The Implementation of BCTrustAI.SL into the Automated Practices of Digital Labour Platforms to Ensure Fairness, Transparency and Accountability
DOI:
https://doi.org/10.46282/blr.2025.9.1.949Keywords:
Digital Labour Platforms , Platform Workers , Automated Decision and Monitoring Mechanisms , Transparency , Human Oversight , Human Review , Information and Consultation , Blockchain Technology , Trustworthy AI, Provision of Information to WorkersAbstract
Since digital labour platforms may infringe upon the rights of platform workers through automated decision-making and monitoring practices, the European Parliament and of the Council has adopted the Directive (EU) 2024/2831 on improving working conditions in platform work (Directive 2024/2831). This directive seeks to foster fairness, transparency, and accountability, establishing four key requirements in its algorithmic management chapter: transparency, human oversight, human review, rights to information and consultation. However, due to the abstract nature of these provisions, meeting the normative expectations of the directive poses a challenge. This paper presents the implementation of the Blockchain-Based Trustworthy Artificial Intelligence Supported by Stakeholders-In-The-Loop Model (BCTrustAI.SL) into the automated decision and monitoring practices used by digital labour platforms. It aims to discuss theoretically the validation of the concept of BCTrustAI.SL, setting the stage for subsequent technical proofs of concept.
References
Ahopelto, H. (2023). Governance mechanisms and metrics for digital platform workers: Multiple case study of digital labour platforms. https://osuva.uwasa.fi/handle/10024/15420
Alasoini, T., Immonen, J., Seppänen, L. and Känsälä, M. (2023). Platform workers and digital agency: Making out on three types of labor platforms. Frontiers in Sociology, 8, https://doi.org/10.3389/fsoc.2023.1063613 DOI: https://doi.org/10.3389/fsoc.2023.1063613
Basukie, J., Wang, Y., and Li, S. (2020). Big data governance and algorithmic management in sharing economy platforms: A case of ridesharing in emerging markets. Technological Forecasting and Social Change, 161, 120310, https://doi.org/10.1016/j.techfore.2020.120310 DOI: https://doi.org/10.1016/j.techfore.2020.120310
Battifarano, M. and Qian, Z. (S.) (2019). Predicting real-time surge pricing of ride-sourcing companies. Transportation Research Part C: Emerging Technologies, 107, 444–462, https://doi.org/10.1016/j.trc.2019.08.019 DOI: https://doi.org/10.1016/j.trc.2019.08.019
Chan, N. K. (2019). The Rating Game: The Discipline of Uber’s User-Generated Ratings. Surveillance & Society, 17(1/2), 183–190, https://doi.org/10.24908/ss.v17i1/2.12911 DOI: https://doi.org/10.24908/ss.v17i1/2.12911
Chen, J., Luo, J., Hu, W. and Ma, J. (2023). Fit into work! From formalizing governance of gig platform ecosystems to helping gig workers craft their platform work. Decision Support Systems, 174, 114016, https://doi.org/10.1016/j.dss.2023.114016 DOI: https://doi.org/10.1016/j.dss.2023.114016
Cram, W. A., Wiener, M., Tarafdar, M. and Benlian, A. (2022). Examining the Impact of Algorithmic Control on Uber Drivers’ Technostress. Journal of Management Information Systems, 39(2), 426–453, https://doi.org/10.1080/07421222.2022.2063556 DOI: https://doi.org/10.1080/07421222.2022.2063556
De Stefano, V., and Aloisi, A. (2018). European legal framework for digital labour platforms (JRC Science for Policy Report No. JRC112243). Publications Office of the European Union. https://doi.org/10.2760/78590
Duggan, J., Sherman, U., Carbery, R. and McDonnell, A. (2020). Algorithmic management and app-work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114–132, https://doi.org/10.1111/1748-8583.12258 DOI: https://doi.org/10.1111/1748-8583.12258
