Estée Lauder employee performance assessments

Occurred: March 2022

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Estée Lauder has agreed an out-of-court settlement with three make-up artists who were sacked after taking an automated job application assessment, according to a BBC documentary. 

Having had to reapply for their positions, the women had their answers and expressions analysed by recruitment analysis software supplied by HireVue, with the results measured against other data about their job performance.

Estée Lauder subsidiary MAC had not previously warned any of the three women of performance issues, leading them to conclude they had been unfairly treated and to begin legal proceedings against the cosmetics company. 

An Estée Lauder spokesman said 'The company takes significant steps to counter unconscious bias in all our employment-related decisions. In the situation described, facial recognition accounted for well under 1% (0.25%) of employees’ overall assessment.'

'The company has teams who overlay objective performance-related data and other qualitative feedback, which accounted for the majority of the employment assessment, to make decisions on employment.'

'Thus, any suggestion that facial recognition technology played a decisive role in any employment-related decision at MAC UK & Ireland or the Estée Lauder Companies UK & Ireland is patently false,' he added.

'Deceptive', 'unfair', opaque algorithmic practices and audit

This is not the first time HireVue's recruitment technologies have drawn criticism

US privacy group EPIC filed a legal complaint (pdf) against HireVue alleging that it's opaque use of facial technologies and biometric data 'constitute unfair and deceptive trade practices.' 

And as Brookings Institution fellow Alex Engler pointed out in a Fast Company op-ed, HireVue mispresented an audit of one of its early career assessment algorithms.

Operator: Estée Lauder/MAC
Developer: HireVue
Country: UK
Sector: Cosmetics
Purpose: Assess employee performance
Technology: Facial recognition; Behavioural analysis
Issue: Accuracy/reliability; Fairness
Transparency: Governance; Black box