Format hybride – Pour participer en présentiel, merci de vous inscrire auprès d’Olfa Mzita – mzita@hec.fr
- Intervenante : Elizabeth Brown (Bentley University)
- Participant: Matteo Winkler (HEC Paris)
- Publication: consultable ici
- Résumé: As biometric monitoring becomes increasingly common in workplace wellness programs, there are three reasons to believe that women will suffer disproportionately from the data collection associated with it. First, many forms of biometric monitoring are subject to gender bias, among other potential biases, because of assumptions inherent in the design and algorithms interpreting the collected data. Second, the expansion of femtech in particular creates a gender-imbalanced data source that may feed into existing workplace biases against women unless more effective safeguards emerge. Finally, many femtech platforms encourage the kind of information sharing that may reduce women’s reasonable expectations of privacy, especially with regard to fertility data, thus increasing the risk of health data privacy invasion. This triple threat to female workers may be offset somewhat by the benefits of health data collection at work and may be remedied at least in part by both legislative and non-legislative means. The current trend toward greater health data collection in the wake of COVID-19 should provoke a reexamination of how employers collect and analyze health data to reduce the impact of these new gender bias drivers.
