Lorena Fernández participates in the European Commission report "Gendered Innovations 2: How inclusive analysis contributes to research and innovation", with other renowned international experts


25 November 2020

Bilbao Campus

On 25 November, the European Commission Directorate-General for Research and Innovation published the Policy Review “Gendered Innovations 2: How inclusive analysis contributes to research and innovation”, written by 24 international experts, including Lorena Fernández, Director of Digital Identity at the University of Deusto and member of the Gender Interdisciplinary Research Platform.

The expert group was chaired by Professor Londa Schiebinger (Stanford University, United States), who has been interviewed in Nature about the project.

This work seeks to support the integration of the gender dimension in EU Research and Innovation under the upcoming framework programme Horizon Europe, to ensure Europe's leadership in science and technology and to support its inclusive growth. To this aim, the report includes methodological tools for intersectional, gender and sex analysis, to improve all research stages: from the establishment of research priorities, the identification of the problem, the collection and analysis of data, to the evaluation and dissemination of results. It also includes case studies where the impact of not incorporating this gender perspective is made visible in areas such as health, artificial intelligence and robotics, energy, transport, marine science and climate change, urban planning, agriculture, fair taxes and companies funding, as well as the COVID-19 pandemic.

Lorena Fernández has drafted the chapter "Facial recognition: analysing gender and intersectionality in machine learning" where she analyses how facial recognition systems have more problems to identify dark-skinned women, the impact of wearing makeup or the problems of this technology when someone is transitioning gender, among other examples. She has also participated in the design of a methodology to analyse gender and intersectionality in machine learning (machine learning - artificial intelligence).

You can consult the full report at this webpage.