Chapter 3 Platforms at Work: Automated Hiring Platforms and Other New Intermediaries in the Organization of Work

Ajunwa, Ifeoma; Greene, Daniel

doi:10.1108/s0277-283320190000033005

Cited by 56 publications

(75 citation statements)

References 49 publications

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“…systems that are more likely to recommend applicants from certain demographic groups [4] or risk assessment systems that solidify and exacerbate patterns of racism and classism in criminal justice [6]. To mitigate such harms, many public-and private-sector organizations have published high-level principles intended to guide the ethical development and deployment of AI systems (e.g., [7,35,49,56,73]).…”

mentioning

confidence: 99%

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Madaio

Stark

Vaughan

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

277

193

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Many organizations have published principles intended to guide the ethical development and deployment of AI systems; however, their abstract nature makes them difficult to operationalize. Some organizations have therefore produced AI ethics checklists, as well as checklists for more specific concepts, such as fairness, as applied to AI systems. But unless checklists are grounded in practitioners' needs, they may be misused. To understand the role of checklists in AI ethics, we conducted an iterative co-design process with 48 practitioners, focusing on fairness. We co-designed an AI fairness checklist and identified desiderata and concerns for AI fairness checklists in general. We found that AI fairness checklists could provide organizational infrastructure for formalizing ad-hoc processes and empowering individual advocates. We discuss aspects of organizational culture that may impact the efficacy of such checklists, and highlight future research directions.

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mentioning

confidence: 99%

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Madaio

Stark

Vaughan

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

277

193

View full text Add to dashboard Cite

show abstract

“…Moreover, in validating our construct in its nomological net, we also aim to shed light on how perceptions of algorithmic autonomy-support can affect job satisfaction and perceived organizational support. These contributions are important, given that many scholars have suggested that algorithmic management and the "platformization" of work have contributed to a lack of job satisfaction and diminished feelings of organization support in the gig-economy [4,11].…”

Section: Expected Contributionsmentioning

confidence: 99%

“…With the emerging importance of the algorithm, a key question is how to make better human-centered algorithms that are useful, fair, and accepted by the various parties affected by them [34]. Yet as sociotechnical ecosystems powered by algorithms [4,34], digital labour platforms have continued to raise concerns among the public and policy makers about the implications of algorithmic management and workplace surveillance on workers' agency and autonomy, their well-being, motivation, and performance [4,6,7,10].…”

Section: Expected Contributionsmentioning

confidence: 99%

“…The application of algorithms to the management of workers on digital labour platforms is a sociotechnical phenomenon known as algorithmic management, or management-by-platform in the gig-economy [4]. Whilst it is tempting to consider algorithmic management as akin to the use of decision-support systems by managers, this set of managerial practices constitutes more than just a repackaging of pre-existing technical competencies and resources in organizations.…”

Section: Introductionmentioning

confidence: 99%

“…While research into users' perceptions of algorithms is starting to grow, currently, there seems to be no instruments that can be used to measure platform workers' perceptions of algorithmic autonomy-support. Moreover, the gig-economy literature is rife with conflicting accounts of autonomy and empowerment versus exploitation and marginalization [4,10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unlocking Perceived Algorithmic Autonomy-Support: Scale Development and Validation

Jabagi¹,

Croteau²,

Audebrand³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Platform workers' autonomy and agency are recurring themes in the study of the gig-economy where narratives purporting workers' autonomy and empowerment conflict with those alleging the control and marginalization of workers. While it has been said that promoting workers' agency can threaten the valuation of platform-based companies, the benefits of supporting workers' autonomy in traditional organizations are well-established. To understand such inconsistencies, it is necessary to measure perceptions of autonomysupport; yet, no validated instruments exist that can be used to measure workers' perceptions of algorithmic autonomy-support. To address this gap, we draw on the Theory of Self-Determination to reconceptualize the notion of autonomy-support for the technoorganizational phenomenon of algorithmically managed platform work. In doing so, we introduce a new construct, namely: Perceived Algorithmic Autonomy Support (PAAS). In this work-in-progress paper, we describe our current work in developing and validating a theoretically-based measure for PAAS. Preliminary results are provided.

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“I Don't Want Someone to Watch Me While I'm Working”: Gendered Views of Facial Recognition Technology in Workplace Surveillance

Stark

Stanhaus

Anthony

2020

Asso for Info Science & Tech

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Employers are increasingly using information and communication technologies to monitor employees. Such workplace surveillance is extensive in the United States, but its experience and potential consequences differ across groups based on gender. We thus sought to identify whether self‐reported male and female employees differ in the extent to which they find the use of workplace cameras equipped with facial recognition technology (FRT) acceptable, and examine the role of privacy attitudes more generally in mediating views on workplace surveillance. Using data from a nationally representative survey conducted by the Pew Research Center, we find that women are much less likely than men to approve of the use of cameras using FRT in the workplace. We then further explore whether men and women think differently about privacy, and if perceptions of privacy moderate the relationship between gender and approval of workplace surveillance. Finally, we consider the implications of these findings for privacy and surveillance via embedded technologies, and how the consequences of surveillance and technologies like FRT may be gendered. Note: We recognize evaluations based on a binary definition of gender are invariably partial and exclusionary. As we note in our discussion of the study's limitations, we were constrained by the survey categories provided by Pew.

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Chapter 3 Platforms at Work: Automated Hiring Platforms and Other New Intermediaries in the Organization of Work

Cited by 56 publications

References 49 publications

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI

Unlocking Perceived Algorithmic Autonomy-Support: Scale Development and Validation

“I Don't Want Someone to Watch Me While I'm Working”: Gendered Views of Facial Recognition Technology in Workplace Surveillance

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