The Effects of Autonomy and Task meaning in Algorithmic Management of Crowdwork (bibtex)
by Toyoda, Yuushi, Lucas, Gale and Gratch, Jonathan
Abstract:
With the tremendous development of AI technologies, people will increasingly encounter software algorithms that supervise their work. Algorithmic management is the term for AI that performs the functions traditionally reserved for human managers (hiring, firing, providing evaluative feedback, and setting compensation). Although such algorithms indisputably perform management functions, they are often framed as support tools that facilitate worker autonomy. Perceptions of autonomy can enhance productivity, especially when the work holds intrinsic meaning for workers. But crowdwork often seems meaningless. More problematically, the meaning of the work must sometimes be obscured due to reasons of security or experimental control (when the workers serve as subjects in a psychological experiment). In this paper, we conduct an online experiment (N=560) to investigate how autonomy-perceptions and the meaningfulness of work interact to shape crowdworker motivation. As predicted, we find that workers are motivated when their work has meaning and algorithmic management is framed in a way that makes worker autonomy salient. However, when work holds no meaning, we find productivity is enhanced when algorithms are framed in a way that makes algorithm control salient. We also find evidence that providing meaning to the work can introduce systematic biases in crowdworker responses that could undermine accuracy in certain contexts.
Reference:
The Effects of Autonomy and Task meaning in Algorithmic Management of Crowdwork (Toyoda, Yuushi, Lucas, Gale and Gratch, Jonathan), In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, International Foundation for Autonomous Agents and Multiagent Systems,, 2020.
Bibtex Entry:
@inproceedings{toyoda_effects_2020,
	address = {Auckland, New Zealand},
	title = {The {Effects} of {Autonomy} and {Task} meaning in {Algorithmic} {Management} of {Crowdwork}},
	url = {https://dl.acm.org/doi/10.5555/3398761.3398923},
	abstract = {With the tremendous development of AI technologies, people will increasingly encounter software algorithms that supervise their work. Algorithmic management is the term for AI that performs the functions traditionally reserved for human managers (hiring, firing, providing evaluative feedback, and setting compensation). Although such algorithms indisputably perform management functions, they are often framed as support tools that facilitate worker autonomy. Perceptions of autonomy can enhance productivity, especially when the work holds intrinsic meaning for workers. But crowdwork often seems meaningless. More problematically, the meaning of the work must sometimes be obscured due to reasons of security or experimental control (when the workers serve as subjects in a psychological experiment). In this paper, we conduct an online experiment (N=560) to investigate how autonomy-perceptions and the meaningfulness of work interact to shape crowdworker motivation. As predicted, we find that workers are motivated when their work has meaning and algorithmic management is framed in a way that makes worker autonomy salient. However, when work holds no meaning, we find productivity is enhanced when algorithms are framed in a way that makes algorithm control salient. We also find evidence that providing meaning to the work can introduce systematic biases in crowdworker responses that could undermine accuracy in certain contexts.},
	booktitle = {Proceedings of the 19th {International} {Conference} on {Autonomous} {Agents} and {MultiAgent} {Systems}},
	publisher = {International Foundation for Autonomous Agents and Multiagent Systems,},
	author = {Toyoda, Yuushi and Lucas, Gale and Gratch, Jonathan},
	month = may,
	year = {2020},
	keywords = {Virtual Humans, ARO-Coop},
	pages = {1404--1412}
}
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