Communication, Digital Technology, and Organization CTO

CALL FOR ABSTRACTS Algorithms that make the difference: Unpacking how new technologies are reshaping diversity and (in)equality at work

  • 1.  CALL FOR ABSTRACTS Algorithms that make the difference: Unpacking how new technologies are reshaping diversity and (in)equality at work

    Posted 5 days ago

    Dear CTO community, there are a still a couple of spots if you are interested in participating in our workshop. If you need any additional information, do not hesitate to get in touch. 

    On behalf of the organizing committee,

    Patrizia Zanoni


    Algorithms that make the difference:

    Unpacking how new technologies are reshaping

    diversity and (in)equality at work


    May, 11th-12th, 2023

    Hasselt University, Belgium


    Confirmed keynote speakers

    Niels van Doorn (University of Amsterdam, Netherlands) - Gig platforms as migration infrastructure

    Joana Vassilopoulou (Brunel University, UK) - AI & Diversity: Is the future inclusive?

    Algorithmic technologies and artificial intelligence (AI) are today increasingly redefining diversity, equality and inclusion at work and in society at large. The diffusion of algorithmic systems to recruit, select, organize, control and evaluate workers is facilitating the emergence of business models that produce new social and economic inequalities across sectors, types of organizations and jobs. Despite the burgeoning interest of scholars and practitioners, we still know relatively little about how these technologies are affecting the opportunities of workers with different socio-demographic profiles to access and keep work and, importantly, their working and employment conditions.

    Early celebratory accounts of algorithmic technologies and AI envisioned that they would end enduring discrimination based on gender, 'race', disability, age, etc. in labor markets by grounding HR decisions in 'objective' data. There is increasing evidence and awareness, however, that these technologies do not eliminate human bias (Vassilopoulou et al., 2022). To the extent that the data fed into these systems reflect past biases and historical inequalities, they are on the contrary very likely to reproduce them in the present and entrench them in the future (Angrave et al., 2016; Eubanks, 2018). Moreover, the actuarial logic of risk assessment underpinning algorithmic and AI technologies encourages their users to prioritize options that were successful in the past, reducing the diversity of possible outcomes in the future. The continuous harvesting and processing of biased 'Big Data' tend thus to erect what Elena Zeide has called a silicon ceiling, or "an imperceptible, but systemic, barrier to opportunity" to upward mobility (2022: 403), that reproduces normative ideals about employees.  

    Indeed, algorithmic technologies can redefine the socio-demographic, educational, and geographical profiles of who is able, willing and allowed to carry out certain types of work, impacting a diverse workforce in highly contradictory ways (Zanoni & Pitts, 2022). On the one hand, in both traditional and platform organizations, these technologies can lower the barriers to entry for groups that have historically been excluded, for instance by enabling remote work, bypassing employers, increasing flexibility, and making jobs less physically demanding (Peticca-Harris et al., 2020). On the other hand, these technologies might create new barriers, for instance as they make work conditional on individuals' access to the Internet, immediate availability when demand rises, and the ability to put up with intensified work and to take the risk of too variable income (Glavin et al., 2021; Holtum et al., 2021). Moreover, it has become increasingly visible that algorithmic systems rely on a globally dispersed workforce carrying out the manual digital labor for their operation under highly precarious conditions (Couldry & Mejias, 2018; van Doorn & Badger, 2020; Özbilgin et al., 2023). They also themselves play a key role in sustaining global value chains that enable capital accumulation by leveraging weak national labor protection and anti-discrimination systems (Mergen and Özbilgin, 2021; Zanoni, 2019; Miszczynski & Zanoni, 2022).

    Multiple responses to these harmful developments are today emerging. Next to widespread workers' mobilization and resistance against highly exploitative business models sustained by algorithmic management (Boewe & Schulten, 2019; Tassinari & Maccarrone, 2017), 'explanations' of algorithmic systems are increasingly being demanded to render them more accountable (Hafermalz and Huysman, 2022). In the meantime, reflections are emerging on how to counterbalance algorithmic systems in order to strengthen, rather than undermine, workers' agency in work (Galière, 2021), and on how to better account for the interactions between expert knowledge and machine in the development of 'fair' HRM systems (Broek, Sergeeva and Husyman, 2021).

