Reputational ranking of platform workers: on the gender discriminatory implications of users’ feedback
One distinctive feature of on-demand platform work is the ease of outsourcing the service’s quality assessment to clients. To set minimum quality standards of service, platforms aggregate users’ feedback and form a reputational score for workers, which, often combined with other criteria, directly i...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
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University of Bologna
2024-12-01
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| Series: | Labour & Law Issues |
| Subjects: | |
| Online Access: | https://labourlaw.unibo.it/article/view/20890 |
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| Summary: | One distinctive feature of on-demand platform work is the ease of outsourcing the service’s quality assessment to clients. To set minimum quality standards of service, platforms aggregate users’ feedback and form a reputational score for workers, which, often combined with other criteria, directly impacts their job opportunities and working conditions. The article questions whether this practice may constitute discrimination under the European Union equality law. It first argues that the use of reputational ranking systems does not qualify as an exception to the prohibition of differential treatment based on a protected characteristic linked to the genuine and determining occupational requirement. Indeed, having a certain reputational score does not constitute a requirement objectively dictated by the nature of the activity or the context in which it is carried out. Referring then to the possibility of the particular disadvantage of a reputational ranking being objectively justified, it is argued that, although assessing workers’ performance may likely be considered a legitimate aim, neither the appropriateness nor the necessity of the measure for achieving that aim should be assumed, and customer ratings, indeed, would probably not meet the proportionality requirement. To conclude, the article highlights how the issue of discriminatory reputational ranking exemplifies a broader question concerning algorithmic accountability, namely whether algorithms can be held accountable for perpetuating existing social biases, especially one as pervasive as sexism, and it criticizes the use of that narrative to frame the issue. |
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| ISSN: | 2421-2695 |