Dunn, M. (2020). Making gigs work: Digital platforms, job quality and worker motivations. New Technology, Work and Employment, 35(2), 232–249, https://doi.org/10.1111/ntwe.12167 DOI: https://doi.org/10.1111/ntwe.12167
Felzmann, H., Fosch-Villaronga, E., Lutz, C. and Tamò-Larrieux, A. (2020). Towards Transparency by Design for Artificial Intelligence. Science and Engineering Ethics, 26(6), 3333–3361, https://doi.org/10.1007/s11948-020-00276-4 DOI: https://doi.org/10.1007/s11948-020-00276-4
Feuerriegel, S., Dolata, M. and Schwabe, G. (2020). Fair AI. Business & Information Systems Engineering, 62(4), 379–384, https://doi.org/10.1007/s12599-020-00650-3 DOI: https://doi.org/10.1007/s12599-020-00650-3
Fredman, S., du Toit, D., Graham, M., Howson, K., Heeks, R., van Belle, J.-P., Mungai, P. and Osiki, A. (2020). Thinking Out of the Box: Fair Work for Platform Workers. King’s Law Journal, 31(2), 236–249, https://doi.org/10.1080/09615768.2020.1794196 DOI: https://doi.org/10.1080/09615768.2020.1794196
Gallagher, C., Gregory, K., and Karabaliev, B. (2023). Digital worker inquiry and the critical potential of participatory worker data science for on-demand platform workers. New Technology, Work and Employment. https://doi.org/10.1111/ntwe.12286 DOI: https://doi.org/10.1111/ntwe.12286
Göksal, Ş. İ. and Solarte Vasquez, M. C. (2024). The Blockchain-Based Trustworthy Artificial Intelligence Supported by Stakeholders-In-The-Loop Model. Scientific Papers of the University of Pardubice, Series D: Faculty of Economics and Administration, 32(2), Article 2, https://doi.org/10.46585/sp32022083 DOI: https://doi.org/10.46585/sp32022083
Göksal, Ş. İ., Vasquez, M. C. S. and Chochia, A. (2025). The EU AI Act’s Alignment within the European Union’s Regulatory Framework on Artificial Intelligence. International and Comparative Law Review, 24(2), 25–53, https://doi.org/10.2478/iclr-2024-0017 DOI: https://doi.org/10.2478/iclr-2024-0017
Grgurev, I. and Radic, I. B. (2023). Indirect Discrimination of Platform Workers. Zbornik Pravnog Fakulteta u Zagrebu, 73(2–3), 233–260. DOI: https://doi.org/10.3935/zpfz.73.23.3
Hadzovic, S., Becirspahic, L. and Mrdovic, S. (2024). It’s time for artificial intelligence governance. Internet of Things, 27, 101292, https://doi.org/10.1016/j.iot.2024.101292 DOI: https://doi.org/10.1016/j.iot.2024.101292
Harmon, E. and Silberman, M. S. (2019). Rating Working Conditions on Digital Labor Platforms. Computer Supported Cooperative Work (CSCW), 28(5), 911–960, https://doi.org/10.1007/s10606-018-9313-5 DOI: https://doi.org/10.1007/s10606-018-9313-5
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M. and Hussain, A. (2024). Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognitive Computation, 16(1), 45–74, https://doi.org/10.1007/s12559-023-10179-8 DOI: https://doi.org/10.1007/s12559-023-10179-8
Hernandez, R. H. (Lindy), Song, Q., Kou, Y. and Gui, X. (2024). ‘At the end of the day, I am accountable’: Gig Workers’ Self-Tracking for Multi-Dimensional Accountability Management. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 1–20, https://doi.org/10.1145/3613904.3642151 DOI: https://doi.org/10.1145/3613904.3642151
Hohma, E. and Lütge, C. (2023). From Trustworthy Principles to a Trustworthy Development Process: The Need and Elements of Trusted Development of AI Systems. AI, 4(4), Article 4, https://doi.org/10.3390/ai4040046 DOI: https://doi.org/10.3390/ai4040046
Jahanbakhsh, F., Cranshaw, J., Counts, S., Lasecki, W. S. and Inkpen, K. (2020). An Experimental Study of Bias in Platform Worker Ratings: The Role of Performance Quality and Gender. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–13, https://doi.org/10.1145/3313831.3376860 DOI: https://doi.org/10.1145/3313831.3376860
