Algorithmic Management and Algorithmic Competencies: Understanding and Appropriating Algorithms in Gig Work

Data-driven algorithms now enable digital labor platforms to automatically manage transactions between thousands of gig workers and service recipients. Recent research on algorithmic management outlines information asymmetries, which make it difficult for gig workers to gain control over their work due a lack of understanding how algorithms on digital labor platforms make important decisions such as assigning work and evaluating workers. By building on an empirical study of Upwork users, we make it clear that users are not passive recipients of algorithmic management. We explain how workers make sense of different automated features of the Upwork platform, developing a literacy for understanding and working with algorithms. We also highlight the ways through which workers may use this knowledge of algorithms to work around or manipulate them to retain some professional autonomy while working through the platform.

[1]  Beth A. Bechky,et al.  The Changing Nature of Work: Careers, Identities, and Work Lives in the 21st Century , 2017 .

[2]  Shagun Jhaver,et al.  Algorithmic Anxiety and Coping Strategies of Airbnb Hosts , 2018, CHI.

[3]  C. Lutz,et al.  Algorithmic Management in the Sharing Economy , 2018 .

[4]  Karrie Karahalios,et al.  "Be Careful; Things Can Be Worse than They Appear": Understanding Biased Algorithms and Users' Behavior Around Them in Rating Platforms , 2017, ICWSM.

[5]  Pawel Popiel,et al.  “Boundaryless” in the creative economy: assessing freelancing on Upwork , 2017 .

[6]  B. Bergvall-Kåreborn,et al.  A Typology of Crowdwork Platforms , 2019 .

[7]  Tina Chien-Wen Yuan,et al.  Using Stakeholder Theory to Examine Drivers' Stake in Uber , 2018, CHI.

[8]  Mehrab Bin Morshed,et al.  Uber in Bangladesh , 2018, Proc. ACM Hum. Comput. Interact..

[9]  Christoph Lutz,et al.  Collective Action and Provider Classification in the Sharing Economy , 2018, New Technology, Work and Employment.

[10]  Joseph A. Maxwell,et al.  Qualitative Research Design: An Interactive Approach , 1996 .

[11]  Nicole Immorlica,et al.  Power Struggles in the Digital Economy: Platforms, Workers, and Markets , 2018, CSCW Companion.

[12]  David García,et al.  Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr , 2017, CSCW.

[13]  Laura A. Dabbish,et al.  Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers , 2015, CHI.

[14]  Aaron Shapiro,et al.  Between autonomy and control: Strategies of arbitrage in the “on-demand” economy , 2018, New Media Soc..

[15]  Vicki Smith,et al.  Consumers' Reports: Management by Customers in a Changing Economy , 1991 .

[16]  Mike Ananny,et al.  Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability , 2018, New Media Soc..

[17]  Daryl D. Green Fueling the Gig Economy: A Case Study Evaluation of Upwork.com , 2018 .

[18]  Jacki O'Neill,et al.  Algorithms at Work: Empirical Diversity, Analytic Vocabularies, Design Implications , 2016, CSCW Companion.

[19]  Jean-Michel Hoc,et al.  The role of algorithm and result comprehensibility of automated scheduling on complacency , 2008 .

[20]  Jacki O'Neill,et al.  Turking in a Global Labour Market , 2016, Computer Supported Cooperative Work (CSCW).

[21]  Paul Dourish,et al.  Standing Out from the Crowd: Emotional Labor, Body Labor, and Temporal Labor in Ridesharing , 2016, CSCW.

[22]  Michael S. Bernstein,et al.  Examining Crowd Work and Gig Work Through the Historical Lens of Piecework , 2017, CHI.

[23]  Min Kyung Lee Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management , 2018, Big Data Soc..

[24]  Michel Avital,et al.  Why Take the Risk? Motivations of Highly Skilled Workers to Participate in Crowdworking Platforms , 2018, ICIS.

[25]  B. Glaser Theoretical Sensitivity: Advances in the Methodology of Grounded Theory , 1978 .

[26]  Arne L. Kalleberg,et al.  Good Jobs, Bad Jobs in the Gig Economy , 2016 .

[27]  Sue Newell,et al.  Datification and its human, organizational and societal effects: The strategic opportunities and challenges of algorithmic decision-making , 2017, J. Strateg. Inf. Syst..

[28]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[29]  Henri Schildt,et al.  Big data and organizational design – the brave new world of algorithmic management and computer augmented transparency , 2017 .

[30]  Mark Graham,et al.  Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy , 2018, Work, employment & society : a journal of the British Sociological Association.

[31]  Mohammad Hossein Jarrahi,et al.  The sharing economy and digital platforms: A review and research agenda , 2018, Int. J. Inf. Manag..

[32]  Sue Newell,et al.  Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long-term societal effects of 'datification' , 2015, J. Strateg. Inf. Syst..

[33]  A. Strauss,et al.  Basics of qualitative research: Grounded theory procedures and techniques. , 1992 .

[34]  Mohammad Hossein Jarrahi,et al.  The Gig Economy and Information Infrastructure , 2017, Proc. ACM Hum. Comput. Interact..

[35]  Alex Rosenblat,et al.  Algorithmic Labor and Information Asymmetries: A Case Study of Uber’s Drivers , 2016 .

[36]  Lior Zalmanson,et al.  Hands on the Wheel: Navigating Algorithmic Management and Uber Drivers' Autonomy , 2017, ICIS.