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Berk (2008) notes that researchers have applied statistical methods on crime for nearly a hundred years.
According to USA TODAY (Baig 2019), after the Parkland shooting, three US companies (Bark Technologies, Gaggle.Net, and Securly Inc.) claim that their AI systems can detect possible signs of cyber bullying and violence by scanning student emails, texts, documents, and social media activity.
In 2016, for example, the UK’s National Police Chiefs’ Council (NPCC), Association of Police and Crime Commissioners (APCC), and National Crime Agency released “The Policing Vision 2025” programme, setting out a ten-year plan to help the law enforcement transform and adapt to the modern policing environment. The aim is to employ innovative and transformative approaches for proactive and preventative policing. As was planned, a new national super-database, called the “National Law Enforcement Data Programme” (NLEDP), will be put in place to replace the existing separate systems by 2020.
In the literature of dual use, dual use technologies refer to tools that can be used to achieve good or evil. AI seems to be dual use as by which malevolent individuals can perpetrate wrongful harms. However, AI is unlike guns and H-bombs in that it is not designed to harm. So, in what sense that AI is a dual use technology is an interesting question. Please see Miller (2018) for the analysis of the concept of dual use.
Assigning meaning is crucial in computer science (e.g., mapping symbols onto actions). A Turing machine can initially be viewed as manipulating otherwise meaningless marks, which become symbols when they are linked with rules as to bear assignment of reference and conform to the rules of syntax. This happens, for example, when the marks are taken as 0s and 1s and construed as numerals and hence as symbols standing for binary numbers, and the same is true for standard construals of machine code. It is also standard practice to add further layers of symbols and representation by building up these up out of binary code. Thus we have higher levels of representations, which can be assigned different kinds of reference, subjected to further kinds of syntax. However, even if we can construct the meaning of individual computational procedure, it may be hard for us to analyze the meaning of billions of procedures in AI.
The human brain is a two-way blackbox. On one hand, psychological behaviourists, holding that the mental states are hard to measure, suggest studying observable outer behaviours instead. On the other hand, Bayesian theorists of predictive coding argue that the brain only measures the sensory signal without directly measuring the external world (Swanson 2016). This creates a problem: how the brain only infers its “cause” in the external world based on the “effect” of the sensory signals. This puzzle is described as “view from inside the blackbox” (Clark 2013, p. 183) or “the skull-bound brain” (Hohwy 2013, p. 15).
In addition to biases, there is also the undecidable problem. It has been proven to be impossible to construct an algorithm that can provide correct answers to all yes-or-no questions (Floridi 2016). For example, Kleene (1943) applies Gödel’s incompleteness theorem to computation, and he shows that no effective system can correctly determine whether a program, if run with a given input, will finish running or continue to run (known as the halting problem). Therefore, biases and errors are somewhat inevitable (Lin et al. forthcoming).
Interestingly, despite adopting a total social credit system, China also announced the Beijing AI Principles (2019, May) through the Beijing Academy of Artificial Intelligence (https://www.baai.ac.cn/blog/beijing-ai-principles)—an organization backed by the Chinese Ministry of Science and Technology and the Beijing municipal government.
According to Ferguson (2017b), before the Kansas City Police Department introduced advanced social network analysis to spot at-risk suspects in 2012, Kansas City’s homicide rate was two-to-four times the national rate. Although the number fell 26.5% after the new technology was employed, homicide and shooting rates dramatically climbed again in 2015. Likewise, in 2013, the Chicago police adopted different algorithms for focused deterrence, which located potential offenders based on their personal criminal record. At the beginning, the software generated numerous false-positive predictions, but its accuracy was significantly improved in 2016 (more than 70% shot people were on the list). However, this by no means implies the ending of violence because the technology only “identifies the disease but offers no cure” (Ferguson 2017b, p. 49). See also Saunders et al. (2016) for similar concerns.
For example, the Chicago Police Department has used an algorithm to prioritize limited resources to focus on those at highest risk by rating every person arrested with a threat score from 1 to 500-plus. Due to the lack of specific guidance on what treatments to apply to the subjects on the list, however, most districts did not focus on intervening with these subjects (Saunders et al. 2016). Careful research shows that the list does not reduce homicides (Saunders et al. 2016). See also Ferguson (2017b, p. 40); Perry et al. (2013); Couchman (2019).