Jang, H., Han, S. H. and Kim, J. H. (2020). User Perspectives on Blockchain Technology: User-Centered Evaluation and Design Strategies for DApps. IEEE Access, 8, 226213–226223, https://doi.org/10.1109/ACCESS.2020.3042822 DOI: https://doi.org/10.1109/ACCESS.2020.3042822
Joamets, K. and Vasquez, M. C. S. (2020). Regulatory Framework of the Research-Based Approach to Education in the EU. TalTech Journal of European Studies, 10(3), 109–136, https://doi.org/10.1515/bjes-2020-0024 DOI: https://doi.org/10.1515/bjes-2020-0024
Johnson, J. (2023). Automating the OODA loop in the age of intelligent machines: Reaffirming the role of humans in command-and-control decision-making in the digital age. Defence Studies, 23(1), 43–67, https://doi.org/10.1080/14702436.2022.2102486 DOI: https://doi.org/10.1080/14702436.2022.2102486
Joyce, S. and Stuart, M. (2021). Digitalised management, control and resistance in platform work: a labour process analysis. In: Haidar, J. and Keune, M. (ed.), Work and Labour Relations in Global Platform Capitalism (pp. 158–584). Cheltenham: Edward Elgar Publishing. DOI: https://doi.org/10.4337/9781802205138.00017
Kalamatiev, T. and Murdzev, N. (2022). The Notion of Digital Labour Platforms and the European Incentive for Improvement of the Working Conditions of the Platform Workers. Harmonius: Journal of Legal and Social Studies in South East Europe, 2022, 207–248.
Knell, M. (2021). The digital revolution and digitalized network society. Review of Evolutionary Political Economy, 2(1), 9–25, https://doi.org/10.1007/s43253-021-00037-4 DOI: https://doi.org/10.1007/s43253-021-00037-4
Kopalle, P. K., Pauwels, K., Akella, L. Y. and Gangwar, M. (2023). Dynamic pricing: Definition, implications for managers, and future research directions. Journal of Retailing, 99(4), 580–593, https://doi.org/10.1016/j.jretai.2023.11.003 DOI: https://doi.org/10.1016/j.jretai.2023.11.003
Lee, W. K. and Cui, Y. (2024). Should Gig Platforms Decentralize Dispute Resolution? Manufacturing & Service Operations Management, 26(2), 519–536, https://doi.org/10.1287/msom.2022.0398 DOI: https://doi.org/10.1287/msom.2022.0398
Li, N., Adepu, S., Kang, E. and Garlan, D. (2020). Explanations for human-on-the-loop: A probabilistic model checking approach. Proceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, 181–187, https://doi.org/10.1145/3387939.3391592 DOI: https://doi.org/10.1145/3387939.3391592
Lin, J., Ma, Z., Gomez, R., Nakamura, K., He, B. and Li, G. (2020). A Review on Interactive Reinforcement Learning From Human Social Feedback. IEEE Access, 8, 120757–120765, https://doi.org/10.1109/ACCESS.2020.3006254 DOI: https://doi.org/10.1109/ACCESS.2020.3006254
Lin, J., Tomlin, N., Andreas, J. and Eisner, J. (2024). Decision-Oriented Dialogue for Human-AI Collaboration. Transactions of the Association for Computational Linguistics, 12, 892–911, https://doi.org/10.1162/tacl_a_00679 DOI: https://doi.org/10.1162/tacl_a_00679
Lu, Y., Yang, M. M., Zhu, J., and Wang, Y. (2024). Dark side of algorithmic management on platform worker behaviors: A mixed-method study. Human Resource Management, 63(3), 477–498, https://doi.org/10.1002/hrm.22211 DOI: https://doi.org/10.1002/hrm.22211
Mettler, T. (2024). The connected workplace: Characteristics and social consequences of work surveillance in the age of datification, sensorization, and artificial intelligence. Journal of Information Technology, 39(3), 547–567, https://doi.org/10.1177/02683962231202535 DOI: https://doi.org/10.1177/02683962231202535
Mora-Cantallops, M., Sánchez-Alonso, S., García-Barriocanal, E. and Sicilia, M.-A. (2021). Traceability for Trustworthy AI: A Review of Models and Tools. Big Data and Cognitive Computing, 5(2), Article 2, https://doi.org/10.3390/bdcc5020020 DOI: https://doi.org/10.3390/bdcc5020020
Muller, Z. (2019). Algorithmic Harms to Workers in the Platform Economy: The Case of Uber. Columbia Journal of Law and Social Problems, 53(2).
