Skip to main content
SpringerLink
Log in

Algorithmic Bosses and What to Do About Them: Automation, Artificial Intelligence and Labour Protection

  • Chapter
  • First Online:
Economic and Policy Implications of Artificial Intelligence

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 288))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Frey and Osborn (2013).

  2. 2.

    See, for instance, the well-known paper of Frey and Osborn (2013). For an in-depth discussion on manufacturing processes, see Dauth et al. (2017).

  3. 3.

    The literature on the topic is already enormous, see Autor (2015), OECD (2016, 2018). See, for a general critical discussion, Kucera (2017). For an in-depth legal discussion, see Estlund (2018).

  4. 4.

    An exception is Eurofound (2018).

  5. 5.

    McKinsey Global Institute (2017).

  6. 6.

    See below Sect. 2.

  7. 7.

    Akhtar et al. (2018). See also the articles of Aloisi and Gramano (2019).

  8. 8.

    Pasquale (2015), Dagnino (2017).

  9. 9.

    See below, Sect. 4.

  10. 10.

    Moore et al. (2018), Ajunwa et al. (2017).

  11. 11.

    Landes (1969).

  12. 12.

    Stone (2004).

  13. 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. 14.

    Emanuele Dagnino, supra note 8.

  15. 15.

    See below in this Section.

  16. 16.

    Pav Akhtar, Phoebe Moore, & Martin Upchurch, supra note 7; Manokha (2017).

  17. 17.

    Lecher (2019). The article reports: “Amazon says supervisors are able to override the process”. See also Baraniuk (2015).

  18. 18.

    De Stefano (2016).

  19. 19.

    Aloisi (2016).

  20. 20.

    Foundation for European Progressive Studies (FEPS) (2017).

  21. 21.

    O’Donovan (2018), Fillon (2018).

  22. 22.

    Pav Akhtar, Phoebe Moore & Martin Upchurch, supra note 7.

  23. 23.

    Bodie et al. (2017).

  24. 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. 25.

    Humanize, https://www.humanyze.com.

  26. 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. 27.

    Fischbach et al. (2009).

  28. 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. 29.

    Crossover, https://www.crossover.com/worksmart/#worksmart-productivity-tool.

  30. 30.

    Interguard, https://interguardsoftware.com/web-filtering.html.

  31. 31.

    Boot (2019).

  32. 32.

    O’Neill (2016).

  33. 33.

    Ifeoma Ajunwa, Kate Crawford & Jason Schultz, supra note 10.

  34. 34.

    Matthew T. Bodie, Miriam A. Cherry, Marcia L. McCormick & Jintong Tang, supra note 23.

  35. 35.

    Olivia Solon, supra note 28.

  36. 36.

    Emanuele Dagnino, supra note 8.

  37. 37.

    Matthew T. Bodie, Miriam A. Cherry, Marcia L. McCormick & Jintong Tang, supra note 23.

  38. 38.

    The workplace of the Future, supra note 28.

  39. 39.

    Matthew T. Bodie, Miriam A. Cherry, Marcia L. McCormick & Jintong Tang, supra note 23.

  40. 40.

    Hendrickx (2015).

  41. 41.

    Ifeoma Ajunwa, Kate Crawford & Jason Schultz, supra note 10.

  42. 42.

    Pav Akhtar, Phoebe Moore & Martin Upchurch, supra note 7.

  43. 43.

    European Economic and Social Committee (2017).

  44. 44.

    Matthew T. Bodie, Miriam A. Cherry, Marcia L. McCormick & Jintong Tang, supra note 23.

  45. 45.

    Frank Pasquale, supra note 8; Noble (2018).

  46. 46.

    Cathy O’Neill, supra note 32.

  47. 47.

    Eubanks (2018).

  48. 48.

    Adler-Milstein et al. (2018). See also the discussion of automated scheduling in Berg (2019).

  49. 49.

    Valerio De Stefano, supra note 18.

  50. 50.

    See also Jerry Kaplan, supra note 13.

  51. 51.

    Cited by Olivia Solon, supra note 28.

