Developing discrimination machines? Cross-sectoral discussion on gender inclusive AI

Representatives from health, human rights, humanitarian and labour organizations participated in a cross-sectoral expert panel, moderated by Women at the Table’s Caitlin Kraft-Buchman. 

– If AI sometimes is perceived as a gold mine, gender is one of the canaries in that mine, said Norway’s Ambassador Tormod Endresen. – We all play roles in ensuring that instead of making discrimination machines, we develop tools for inclusion. 

UN Women’s Helene Molinier presented a new position paper released by the Coalition on Technology and Innovation for Gender Equality called Placing Gender Equality at the Heart of the Global Digital Compact. Molinier emphasized that AI can generate new forms of social vulnerabilities and now is the time to cover gaps in digital governance with the negotiation of the Global Digital Compact. Prioritizing gender perspectives in AI is instrumental not just because it's going to be beneficial for women and girls, but because it would set countries on a path for a more inclusive future for everyone, she said. 

Invisible in data sets  

Ishita Barua, co-founder and Chief Medical Officer of the start-up LIVV Health took an optimistic view and emphasized that AI tools have large potentials for providing meaningful service. But it is key that all individuals own their own data. 

– Data is at the heart of everything and has become the glue that binds us all together, said Steve MacFeely from WHO. He pointed out that good data governance is a prerequisite for good AI governance: – don’t get distracted by the noise, focus on the signals; don’t forget about the data! 

MacFeely highlighted that gender digital divides can arise when women do not own or have access to phones or are not registered at birth. This creates a data divide as they become invisible in data sets and therefore underrepresented in analyses and models. This ‘hard codes’ and reinforces gender stereotypes in the models that we are building. 

Exacerbating stereotypes 

The human rights implications include that as human prejudice and stereotypes are reproduced, access to essential services such as health services can be prevented, said OHCHR’s Scott Campbell. Discussions must be grounded in human rights frameworks with gender equality and non-discrimination as fundamental principles. 

Hovig Etyemezian and Rebeca Moreno Jimenez from UNHCR emphasized that misinformation and disinformation challenges become worse with auto-generation of content in humanitarian contexts. AI can exacerbate the stereotypes of refugee women and when technology goes unsupervised it can affect people who are already targeted. They also highlighted that working closely with refugees and displaced communities can certainly help design AI based solutions that can help solve complex problems whilst ensuring that do no harm principles are implemented.

From a labour perspective, ILO’s Mia Seppo pointed out that overall women are 2.5 times more affected by automatization than men, and more so in high income countries. A badly managed transition could disproportionately harm women. Investment is needed to create opportunities for women to receive training and benefit from new job opportunities which will likely to be technology-driven jobs in sectors where women are at the moment under-represented.  The gender pay gap of over 25% in one out of four countries for science, engineering and ICT professionals needs to be closed and pathway found from informal work to formalised technology-driven jobs, said Seppo.