Nassar, M., Salah, K., ur Rehman, M. H. and Svetinovic, D. (2020). Blockchain for explainable and trustworthy artificial intelligence. WIREs Data Mining and Knowledge Discovery, 10(1), 1340, https://doi.org/10.1002/widm.1340 DOI: https://doi.org/10.1002/widm.1340
Nunan, D., and Di Domenico, M. (2022). Value creation in an algorithmic world: Towards an ethics of dynamic pricing. Journal of Business Research, 150, 451–460, https://doi.org/10.1016/j.jbusres.2022.06.032 DOI: https://doi.org/10.1016/j.jbusres.2022.06.032
Park, S. and Ryoo, S. (2023). How Does Algorithm Control Affect Platform Workers’ Responses? Algorithm as a Digital Taylorism. Journal of Theoretical and Applied Electronic Commerce Research, 18(1), Article 1, https://doi.org/10.3390/jtaer18010015 DOI: https://doi.org/10.3390/jtaer18010015
Rahwan, I. (2018). Society-in-the-loop: Programming the algorithmic social contract. Ethics and Information Technology, 20(1), 5–14, https://doi.org/10.1007/s10676-017-9430-8 DOI: https://doi.org/10.1007/s10676-017-9430-8
Rosenblat, A., Levy, K. E. C., Barocas, S. and Hwang, T. (2017). Discriminating Tastes: Uber’s Customer Ratings as Vehicles for Workplace Discrimination. Policy & Internet, 9(3), 256–279, https://doi.org/10.1002/poi3.153 DOI: https://doi.org/10.1002/poi3.153
Rozas, D., Saldivar, J. and Zelickson, E. (2021). The platform belongs to those who work on it! Co-designing worker-centric task distribution models. Proceedings of the 17th International Symposium on Open Collaboration, 1–12, https://doi.org/10.1145/3479986.3479987 DOI: https://doi.org/10.1145/3479986.3479987
Schmager, S. and Sousa, S. (2021). A Toolkit to Enable the Design of Trustworthy AI. In: Stephanidis, C. et al. (eds.), HCI International 2021—Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence (pp. 536–555). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-90963-5_41 DOI: https://doi.org/10.1007/978-3-030-90963-5_41
Schmitz, A., Akila, M., Hecker, D., Poretschkin, M. and Wrobel, S. (2022). The why and how of trustworthy AI: An approach for systematic quality assurance when working with ML components. At - Automatisierungstechnik, 70(9), 793–804, https://doi.org/10.1515/auto-2022-0012 DOI: https://doi.org/10.1515/auto-2022-0012
Sharp, M., Dadfarnia, M., Sprock, T. and Thomas, D. (2021). Procedural Guide for System-Level Impact Evaluation of Industrial Artificial Intelligence-Driven Technologies: Application to Risk-Based Investment Analysis for Condition Monitoring Systems in Manufacturing. Journal of Manufacturing Science and Engineering, 144(7), 071008–071022, https://doi.org/10.1115/1.4053155 DOI: https://doi.org/10.1115/1.4053155
Shestakofsky, B., and Kelkar, S. (2020). Making platforms work: Relationship labor and the management of publics. Theory and Society, 49(5), 863–896. https://doi.org/10.1007/s11186-020-09407-z DOI: https://doi.org/10.1007/s11186-020-09407-z
Singh, S. K., Rathore, S. and Park, J. H. (2020). BlockIoTIntelligence: A Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence. Future Generation Computer Systems, 110, 721–743, https://doi.org/10.1016/j.future.2019.09.002 DOI: https://doi.org/10.1016/j.future.2019.09.002