The New Orleans Police Department has applied similar techniques to those employed by the Chicago Police Department since 2012. For a more integrated approach using predictive technologies to reduce crime, the city also supplemented the Group Violence Reduction Strategy as part of their broader NOLA for Life murder reduction strategy.
As Ferguson observes, “[b]ig data collection will not count those whom it cannot see” (2017b, p. 179). Big-data-driven systems will overlook the populations who do not “engage in activities that big data and advanced analytics are designed to capture” (Lerman 2013, p. 56). In our case, those with criminal records or gang associations, as well as prior police contact, are most likely to be marked as suspicious. This creates the concern about “the initial selection bias” (Ferguson 2015, p. 402) of law enforcement data-collection systems that certain individuals will always be at risk to be future targets of suspicion, despite that they are not currently engaging in criminal activities. The danger is straightforward. The databases with “the initial selection bias” will make it easier for a police officer to justify her suspicion if she tends to believe that a particular type of person may be more likely to commit a crime (Saunders et al. 2016; Richardson et al. 2019).
Also, according to Miller and Blackler’s (2017) normative theory of policing, the protection of moral rights is the principal purpose of policing, constrained by democratically supported laws. The purpose in protecting these rights justifies policing. So we can, for example, claim that the police officers are justified to arrest and detain someone for assault. They possess the moral right to do so in virtue of their membership of a morally legitimate police institution. Police officers are individually institutionally responsible for at least some of their actions and omissions regarding the purpose of protecting moral rights.
When used properly, the technologies may benefit law enforcement with increased accuracy. As Ferguson (2015) points out, big data enables not only a wealth of suspicious inferences, but also an equal number of potentially exculpatory facts. When big data is available, police should be required to use it in an exculpatory manner as well. It offers to search for more information and more precise information, including exculpatory information that reduces suspicion, and thus can make more reliable predictions than human investigators. It allows for a more focused use of police resources as well. Moreover, with a vast amount of information, the big data technologies allow collecting unexpected seemingly innocuous connections and correlations for future criminal activities. Take one of Ferguson’s examples (2015, pp. 395–396), a drug dealer needs tiny plastic bags and a scale to package crack cocaine. It is considered that recent innovations can help to track the sale of these items and thus to help spot the drug dealer. Similarly, big data is useful to reveal patterns of national or transnational crimes which were difficult to track before.
China is also exporting its surveillance tech to the global. See Mozur et al. (2019).
A similar debate is currently ongoing in the UK, concerning the Metropolitan Police and the Home Secretary’s trials of the facial recognition surveillance technology since 2016. According to a final report conducted by the London Policing Ethics Panel, an independent panel set up by the Mayor of London to provide ethical advice on policing issues that may impact on public confidence, ‘[m]arginal benefit would not be sufficient to justify [life facial recognition’s] adoption in the face of the unease that it engenders in some, and hence the potential damage to policing by consent’ (London Policing Ethics Panel 2019, p. 47). The panel suggests that the facial recognition surveillance technology should not be adopted unless it could be shown from the field trials that it could be able to significantly increase police efficiency and effectiveness in dealing with serious offences. Currently, human rights organisations Liberty and Big Brother Watch are challenging the use of FR cameras in the courts.
Restrictions are placed on the rights of the data subjects, where necessary and proportionate, in order to avoid obstructing an investigation or inquiry, avoid prejudicing the prevention, detection, investigation or prosecution of criminal offences or the execution of criminal penalties, protect public security, protect national security, and protect the rights and freedoms of others. See the Guide to Law Enforcement Processing of DPA on the Information Commissioner’s Office website (https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-law-enforcement-processing/individual-rights/the-right-of-access/).
As our solution is not necessarily involving police intervention, it raises a question of whether the term “predictive policing” should be substituted or integrated into a larger framework of humanity security.
The databases are the Police National Computer (PNC) and the Police National Database (PND). For further details, please refer to the UK government’s the NLEDP Privacy Impact Assessment Report (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/721542/NLEDP_Privacy_Impact_Assessment_Report.pdf).
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Hung, TW., Yen, CP. On the person-based predictive policing of AI. Ethics Inf Technol (2020). https://doi.org/10.1007/s10676-020-09539-x