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Artificial Intelligence and Inequality

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Global Handbook of Inequality

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Abstract

In this chapter, I consider the way in which ‘artificial intelligence’ emerged and developed as a research field, a turning point being the mid-1950s. In its initial stages, the development of programs for chess-playing machines proved important because playing chess requires intelligent strategy and action. Early efforts led, more slowly than expected, to specialised artificial intelligence, although from only the 2010s one can speak of artificial general intelligence. The key technical dimensions, which have led to intelligent machines – namely algorithms, artificial neural networks and machine learning – are explicitly considered. After covering two fundamental aspects, those being the early development of ICTs and the overall supply chain necessary to keep the whole infrastructure of artificial intelligence going, the inequality issue is considered. Regarding the latter, even though it is too soon to have a reliable measure of the impact of artificial intelligence on the economy and society, a few dimensions are hinted at, such as the divide which is emerging in the labour market, the presence of gender and racial biases and the amplification of political inequality.

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Notes

  1. 1.

    In particular, for defence- and security-related issues, see CRS (2020).

  2. 2.

    Alas China, India and the Russian Federation – three important players in the field of artificial intelligence – do not belong to the OECD.

  3. 3.

    Some of the original words are outdated: nowadays nobody would use the adjective ‘automatic’ before ‘computer’, while ‘neuron nets’ would be ‘neural networks’.

  4. 4.

    A complementary, thorough, analysis can be found in Nilsson (2010).

  5. 5.

    Eratosthenes of Cyrene was a Greek mathematician and was born in 276 before Christ.

  6. 6.

    https://www.ibm.com/cloud/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks (site accessed 23 October 2022).

  7. 7.

    The next few paragraphs draw heavily from these works.

  8. 8.

    For a thorough review, see Schmidhuber (2015, 2021).

  9. 9.

    Checkers are also called draughts.

  10. 10.

    The range of phenomena which can be addressed through statistical learning is so broad that there is no point even in trying to draw a list, where this latter would range from consumers’ behaviour to speech recognition.

  11. 11.

    Superconducting devices are not improved semiconductors; they are based on a completely different technology.

  12. 12.

    This paragraph draws heavily from De Liso (2008), to which the reader could refer for a concise analysis of the establishment of a digital division of labour in our economies.

  13. 13.

    The classical reference for the early history of computing is Goldstine (1993); worthy of a comment is the fact that Goldstine participated in the construction of ENIAC.

  14. 14.

    As Crawford (2021, p. 33) reminds us, there are seventeen rare earth elements: cerium, dysprosium, erbium, europium, gadolinium, holmium, lanthanum, lutetium, neodymium, praseodymium, promethium, samarium, scandium, terbium, thulium, ytterbium and yttrium.

  15. 15.

    Wealth is broader than income, and it refers to the assets that people own minus their liabilities.

  16. 16.

    One cannot review all of these dimensions here, but the interested readers can find many loci in this handbook to satisfy their curiosity; incidentally, ‘spatial inequality’ refers to disparities which exist between centres and peripheries, urban and rural areas and between regions with more or less resources.

  17. 17.

    A few names and works worth remembering are: Tawney (1952), Williams (1962), Sen (1997), Rawls (1999), Therborn (2006), Atkinson (2015) and Gosepath (2021).

  18. 18.

    ‘Vertical integration’ refers to the process whereby a company buys another one which supplies – or is supplied by – it with good or services; the aim of the acquisition or of the merger lies in having control of more phases of the process of production. For example, a company assembling computers buys the company supplying transistors and microchips and/or the company supplying the screens, thus integrating production under one sole company.

  19. 19.

    https://www.economist.com/science-and-technology/2021/07/07/ai-is-transforming-the-coding-of-computer-programs [site accessed 10th November 2022].

  20. 20.

    Obviously more players, such as for instance China’s Alibaba, participate in the game; the main point, though, is that the birth of artificial intelligence as well as the key early steps towards artificial general intelligence took place in Western, rich, market-economy countries.

  21. 21.

    https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G [site accessed 11th November 2022].

  22. 22.

    See also Loideain and Adams (2020).

  23. 23.

    In 2019 the US Defense Department promoted the Joint Enterprise Defense Infrastructure (JEDI) initiative to create a war-fighter cloud; the initiative was postponed and redefined as Joint Warfighting Cloud Capability, among other things, because of allegations of Amazon being involved in designing the JEDI process (see The Economist, https://www.economist.com/business/2019/07/11/amazon-is-eyeing-billions-in-federal-contracts and https://www.economist.com/business/2022/08/08/can-tech-reshape-the-pentagon).

  24. 24.

    Article by Richard Waters in the Financial Times of 21st July 2022, available at https://www.ft.com/content/9e0185eb-66d9-4686-afe2-e7b1d12b4920 [site accessed 12th November 2022].

  25. 25.

    Incidentally, despite signs of restructuring of the high-tech sector – where ‘restructuring’ means thousands of redundancies as is happening in November 2022 in Meta (Facebook) and Twitter – these companies have already left a mark on the way in which artificial intelligence has evolved in the last few years. Should a few of them go bankrupt other giant companies will buy what is left, thus keeping the grip on the competence which will be always sought after, i.e. artificial intelligence.

  26. 26.

    https://www.nytimes.com/2018/04/04/us/politics/cambridge-analytica-scandal-fallout.html [site accessed 12th November 2022].

  27. 27.

    For a review see Westerlund (2019).

  28. 28.

    For a résumé of what happened one can read the article ‘Robert Mueller charges Russians with election interference’, in The Economist of 16th February 2018 available at https://www.economist.com/democracy-in-america/2018/02/16/robert-mueller-charges-russians-with-election-interference [site accessed 14th November 2022]

  29. 29.

    Edward Snowden is the whistle-blower who revealed how the US National Security Agency, or NSA, was destroying privacy, Internet freedom and basic liberties for people around the world with the massive surveillance machine the NSA was secretly building (these words are taken nearly verbatim from Snowden’s interview to The Guardian of 11 June 2013, available at: https://www.theguardian.com/world/2013/jun/09/edward-snowden-nsa-whistleblower-surveillance [site accessed 13th November 2022].

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Authors and Affiliations

  1. Department of Law – Economics Division, University of Salento, Lecce, Italy

    Nicola De Liso

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  1. Nicola De Liso
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Correspondence to Nicola De Liso .

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  1. Centre for the Study of Social Systems, Jawaharlal Nehru Univ, New Delhi, Delhi, India

    Surinder S. Jodhka

  2. Institute for Asian and African Studies, Humboldt University, Berlin, Berlin, Germany

    Boike Rehbein

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    Benjamin Baumann

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De Liso, N. (2023). Artificial Intelligence and Inequality. In: Jodhka, S.S., Rehbein, B. (eds) Global Handbook of Inequality. Springer, Cham. https://doi.org/10.1007/978-3-030-97417-6_49-1

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