    In this workshop, we welcome theoretical and empirical contributions on any topic related to how algorithmic and AI technologies are affecting how work is being reorganized and how such reorganization relates to processes of (de)differentiation of the workforce, inclusion/exclusion, exploitation, precarization and (in)equality. Contributions drawing on critical theoretical traditions of scholarship are particularly welcome. The illustrative examples hereunder are non-exhaustive:

         How do algorithmic and AI systems in HRM and work processes redefine diverse workers' opportunities? To which extent do they entrench past inequalities?

         Which role do AI systems play in the production of categories and normative understandings of the 'ideal employee'? With which effects on historically subordinated groups of workers?

         How does algorithmic technology force to rethink notions such as identity, intersectionality and fairness?

         How do platform organizations redefine barriers to enter certain jobs and sectors? How do they redefine the socio-demographic composition of the workforce and the work and employment conditions?

         How do AI systems enable the surveillance and control of spatio-temporally distributed workers (e.g. in the platform economy, remote work)? With which effects on different groups of workers? 

         How are algorithmic technologies leveraged to integrate diverse workers into processes of capital accumulation?  

         Under which conditions do algorithmic technologies reduce discrimination?

         How do contemporary discourses of AI reshape what we understand as 'fair' HRM, work and employment practices?

         How does the use of algorithmic technologies and AI in HRM affect the possibility of workers to see and denounce discrimination? How can accountability be increased?

         How are organizations changing their diversity and equality policies to take into consideration emergent algorithmic technologies and AI?

         How do diversity, equality and inclusion managers understand AI systems and engage with them in their work?

         What role does gender, race, age, etc. identity play in the processes of recomposition of work collectives fragmented by algorithmic technologies? To what extent are communities reconnecting and mobilizing?

         How are algorithmic technologies reappropriated by diverse workers to increase equality?

         What kinds of struggles for more fair AI at work are today emerging? How can they be politically supported?

         What kinds of methodological challenges are we encountering in the study of algorithmic technologies, diversity and equality?


    The workshop would like to initiate a forum for researchers interested in how algorithmic technologies and AI are transforming work and diversity, equality and inclusion in both 'traditional' and new organizations (e.g. platform firms of various types). It will foster conversations among 30 to 35 researchers sharing their research on these topics and facilitate discussions of the most promising conceptual and methodological approaches. Participants will be assigned a colleague's paper to read and discuss to generate in-depth, constructive conversations conducive to the further development of participants' work. We foresee a dedicated space for PhD and early-career researchers to present their work and receive feedback. Depending on the nature of the submissions, the possibility of a shared publication in the form of a special issue or an edited volume will be considered.


    To participate please submit an abstract of max. 500 words by no later than March 10th, 2023, to Please include all authors' names and email addresses and indicate which authors plan to attend if the abstract is accepted. You will be notified about acceptance by March 20th, 2023. If you have any inquiries, you can contact Jannes Zwaenepoel at the above mentioned address.


    There is no participation fee, yet participants are expected to cover themselves travel, accommodation and any other expenses. 

    We look forward to welcoming you to Hasselt!


    Patrizia Zanoni (Universiteit Hasselt), Paul Boselie (Utrecht University), Mayra Ruiz Castro (University of Roehampton), Sophia Galière (Université Côte d'Azur), Ella Hafermalz (Vrije Universiteit Amsterdam), Mustafa Özbilgin (Brunel University), Jo Pierson (Hasselt University), Koen Van Laer (Hasselt University) and Jannes Zwaenepoel (Universiteit Hasselt).


    This initiative is funded through the FWO project grant G085119N "Is artificial intelligence a game changer for diversity at work? A sociomaterial study of the effects of algorithmic HR analytics on HRM practice and equal opportunities".


    Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1–11.