  52. 52.

    Finkin (2017).

  53. 53.

    Hendrickx (2014).

  54. 54.

    Matthew T. Bodie, Miriam A. Cherry, Marcia L. McCormick & Jintong Tang, supra note 23.

  55. 55.

    Romano and Zitelli (2017).

  56. 56.

    Sadowski (2016).

  57. 57.

    See, for instance, Standing (2004), Hollo (2016).

  58. 58.

    See, however, Rogers (2019) and the other articles dealing with UBI published in that same Journal’s issue.

  59. 59.

    Zwolinski (2014). Janine Berg, supra note 48, also dismisses the idea that a UBI could adequately substitute for employment protection.

  60. 60.

    Sciarra (2007).

  61. 61.

    De Stefano (2014).

  62. 62.

    Fenwick and Novitz (2010), Arthurs (2006), Mantouvalou (2012).

  63. 63.

    Politakis (2007).

  64. 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. 65.

    See Hendrickx (2019).

  66. 66.

    Bărbulescu v. Romania, No 61496/08, ECHR 2017.

  67. 67.

    Article 9 of the revised Convention.

  68. 68.

    Mittelstadt and Wachter (2019).

  69. 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. 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. 71.

    Brent Mittelstadt & Sandra Wachter, supra note 69.

  72. 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. 73.

    Frank Hendrickx, supra note 72.

  74. 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. 75.

    Liebman (1993).

  76. 76.

    Adams et al. (2018).

  77. 77.

    Deakin et al. (2014a), FitzRoy and Kraft (2005).

  78. 78.

    Deakin et al. (2014b).

  79. 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. 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. 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. 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. 83.

    UNI Global Union (2017).

  84. 84.

    Phoebe Moore, Martin Upchurch & Xanthe Whittaker, supra note 10; Ilaria Armaroli & Emanuele Dagnino, supra note 72.

  85. 85.

    Seifert (2018).

  86. 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).

References

  • Adams, Z., Bishop, L., Deakin, S., Fenwick, C., Garzelli, S.M., Rusconi, G.: The economic significance of laws relating to employment protection and different forms of employment: analysis of a panel of 117 countries, 1990–2013. Int. Labour Rev. (2018). https://doi.org/10.1111/ilr.12092

    Article  Google Scholar 

  • Adler-Milstein, S., Bellisle, D., Fugiel, P.J., Jarpe, M., Kesavan, S., Lambert, S.J., McCorkell, L., Ospina, L.A., Pendem, P., Rapoport, E.D., Williams, J.: Stable scheduling increases productivity and sales. The Stable Scheduling Study. https://worklifelaw.org/projects/stable-scheduling-study/report/ (2018)

  • Ajunwa, I., Crawford, K., Schultz, J.: Limitless worker surveillance. Calif. Law Rev. 105, 102 (2017)

    Google Scholar 

  • Akhtar, P., Moore, P., Upchurch, M.: Digitalisation of work and resistance. In: Moore, P., Upchurch, M., Whittaker, X. (eds.) Humans and Machines at Work: Monitoring, Surveillance and Automation in Contemporary Capitalism (2018)

    Google Scholar 

  • Aloisi, A.: Commoditized workers: case study research on labour law issues arising from a set of ‘on-demand/gig economy’ platforms. Comp. Labor Law Policy J. 37, 653 (2016)

    Google Scholar 

  • Aloisi, A., Gramano, E.: Artificial intelligence is watching you at work. Digital surveillance, employee monitoring and regulatory issues in the EU context. Comp. Labor Law Policy J. 41 (2019)

    Google Scholar 

  • Armaroli, I., Dagnino, E.: A seat at the table: negotiating data processing in the workplace. A national case study and comparative insights. Comp. Labor Law Policy J. 41 (2019)

    Google Scholar 

  • Arthurs, H.: Who’s afraid of globalization? Reflections on the future of labour law. In: Craig, J.D.R., Michael Lynk, S. (eds.) Globalization and the Future of Labour Law (2006)

