Abstract
This paper aims at filling some gaps in the mainstream debate on automation and the future of work. This debate has concentrated, so far, on how many jobs will be lost as a consequence of technological innovation. This paper examines instead issues related to the quality of jobs in future labour markets. It addresses the detrimental effects on workers of awarding legal capacity and rights and obligation to robots. It examines the implications of practices such as People Analytics and the use of big data and artificial intelligence to manage the workforce. It stresses on an oft-neglected feature of the contract of employment, namely the fact that it vests the employer with authority and managerial prerogatives over workers. It points out that a vital function of labour law is to limit these authority and prerogatives to protect the human dignity of workers. It then highlights the benefits of human-rights based approaches to labour regulation to protect workers’ privacy against invasive electronic monitoring. It concludes by highlighting the crucial role of collective regulation and social partners in governing automation and the impact of technology at the workplace. It stresses that collective dismissal regulation and the involvement of workers’ representatives in managing and preventing job losses is crucial and that collective actors should actively participate in the governance of technology-enhanced management systems, to ensure a vital “human-in-command” approach.
BOF-ZAP Professor of Labour Law at KU Leuven, the University of Leuven. This chapter draws on the article ‘Negotiating the Algorithm’: Automation, Artificial Intelligence and Labour Protection published in a special issue of the Comparative Labor & Policy Journal on “Automation, Artificial Intelligence, and Labour Protection” guest-edited by me (41 Comp. Labor Law Policy J. (2019)). This chapter and the special issue were published within the framework of the Odysseus grant “Employment rights and labour protection in the on-demand economy” that I received from the FWO Research Foundation—Flanders.
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Notes
- 1.
Frey and Osborn (2013).
- 2.
- 3.
- 4.
An exception is Eurofound (2018).
- 5.
McKinsey Global Institute (2017).
- 6.
See below Sect. 2.
- 7.
- 8.
- 9.
See below, Sect. 4.
- 10.
- 11.
Landes (1969).
- 12.
Stone (2004).
- 13.
The term “artificial intelligence”, in this paper, is used as a reference to the so-called “narrow artificial intelligence” or “weak artificial intelligence”, namely the artificial intelligence used to performed a single task, such as—as a commonly used description goes—“playing chess or Go, making purchase suggestions, sales predictions and weather forecast”. This is the only type of artificial intelligence that exists, nowadays. Even self-driving cars are considered merely a sum of several narrow AIs, and the same applies to online translation engines. Narrow AI is commonly opposed to “General AI”, i.e. “the type of Artificial Intelligence that can understand and reason its environment as a human would”, which has not been developed yet. The direct citations are from Dickson (2017). For a broader discussion of the distinction between “strong” and “weak” AI, see Kaplan (2016).
- 14.
Emanuele Dagnino, supra note 8.
- 15.
See below in this Section.
- 16.
Pav Akhtar, Phoebe Moore, & Martin Upchurch, supra note 7; Manokha (2017).
- 17.
- 18.
De Stefano (2016).
- 19.
Aloisi (2016).
- 20.
Foundation for European Progressive Studies (FEPS) (2017).
- 21.
- 22.
Pav Akhtar, Phoebe Moore & Martin Upchurch, supra note 7.
- 23.
Bodie et al. (2017).
- 24.
For a thorough review carried out by a public authority of common EPM practices see Article 29 Data Protection Working Party (now, the European Data Protection Board), Opinion 2/2017 on data processing at work, adopted on 8 June 2017.
- 25.
Humanize, https://www.humanyze.com.
- 26.
According to the Article 29 Data Protection Working Party (now, the European Data Protection Board), supra note 24: “The risk is not limited to the analysis of the content of communications. Thus, the analysis of metadata about a person might allow for an equally privacy-invasive detailed monitoring of an individual’s life and behavioural patterns”.
- 27.
Fischbach et al. (2009).
- 28.
The workplace of the Future, The Economist, Mar 28, 2018, https://www.economist.com/news/leaders/21739658-artificial-intelligence-pushes-beyond-tech-industry-work-could-become-faireror-more; Solon (2017).
- 29.
- 30.
Interguard, https://interguardsoftware.com/web-filtering.html.
- 31.
Boot (2019).
- 32.
O’Neill (2016).
- 33.
Ifeoma Ajunwa, Kate Crawford & Jason Schultz, supra note 10.
- 34.
Matthew T. Bodie, Miriam A. Cherry, Marcia L. McCormick & Jintong Tang, supra note 23.
- 35.
Olivia Solon, supra note 28.
- 36.
Emanuele Dagnino, supra note 8.
- 37.
Matthew T. Bodie, Miriam A. Cherry, Marcia L. McCormick & Jintong Tang, supra note 23.
- 38.
The workplace of the Future, supra note 28.
- 39.
Matthew T. Bodie, Miriam A. Cherry, Marcia L. McCormick & Jintong Tang, supra note 23.
- 40.
Hendrickx (2015).
- 41.
Ifeoma Ajunwa, Kate Crawford & Jason Schultz, supra note 10.
