Research

Potocka-Sionek, N. & Aloisi, A. (2021)

International Journal of Comparative Labour Law and Industrial Relations, Vol. 37, No 1

Aloisi, A. (2020)

Italian Labour Law E-Journal, Vol. 13, No 2, 67-87

Aloisi, A. & De Stefano, V. (2020)

International Labour Review, Vol. 159, No. 1, pp. 47-69

Aloisi, A. & Gramano, E. (2020)

Comparative Labor Law & Policy Journal, Vol. 41, No. 1, pp. 101-127.

Aloisi, A. (2020)

Forthcoming in e-book “Quaderni del Premio Giorgio Rota”

Aloisi, A. & Gramano, E. (2019)

Pulignano V. & Hendrickx F. (Eds.), Employment Relations for the 21st Century, Bulletin of Comparative Labour Relations, Vol. 107

Press coverage

Jonathan Keane

The Sifted, 9 August 2021

Antonio Aloisi, an assistant professor at IE Law School in Madrid specialising in tech and labour regulation, tells Sifted that these cases are the next evolution in creating new rules and regulations for the platform economy.

“My intuition is that we are entering a second age of litigation in the field of platform work. In the last five years many workers have been bringing claims before courts all over Europe to challenge the legal consideration `{`of workers’ status`}`.”

Recent cases, Aloisi says, are part of rising scepticism of algorithmic control in the workplace. In the gig economy, those questions can be particularly fraught as they can be the difference between getting work or not.

“The algorithm is a way to exert control and organisation,” he says. “There is a direct impact on not only the possibility of being hired but also on the remuneration that the worker is able to get.”

Leonie Cater, Melissa Heikkilä and Clothilde Goujard

Politico Europe, 28 July 2021

But speaking to Decoded, Madrid-based labor law and AI expert Antonio Aloisi said algorithmic transparency doesn’t have to be complicated.

Rather than getting bogged down in source code and jargon, he stressed the importance of explainability, pointing to a “percentage-based” approach. Online delivery couriers, for example, could ascertain the weighting of the metrics used to rank them internally: they could know whether customer reviews constitute 20 percent or 5 percent of algorithmic decisions like order allocations.

“That’s how predictability and reliability of the algorithm is increased, so the workers are aware of the consequences of their behavior,” he added. “This is not about learning the code, but understanding the consequences.”

In Aloisi’s view, the EU’s data protection laws — which ban automated decision-making processes legally affecting a data subject (such as a loss of profitability) — combined with the rider law could increase not only the transparency but also the accuracy and contestability of decisions taken by gig work and social media companies.

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