    Google Scholar 

  • Autor, D.: Why are there still so many jobs? The history and future of workplace automation. J. Econ. Perspect. 29(3) (2015) (Summer)

    Article  Google Scholar 

  • Baraniuk, C.: How algorithms run Amazon’s warehouse. BBC Future, 18 Aug 2015. https://www.bbc.com/future/story/20150818-how-algorithms-run-amazons-warehouses (2015)

  • Berg, J.: Protecting workers in the digital age: technology, outsourcing and the growing precariousness of work. Comp. Labor Law Policy J. 41 (2019)

    Google Scholar 

  • Bodie, M.T., Cherry, M.A., McCormick, M.L., Tang, J.: The law and policy of people analytics. Univ. Colorado Law Rev. 88, 961 (2017)

    Google Scholar 

  • Boot, R.: UK businesses using artificial intelligence to monitor staff activity. The Guardian, 7 Apr 2019. https://www.theguardian.com/technology/2019/apr/07/uk-businesses-using-artifical-intelligence-to-monitor-staff-activity (2019)

  • Choudary, S.P.: The architecture of digital labour platforms: policy recommendations on platform design for worker well-being. ILO Future of Work Research Paper Series, No. 3 (2018)

    Google Scholar 

  • Dagnino, E.: People analytics: lavoro e tutele al tempo del management tramite big data. Labour Law Issues 3, 1 (2017)

    Google Scholar 

  • Dauth, W., Findeisen, S., Südekum, J., Wößner, N.: German robots—the impact of industrial robots on workers. IAB Discussion Paper 30/2017, 2 Oct 2017. https://doku.iab.de/discussionpapers/2017/dp3017.pdf (2017)

  • De Stefano, V.: A tale of oversimplification and deregulation: the mainstream approach to labour market segmentation and recent responses to the crisis in European countries. Ind. Law J. 43, 253 (2014)

    Article  Google Scholar 

  • De Stefano, V.: The rise of the ‘just-in-time workforce’: on-demand work, crowdwork and labour protection in the ‘gig-economy.’ Comp. Labor Law Policy J. 37, 471 (2016)

    Google Scholar 

  • Deakin, S., Fenwick, C., Sarkar, P.: Labour law and inclusive development: the economic effects of industrial relations laws in middle income countries. In: Schmiegelow, M., Schmiegelow, H. (eds.) Institutional Competition between Common Law and Civil Law: Theory and Policy (2014a)

    Chapter  Google Scholar 

  • Deakin, S., Malmberg, J., Sarkar, P.: How do labour laws affect unemployment and the labour share of national income? The experience of six OECD countries, 1970–2010. Int. Labour Rev. 153, 1 (2014b)

    Article  Google Scholar 

  • Dickson, B.: What is narrow, general and super artificial intelligence. TechTalks. 12 May 2017. https://bdtechtalks.com/2017/05/12/what-is-narrow-general-and-super-artificial-intelligence/ (2017)

  • Estlund, C.: What should we do after work? Automation and employment law. Yale Law J. 128, 254 (2018)

    Google Scholar 

  • Eubanks, V.: Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press, New York (2018)

    Google Scholar 

  • Eurofound: Game Changing Technologies: Exploring the Impact of Production Processes and Work. Research Report (2018)

    Google Scholar 

  • 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), 31 May 2017. JO C 288, 31.8.2017, p. 43 (2017)

    Google Scholar 

  • Fenwick, C., Novitz, T.: Human Rights at Work: Perspectives on Law and Regulation. Bloomsbury Publishing, London (2010)

    Google Scholar 

  • Fillon, W.: How rating your server is making their life miserable. Eater, 22 June 2018. https://www.eater.com/2018/6/22/17492528/tablets-restaurants-surveys-score-servers (2018)

  • Finkin, M.: Chapter 7: privacy and autonomy. Empl. Rights Employ. Policy J. 21, 589 (2017)

    Google Scholar 

  • Finkin, M.: Worker rights as human rights: regenerative reconception or rhetorical refuge? In: Bellace, J., ter Haar, B. (eds.) Research Handbook on Labour, Business and Human Rights Law (2019)