Solarte-Vasquez, M. C. and Hietanen-Kunwald, P. (2020). Transaction design standards for the operationalisation of fairness and empowerment in proactive contracting. International and Comparative Law Review, 20(1), 180–200, https://doi.org/10.2478/iclr-2020-0008 DOI: https://doi.org/10.2478/iclr-2020-0008
Solarte-Vásquez, M. C. and Nyman-Metcalf, K. (2017). Smart Contracting: A Multidisciplinary and Proactive Approach for the EU Digital Single Market. TalTech Journal of European Studies, 7(2), 208–246, https://doi.org/10.1515/bjes-2017-0017 DOI: https://doi.org/10.1515/bjes-2017-0017
Tóth, Z., Caruana, R., Gruber, T. and Loebbecke, C. (2022). The Dawn of the AI Robots: Towards a New Framework of AI Robot Accountability. Journal of Business Ethics, 178(4), 895–916, https://doi.org/10.1007/s10551-022-05050-z DOI: https://doi.org/10.1007/s10551-022-05050-z
Toy, T. (2023). Transparency in AI. AI & SOCIETY, 39, 2841–2851, https://doi.org/10.1007/s00146-023-01786-y DOI: https://doi.org/10.1007/s00146-023-01786-y
Uzunca, B. and Kas, J. (2023). Automated governance mechanisms in digital labour platforms: How Uber nudges and sludges its drivers. Industry and Innovation, 30(6), 664–693, https://doi.org/10.1080/13662716.2022.2086450 DOI: https://doi.org/10.1080/13662716.2022.2086450
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(6), 898–914, https://doi.org/10.1080/1369118X.2017.1294194 DOI: https://doi.org/10.1080/1369118X.2017.1294194
Waldkirch, M., Bucher, E., Schou, P. K. and Grünwald, E. (2021). Controlled by the algorithm, coached by the crowd – how HRM activities take shape on digital work platforms in the gig economy. The International Journal of Human Resource Management, 32(12), 2643–2682, https://doi.org/10.1080/09585192.2021.1914129 DOI: https://doi.org/10.1080/09585192.2021.1914129
Wang, C., Chen, J. and Xie, P. (2022). Observation or interaction? Impact mechanisms of gig platform monitoring on gig workers’ cognitive work engagement. International Journal of Information Management, 67, 102548, https://doi.org/10.1016/j.ijinfomgt.2022.102548 DOI: https://doi.org/10.1016/j.ijinfomgt.2022.102548
Weiss, M. (2020). The platform economy: The main challenges for labour law. In: Méndez, L. M. (ed.), Regulating the Platform Economy. International Perspectives on New Forms of Work (pp. 11–20). Oxon: Routledge. DOI: https://doi.org/10.4324/9781003035008-1
Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T. and He, L. (2022). A survey of human-in-the-loop for machine learning. Future Generation Computer Systems, 135, 364–381, https://doi.org/10.1016/j.future.2022.05.014 DOI: https://doi.org/10.1016/j.future.2022.05.014
Yan, C., Zhu, H., Korolko, N. and Woodard, D. (2020). Dynamic pricing and matching in ride-hailing platforms. Naval Research Logistics (NRL), 67(8), 705–724, https://doi.org/10.1002/nav.21872 DOI: https://doi.org/10.1002/nav.21872
Zicari, R. V. et al. (2023). How to Assess Trustworthy AI in Practice. Digital Society, 2(3), 35, https://doi.org/10.1007/s44206-023-00063-1 DOI: https://doi.org/10.1007/s44206-023-00063-1
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