- 42.
Pav Akhtar, Phoebe Moore & Martin Upchurch, supra note 7.
- 43.
European Economic and Social Committee (2017).
- 44.
Matthew T. Bodie, Miriam A. Cherry, Marcia L. McCormick & Jintong Tang, supra note 23.
- 45.
Frank Pasquale, supra note 8; Noble (2018).
- 46.
Cathy O’Neill, supra note 32.
- 47.
Eubanks (2018).
- 48.
- 49.
Valerio De Stefano, supra note 18.
- 50.
See also Jerry Kaplan, supra note 13.
- 51.
Cited by Olivia Solon, supra note 28.
- 52.
Finkin (2017).
- 53.
Hendrickx (2014).
- 54.
Matthew T. Bodie, Miriam A. Cherry, Marcia L. McCormick & Jintong Tang, supra note 23.
- 55.
Romano and Zitelli (2017).
- 56.
Sadowski (2016).
- 57.
- 58.
See, however, Rogers (2019) and the other articles dealing with UBI published in that same Journal’s issue.
- 59.
Zwolinski (2014). Janine Berg, supra note 48, also dismisses the idea that a UBI could adequately substitute for employment protection.
- 60.
Sciarra (2007).
- 61.
De Stefano (2014).
- 62.
- 63.
Politakis (2007).
- 64.
For an extensive discussion of how protection of the human dignity and human rights of workers can be posed as a foundational element of labour law, see the contributions collected in Philosophical Foundations of Labour Law (Hugh Collins, Gillian Lester, and Virginia Mantouvalou eds. 2019). For an in-depth critical appraisal of human-rights based arguments in labour-law discourses, see, however, Finkin (2019).
- 65.
See Hendrickx (2019).
- 66.
Bărbulescu v. Romania, No 61496/08, ECHR 2017.
- 67.
Article 9 of the revised Convention.
- 68.
Mittelstadt and Wachter (2019).
- 69.
For an in-depth account of the potential shortcomings of Article 22, see Floridi et al. (2017). A critical question will concern the interpretation of the word “solely” in this context. Adequate standards are needed to ensure that nominal involvement of humans that sanction decisions made by automatic mechanisms will not deprive data subjects of the protection under Art 22.
- 70.
Another case of exception is when data subjects give their express consent to solely automated decision-making. It is worth noting, however, that the Article 29 Data Protection Working Party (now, the European Data Protection Board) in its Opinion 2/2017 on data processing at work, adopted on 8 June 2017, observed: “consent is highly unlikely to be a legal basis for data processing at work, unless employees can refuse without adverse consequences”.
- 71.
Brent Mittelstadt & Sandra Wachter, supra note 69.
- 72.
Article 88, Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation); see, for initial comments, Armaroli and Dagnino (2019), Hendrickx (2018), Fusco (2018).
- 73.
Frank Hendrickx, supra note 72.
- 74.
For an analysis of the United States’ legal framework in this context, see Frank Pasquale, supra note 8. See also Ifeoma Ajunwa, Kate Crawford & Jason Schultz, supra note 10; Matthew T. Bodie, Miriam A. Cherry, Marcia L. McCormick & Jintong Tang, supra note 23; Frank Hendrickx, supra note 53.
- 75.
Liebman (1993).
- 76.
Adams et al. (2018).
- 77.
- 78.
Deakin et al. (2014b).
- 79.
Directive 2002/14/EC of the European Parliament and of the Council of 11 March 2002 establishing a general framework for informing and consulting employees in the European Community.
- 80.
Swedish Employment (Co-Determination in the Workplace) Act (1976:580), Section 19, for instance, binds employers “to regularly inform an employees’ organisation in relation to which [they are] bound by collective bargaining agreement as to the manner in which the business is developing in respect of production and finance and as to the guidelines for personnel policy”. Analogous duties are provided also when the employer is not bound by a collective agreement.
- 81.
European Economic and Social Committee, Artificial intelligence—The consequences of artificial intelligence on the (digital) single market, production, consumption, employment and society (own-initiative opinion No. 7, 2017). See now also ILO Global Commission on the Future of Work, Work for a Brighter Future (2019).
- 82.
Information and consultation and collective negotiation on data collection and processing are also recommended under the 1997 ILO Code of practice on the protection of workers’ personal data. See also Choudary (2018).
- 83.
UNI Global Union (2017).
- 84.
Phoebe Moore, Martin Upchurch & Xanthe Whittaker, supra note 10; Ilaria Armaroli & Emanuele Dagnino, supra note 72.
- 85.
Seifert (2018).
- 86.
Recently, the OECD also adopted a recommendation calling for social dialogue to play a role about the introduction and use of artificial intelligence at work. See OECD (2019).
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De Stefano, V. (2020). Algorithmic Bosses and What to Do About Them: Automation, Artificial Intelligence and Labour Protection. In: Marino, D., Monaca, M. (eds) Economic and Policy Implications of Artificial Intelligence. Studies in Systems, Decision and Control, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-030-45340-4_7
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