    Google Scholar 

  • Fischbach, K., Gloor, P.A., Lassenius, C., Olguin, D.O., Pentland, A.S., Putzke, J., Schoder, D.: Analyzing the flow of knowledge with sociometric badges. In: COINs. https://www.ickn.org/documents/COINs2009_Fischbach_Gloor_Lassenius_etc.pdf (2009)

  • FitzRoy, F., Kraft, K.: Co-determination, efficiency and productivity. Br. J. Ind. Relat. 43, 233 (2005)

    Article  Google Scholar 

  • Floridi, L., Mittelstadt, B., Wachter, S.: Why a right to explanation of automated decision-making does not exist in the general data protection regulation. Int. Data Priv. Law 7, 76 (2017)

    Article  Google Scholar 

  • Foundation for European Progressive Studies (FEPS): Work in the European Gig-Economy (2017)

    Google Scholar 

  • Frey, C.B., Osborn, M.A.: The future of employment: How susceptible are jobs to computerisation. Oxford Martin School, 17 Sept 2013. https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf (2013)

  • Fusco, F.: Employee privacy in the context of EU regulation N.2016/679: some comparative remarks. Paper presented at the XVI International Conference in Commemoration of Professor Marco Biagi, Mar 2018

    Google Scholar 

  • Hendrickx, F.: Employment privacy. In: Blanpain, R. (ed.) Comparative Labour Law and Industrial Relations in Industrialized Market Economies (2014)

    Google Scholar 

  • Hendrickx, F.: Privacy en elektronisch toezicht. In: Hendrickx, F. (ed.) Arbeidsrecht (2015)

    Google Scholar 

  • Hendrickx, F.: Privacy, data protection and measuring employee performance. The triggers from technology and smart work. Regulating for Globalization. Trade, Labor and EU Law Perspectives, 21 Mar 2018. https://regulatingforglobalization.com/2018/03/21/privacy-data-protection-and-measuring-employee-performance-the-triggers-from-technology-and-smart-work (2018)

  • Hendrickx, F.: Article 7. Protection of private and family life and Article 8. Protection of personal data. In: Clauwaert, S., Dorssemont, F., Lörcher, K., Schmitt, M. (eds.) The Charter of Fundamental Rights of the European Union (CFREU) and the Employment Relation (2019)

    Google Scholar 

  • Hollo, T.: Can less work be more fair? A Discussion Paper on Universal Basic Income and Shorter Working Week. The Green Institute, Canberra City (2016)

    Google Scholar 

  • Kaplan, J.: Artificial Intelligence: What Everyone Needs to Know. Oxford University Press, Oxford (2016)

    Google Scholar 

  • Kucera, D.: New automation technologies and job creation and destruction dynamics. ILO Employment Policy Brief, 12 May 2017

    Google Scholar 

  • Landes, D.: The Unbound Prometheus: Technological Change and Industrial Development in Western Europe from 1750 to the Present. Cambridge University Press, Cambridge (1969)

    Google Scholar 

  • Lecher, C.: How Amazon automatically tracks and fires warehouse workers for ‘productivity’. The Verge, 25 Apr 2019. https://www.theverge.com/2019/4/25/18516004/amazon-warehouse-fulfillment-centers-productivity-firing-terminations (2019)

  • Liebman, S.: Individuale e collettivo nel contratto di lavoro (1993)

    Google Scholar 

  • Manokha, I.: Why the rise of wearable tech to monitor employees is worrying. The Independent, 4 Jan 2017. https://www.independent.co.uk/life-style/gadgets-and-tech/why-the-rise-of-wearable-tech-to-monitor-employees-is-worrying-a7508656.html (2017)

  • Mantouvalou, V.: Are labour rights human rights? Eur. Labour Law J. 3, 151 (2012)

    Article  Google Scholar 

  • McKinsey Global Institute: A future that works: automation, employment and productivity, Jan 2017

    Google Scholar 

  • Mittelstadt, B., Wachter, S.: A right to reasonable interferences: re-thinking data protection law in the age of big data and AI. Columbia Bus. Law Rev. https://ssrn.com/abstract=3248829 (2019)

