approach towards transparency included the open-sourcing of algorithms.[133] Software code can be looked into and improvements can be proposed through source-code-hosting facilities.

The novels Daemon and Freedom™ by Daniel Suarez describe a fictional scenario of global algorithmic regulation.[134]

See also

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Bibliography

Code: Version 2.0 (Basic Books, 2006) ISBN 978-0-465-03914-2

Yeung, Karen; Lodge, Martin (2019). Algorithmic Regulation. Oxford University Press. ISBN 9780198838494.

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