There is growing evidence that having a diverse research workforce can help address complex multifactorial issues. As science is increasingly called upon to address complex socio-technical challenges, diversity and inclusion are a necessity.  At the same time, the inadvertent exclusion of a significant proportion of people from entering scientific careers significantly reduces the talent pool that academia and other sectors can tap into.

Policy measures are required to promote equity, diversity and inclusion in science. These need to extend beyond simply providing equal opportunities and ensure that those from under-represented or marginalised groups are enabled and supported to seize these opportunities.

‘Gender-blindness’ in research and innovation is both a symptom and a cause of the under-representation of women in science, particularly at senior levels. Research priorities and agendas are largely established by men, and research design and resultant innovations often fail to consider gender specificities. In extreme cases, the lack of attention to gender considerations when translating scientific knowledge into products or actions can actually be harmful to women.

Efforts are being made in many countries and institutions to ensure that the governance and decision-making processes for scientific research are gender neutral. Several countries have introduced legislation and/or dedicated funding mechanisms to promote the inclusion of gender considerations in research content.

The interaction between personal traits/perspectives, social/cultural factors and organisational factors (e.g. at institutional and system levels) is what determines structural inequality. Hence, these three variables need to be considered together to identify effective policy levers that address EDI in a systemic way.

There is a challenge in obtaining the quantitative data that may be necessary to fully apply an intersectional approach to EDI policies in science systems. Nevertheless, the conceptual framing of EDI in terms of intersectionality can help identify structural challenges and data gaps that need to be addressed to monitor policy impacts. 

There are a number of factors that are known to affect access, progression and retention in research careers, such as socio-economic background, ethnicity, including national minorities and indigenous peoples, migration, disability, geographic location, gender identity and sexual orientation, and age. In addition, different dimensions of individuals’ characteristics are not independent of each other, and intersectional approaches potentially lead to more effective policies to improve equity, diversity and inclusion. However, policy makers are currently lacking relevant data to be able to implement intersectional analyses, as data is frequently not available with respect to groups of interest.