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2024 | OriginalPaper | Buchkapitel

16. Redefining a Normative Framework for Meritocracy in the Era of Generative AI: An Inter-Disciplinary Perspective

verfasst von : Kalpana Tyagi

Erschienen in: Law and Economics of Justice

Verlag: Springer Nature Switzerland

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Abstract

Meritocracy is the hallmark of our modern day societies. It is widely believed, and also empirically established, that meritocratic systems outshine non-meritocratic ones. Meritocratic individuals, on average, tend to perform better. Meritocracy takes subjective elements, such as chance, and luck out of the equation, and seeks to reward those with intelligence, and fortitude. From the Silicon Valley in the US, Singapore’s political and economic growth, to the rise, and the sustenance of the big pharma in Switzerland, meritocracy has consistently nourished these economies to catapult them to the world’s leading economies, and innovation systems. The meteoric rise of Singapore from a resource-deprived and newly independent country (in 1959) to the world’s fifth richest Nation State (in 2022) may be attributed in part to its unique, meritocratic political system. What then is this meritocracy? Is there one standard understanding of meritocracy, or does it vary by the State, the context, or is it simply a narrative of the times that we live in? Is there a correlation between meritocracy and inequality? This research article discusses the nature of meritocracy, and how the fallacies of meritocratic societies be remedied to ensure a balance between meritocracy, egalitarianism, and justice. The article discusses the foregoing issues against the backdrop of the fast-rising Generative Artificial Intelligence, that just like meritocracy, contributes to growth, while simultaneously exacerbating the societal inequalities (and its accompanying discontent). From a normative perspective, this research suggests a Scandinavian approach to balancing wealth creation with a more positive and detailed welfare-state institution that will not only foster increased social trust; it will also ensure that those with “merit” are able to exercise it with “empathy” in the age of Generative AI.

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Fußnoten
1
Young (1958).
 
2
Markovits (2020).
 
3
Nelson (2019).
 
4
Markovits (2020).
 
5
Tyagi (2023).
 
6
Young (1958).
 
7
Markovits (2020).
 
8
Gordon (1993), p. 1558.
 
9
Nelson (2019).
 
10
Morriw, Bühlmann, Sommet, Vendecasteele (2022).
 
11
Markovits (2020), p. 40.
 
12
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13
Petersen (2020).
 
14
Lee (2012).
 
15
Noble, Roberts (2019), p. 6.
 
16
Noble, Roberts (2019), p. 15.
 
17
Bellows (2009) pp. 24–25.
 
18
See reference to Krauze and Slomczynski, Green and Pojman in Thomas J. Bellows (2009), p. 26.
 
19
Bellows (2009) p. 32.
 
20
Bellows (2009, p. 37.
 
21
Bellows (2009), pp. 37–38.
 
22
International Monetary Fund (2023).
 
23
KOF Swiss Economic Institute (2 December 2022).
 
24
KOF Swiss Economic Institute (2 December 2022).
 
25
Chemical and pharmaceutical industry (2023); Flow Bank Insight on LinkedIn (6 November 2023).
Please note that the number of people employed varies as some sources count only those employed in the pharmaceuticals sector (47,010 people); whereas others count chemicals and pharmaceuticals industry collectively (74,000 people). The expressions pharmaceuticals, and life sciences, for the purposes of this research, shall, accordingly be used interchangeably.
 
26
BAK Economics (2020), p. 8.
 
27
BAK Economics (2020), p. 43.
 
28
Blok (28 October 2022).
 
29
An average of 48 patents (per million inhabitants) were registered in Switzerland between 2014–2018. Source: Blok (28 October 2022).
 
30
BAK Economics (2020), pp. 9–10.
 
31
Fischer (2011).
 
32
Gardner (2023).
 
33
For a detailed discussion on the economics of the digital multi-sided platform economy, and how it has led the global market to concentrate in the hands of a few digital players (and what role therein for merger control), see Tyagi (2019).
 
34
Molla, Del Rey (2017); Perez JC (28 January 2004).
 
35
Schumpeter (1942).
 
36
Stigler Committee on Digital Platforms (2019), p. 9.
 
37
Shields (2018).
 
38
Tyagi (2023).
 
39
Hervey (2023).
 
40
Hervey (2023).
 
41
Hervey (2023).
 
42
Trapova/Mezei (2022).
 
43
Trapova/Mezei (2022).
 
44
Authors Guild v. Open AI (Complaint filed on 19 September 2023) No. 1:23-cv-8292, at paras 134–143.
 
45
Authors Guild v. Open AI (Complaint filed on 19 September 2023) No. 1:23-cv-8292, at paras 134–143.
 
46
Authors Guild v. Open AI (Complaint filed on 19 September 2023) No. 1:23-cv-8292, at paras 144–309.
 
47
Authors Guild v. Open AI (Complaint filed on 19 September 2023) No. 1:23-cv-8292, at para 199.
 
48
Authors Guild v. Open AI (Complaint filed on 19 September 2023) No. 1:23-cv-8292, at para 198–199.
 
49
Authors Guild v. Open AI (Complaint filed on 19 September 2023) No. 1:23-cv-8292, at para 339.
 
50
OECD Digital Economy Papers (2023) AI Language Models: Technological, Socio-Economic and Policy Considerations, p. 25.
 
51
Trendacosta, Doctorow (2023).
 
52
Morriw, Bühlmann, Sommet, Vendecasteele (2022).
 
53
Sato (2023).
 
54
Sato (2023).
 
55
Torpe (2003).
 
56
Kumlin/Rothstein (2005).
 
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Metadaten
Titel
Redefining a Normative Framework for Meritocracy in the Era of Generative AI: An Inter-Disciplinary Perspective
verfasst von
Kalpana Tyagi
Copyright-Jahr
2024
DOI
https://doi.org/10.1007/978-3-031-56822-0_16

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