  • Moore, P., Upchurch, M., Whittaker, X.: Humans and Machines at Work: Monitoring, Surveillance and Automation in Contemporary Capitalism (2018)

    Google Scholar 

  • Noble, S.U.: Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press, New York (2018)

    Google Scholar 

  • O’Donovan, C.: An invisible rating system at your favorite chain restaurant is costing your server. BuzzFeed News, 21 June 2018

    Google Scholar 

  • OECD: The risk of automation for jobs in OECD countries: a comparative analysis. OECD Social, Employment and Migration Working Papers, No. 189, 14 May 2016

    Google Scholar 

  • OECD: Automation, skills use and training. OECD Social, Employment and Migration Working Papers, No. 202, 8 Mar 2018

    Google Scholar 

  • O’Neill, C.: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Broadway Books, New York (2016)

    MATH  Google Scholar 

  • Organization for Economic Co-operation and Development (OECD): Recommendation of the Council on Artificial Intelligence (2019)

    Google Scholar 

  • Pasquale, F.: The Black Box Society. The Secret Algorithms That Control Money and Information (2015)

    Google Scholar 

  • Politakis, G.: Protecting Labour Rights as Human Rights: Present and Future of International Supervision. ILO, Geneva (2007)

    Google Scholar 

  • Rogers, B.: Basic income and the resilience of social democracy. Comp. Labor Law Policy J. 41 (2019)

    Google Scholar 

  • Romano, A., Zitelli, A.: Il reddito di base è una cosa seria. Valigia Blu, 7 Mar 2017. https://storie.valigiablu.it/reddito-di-base (2017)

  • Sadowski, J.: Why Silicon Valley is embracing universal basic income. The Guardian, 22 June 2016. https://www.theguardian.com/technology/2016/jun/22/silicon-valley-universal-basic-income-y-combinator (2016)

  • Sciarra, S.: EU Commission green paper ‘modernising labour law to meet the challenges of the 21st century.’ Ind. Law J. 36, 375 (2007)

    Article  Google Scholar 

  • Seifert, A.: Employee data protection in the transational company. In: Hendrickx, F., De Stefano, V. (eds.) Game Changers in Labour Law: Shaping the Future of Work. Bulletin of Comparative Labour Relations No. 100 (2018)

    Google Scholar 

  • Solon, O.: Big brother isn’t just watching: workplace surveillance can track your every move. The Guardian, 6 Nov 2017. https://www.theguardian.com/world/2017/nov/06/workplace-surveillance-big-brother-technology?CMP=share_btn_tw (2017)

  • Standing, G.: A Precariat Charter From Denizens to Citizens (2004)

    Google Scholar 

  • Stone, K.V.W.: From Widgets to Digits: Employment Regulation for the Changing Workplace. Cambridge University Press, Cambridge (2004)

    Book  Google Scholar 

  • UNI Global Union: Global Union Sets New Rules for the Next Frontier of Work—Ethical AI and Employee Data Protection. UNI Global Union, 11 Dec 2017. https://uniglobalunion.org/news/global-union-sets-new-rules-next-frontier-work-ethical-ai-and-employee-data-protection (2017)

  • Zwolinski, M.: The pragmatic libertarian case for a basic income guarantee. Cato-Unbound, 4 Aug 2014. https://www.cato-unbound.org/2014/08/04/matt-zwolinski/pragmatic-libertarian-case-basic-income-guarantee (2014)

Download references

Author information

Authors and Affiliations

  1. KU Leuven, Leuven, Belgium

    Valerio De Stefano

Authors
  1. Valerio De Stefano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valerio De Stefano .

Editor information

Editors and Affiliations

  1. Mediterranea University of Reggio Calabria, Reggio Calabria, Italy

    Domenico Marino

  2. Mediterranea University of Reggio Calabria, Reggio Calabria, Italy

    Melchiorre A. Monaca

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

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

Download citation

Publish with us

Policies and ethics

Search

Navigation