Abstract
The chapter presents the outline of the book, justifies the purposes of it and sets the boundaries of this enquiry. It frames the work into the methodological limits of a theoretical reconstruction of the topic.
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Notes
- 1.
- 2.
See Julia Black’s perspective on decentred regulation, in which law is one out of many different systems to influence social behaviour: Black (2002), p. 4.
- 3.
- 4.
Pagallo (2018), p. 1 ff.
- 5.
- 6.
Information and Communication Technologies have been seen and presented, at least initially, as a means for facilitating anti-social criminal activities: see Thomas and Loader (2000), p. 1.
- 7.
See the Council of Europe Budapest Convention, 23.11.2001, at https://www.coe.int/en/web/conventions/full-list/-/conventions/treaty/185/signatures.
- 8.
Although the Council of Europe started drawing attention on this topic in the Eighties, with the Recommendations of the Committee of Ministers R(89)9, on computer-related crimes and R(95)13 concerning problems of criminal procedural law connected with information technology. In 1996 a committee was established to draft the text of a convention about cybercrime, that than was signed in Budapest, in 2001.
- 9.
For an overview of the impact of the Convention see Koops and Brenner (2006).
- 10.
See, among the first attempts to define the concept, Sieber (1977). Thomas and Loader (2000), p. 3. Although both research and legislation evolved significantly, it is still impossible to give one, unique and undisputed definition of cybercrime. “Cybercrime is a container term of convenience, describing a collection of acts or a field of criminal activity, rather than a single concept”: Boister (2018), p. 188. Moreover, many other similar terms are often used, such as ‘computer crime’,’ IT crime’… It has been said that the concept encompasses a whole range of terms which imply that the digital technology (not only computers) is an element of the offence. In this sense, internet connections are necessary elements, either for crimes against digital technologies and crime committed by means of digital technologies: Sieber (2008), p. 127.
- 11.
Pagallo and Quattrocolo (2018), p. 400.
- 12.
See the seminal work of Susskind (2008).
- 13.
de Vries (2013), all Ch. 1.
- 14.
Data mining and predictive analytics have origins in commerce: Pasquale (2015), p. 4: “At the core of information economy are Internet and the finance companies that accumulate vast amounts of digital data, and with it intimate details of their customers’ – our -lives”.
- 15.
Katz (2013), p. 916.
- 16.
For the distinction between code-driven and data-driven regulation, see Hildebrandt (2018), pp. 2, 3.
- 17.
Evans (2019), p. 260: “the modern debate between deep-learning practitioners and advocates of logic-based approaches resembles the Eighteenth century debate between empiricists and rationalists”.
- 18.
It was 1967 when P. Souleau, a high rank French magistrate, dreamt of a joint venture between cyberneticists and judiciary to create models reproducing the most basic and repetitive parts of the judicial adjudication, in particular vehicles accidents (Souleau 1969, p. 70).
- 19.
Solum (1992).
- 20.
This attitude has been recently studied under the sociological point of view: see Christin (2017), p. 2 ff. Based on his empirical research in American local criminal courts, the author concludes that “in criminal justice innovation does not come with the glitter and appeal that it has in other sectors: it is often a source of uncertainty, as innovation arrives without the vetting of precedent”.
- 21.
See, e.g., Breton-Darrien (2018), p. 19, suggesting that the stakeholders in the area of legal-tech accept not to mine and reuse data from criminal decisions.
- 22.
JRC Technical Reports, AI Watch. Defining Artificial Intelligence, Publications Office of the European Union, Luxembourg, 2020, 11.
- 23.
“Artificial intelligence (AI) systems are software (and possibly also hardware) systems designed by humans(2) that, given a complex goal, act in the physical or digital dimension by perceiving their environment through data acquisition, interpreting the collected structured or unstructured data, reasoning on the knowledge, or processing the information, derived from this data and deciding the best action(s) to take to achieve the given goal. AI systems can either use symbolic rules or learn a numeric model, and they can also adapt their behaviour by analysing how the environment is affected by their previous actions.”
- 24.
Craglia (2018).
- 25.
Turing (1950), especially § 8.
- 26.
Beller (2018), p. 45.
- 27.
For a general overview in a non-mathematics context, see the Council of Europe study about Algorithms and Human Rights, Council of Europe Publication, Strasbourg, 2018.
- 28.
Gillespie (2014), p. 167.
- 29.
Diakopoulos (2015), p. 400.
- 30.
Durante (2019), p. 9 ff.
- 31.
Nieva Fenoll (2018), p. 33 ff. Although many of the decisions deemed potentially automated are placed in the realm of civil procedure, the Author points out some small areas of criminal proceeding that could be automated.
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Quattrocolo, S. (2020). Approaching the Unknown: Some Preliminary Words. In: Artificial Intelligence, Computational Modelling and Criminal Proceedings. Legal Studies in International, European and Comparative Criminal Law, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-030-52470-8_1
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