Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers

By Min Kyung Lee, Daniel Kusbit, Evan Metsky, Laura A. Dabbish. International Conference on Human Factors in Computing Systems, 2015

863 citations recorded by the source corpus.

Subfields: Automotive Engineering, Safety Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/b5908258f894373da862ce970f9f44a485ce9bc1

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Citations in the corpus

Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy (2018, positive): “A far more effective means of control was the ‘algorithmic management’ enabled by platform-based rating and reputation systems (Lee et al., 2015; Rosenblat and Stark, 2016).”
Algorithmic Labor and Information Asymmetries: A Case Study of Uber’s Drivers (2016, neutral): “Many drivers express frustration and enthusiasm alike for surge pricing because its very dynamism is characteristically fickle and opaque, a finding supported by Lee et al. (2015, p. 1609).”
Algorithmic Control in Platform Food Delivery Work (2019, background): “While all food delivery platforms use algorithmic management to assign and evaluate work (Lee et al. 2015; Rosenblat 2018), they vary in the extent to which they constrain the freedoms—over schedules and activities—widely associated with gig work.”
Explanations as Mechanisms for Supporting Algorithmic Transparency (2018, background): “In others, users are motivated to become more knowledgeable about the algorithmic outputs so that they can create workarounds in an effort to avoid negative outcomes [32].”
Community building on crowdwork platforms: Autonomy and control of online workers? (2020, background): “Studies on the platform-mediated but locally fixed service work via Uber or Deliveroo show that, despite the rigid control, workers develop practices to outsmart the App-based management (Ivanova et al., 2018; Lee et al., 2015; Rosenblatt and Stark, 2016).”
Human Trust in Artificial Intelligence: Review of Empirical Research (2020, background): “Lee et al. (2015) suggested an additional explanation of the drivers ’ decrease of trust.”
Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models (2023, background): “…can contribute to the ongoing improvement and reﬁnement of AI models, ensuring that they remain fair, accurate, and relevant to users’ needs [154, 155, 156, 157] Openness and transparency Sharing information about the methodologies, data sources, and potential biases of AI models can enable…”
Ridesourcing systems: A framework and review (2019, methodology): “For example, Lee et al. (2015) explore the impact of software algorithms and data-driven management on drivers’ working practices in the context of Uber and Lyft.”
Artificial Intelligence in Human Resources Management: Challenges and a Path Forward (2019, background): “17 These are also being applied to regular employees now.”
Examining Crowd Work and Gig Work Through the Historical Lens of Piecework (2017, background): “…payment for this work may ultimately be determined by algorithms that fundamentally don’t understand people; the layers between us and our managers might eventually become “defective (simple, observable)” algorithms [9], just like those which already frustrate on–demand workers [94, 133, 72].”
The Future of Work in the ‘Sharing Economy’. Market Efficiency and Equitable Opportunities or Unfair Precarisation? (2016, positive): “These quantitative findings are corroborated by triangulating them with qualitative interviews (Lee et al., 2015) and analysis of online forums posts (Rosenblat & Stark, 2015).”
Platform-Capital’s ‘App-etite’ for Control: A Labour Process Analysis of Food-Delivery Work in Australia (2019, methodology): “A growing body of literature refers to ‘algorithmic management’ and ‘algorithmic control’ (Kenney and Zysman, 2016; Lee et al., 2015; Rosenblat and Stark, 2016; Schildt, 2017), signifying control systems increasingly based on ‘deep learning’ (i.”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “orting transparency, machine learning explanations also became a common approach to achieve transparency in many applications like social media, e-commerce and data-driven management of human workers [119,120,68]. The systems in these contexts generate explanations and describe the reasoning behind ”
Algorithmic Mediation in Group Decisions: Fairness Perceptions of Algorithmically Mediated vs. Discussion-Based Social Division (2017, background): “Recent studies have examined how people make sense of algorithmically-curated social media [16] and algorithmically-managed workplaces [30, 39].”