    Couldry, N., & Mejias, U. A. (2019). Data colonialism: Rethinking big data's relation to the contemporary subject. Television & New Media, 20(4), 336-349.

    Boewe, J., & Schulten, J. (2019) The long struggle of the Amazon employees. Laboratory of resistance: Union organising in e-commerce worldwide. Rosa Luxemburg Foundation. Updated, expanded second edition. Available at

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

    Galière, S. (2021). Towards a capacitating instrumentation of digital platforms: A case study of Airbnb Experience. RIMHE: Revue Interdisciplinaire Management, Homme Entreprise, 4310(2), 27a-50a.

    Glavin, P., Bierman, A., & Schieman, S. (2021). Über-alienated: Powerless and alone in the gig economy. Work and Occupations, 48(4), 399-431.

    Hafermalz, E., & Huysman, M. (2022). Please explain: Key questions for explainable AI research from an organizational perspective. Morals & Machines, 1(2), 10-23.

    Holtum, P. J., Irannezhad, E., Marston, G., & Mahadevan, R. (2021). Business or pleasure? A comparison of migrant and non-migrant Uber drivers in Australia. Work, Employment and Society, 36(2), 290-309.

    Mergen, A., & Özbilgin, M. (2021). Toxic illusio in the global value chain: The case of Amazon. In Destructive leadership and management hypocrisy (pp. 163-178). Emerald Publishing Limited.

    Özbilgin , M., Gundogdu, N., & Akalin, C. (2023). Artificial intelligence, gig economy and precarity. In E. Meliou, J. Vassilopoulou, M. Özbilgin (Eds). Diversity and Precarious Work During Socio-Economic Upheaval. Cambridge: Cambridge University Press.

    Peticca-Harris, A., deGama, N., and Ravishankar, M. N. (2020). Postcapitalist precarious work and those in the `drivers' seat: Exploring the motivations and lived experiences of Uber drivers in Canada. Organization 27(1): 36–59.

    TAssinari, A., & Maccarrone, V. (2017). The mobilisation of gig economy couriers in Italy: some lessons for the trade union movement .Transfer, 23(3), 353-357.

    Van den Broek, E., Sergeeva, A., & Huysman, M. (2021). When the machine meets the expert: An ethnography of developing AI for hiring. MIS Quarterly, 45(3), 1557-1580.

    van Doorn, N., Ferrari, F. & Graham, M. (2022). Migration and migrant labour in the gig economy: An intervention. Work, Employment & Society,

    van Doorn, N. (2017). Platform labor: On the gendered and racialized exploitation of low-income service work in the 'on-demand' economy. Information, Communication & Society, 20, 898–914.

    Vassilopoulou, J., Kyriakidou, O., Özbilgin, M. F., & Groutsis, D. (2022). Scientism as illusio in HR algorithms: Towards a framework for algorithmic hygiene for bias proofing. Human Resource Management Journal, 1– 15.

    Zanoni, P. (2019). Labor market inclusion through predatory capitalism? The 'sharing economy', diversity, and the crisis of social reproduction in the Belgian coordinated market economy. In S. Vallas & A. Kovalainen (eds.) Work and Labor in the Digital Age. Research in the Sociology of Work. Vol 33. Emerald, pp. 145–164.

    Zanoni, P., & Miszczynski, M. (2022). Post-diversity, precarious work for all: Un-bordering categories of socio-demographic difference in the Amazon warehouse, AOM, hybrid, Seattle, 5-9 August.

    Zanoni, P., & Pitts, H.F. (2022). Inclusion through the platform economy? The 'diverse' crowd as relative surplus populations and the pauperization of labour. In I. Ness (ed.) Routledge Handbook of the Gig Economy. London: Routledge.

    Zeide, E. (2022). The Silicon Ceiling: How Artificial Intelligence Constructs an Invisible Barrier to Opportunity. University of Missouri-Kansas City Law Review, 91(403).

    Patrizia Zanoni
    Full Professor, PhD
    School of Social Sciences Hasselt University
    Twitter @patrizia_zanoni
    Co-editor-in-Chief of Organization: The critical journal of organization, theory & society