Universities around the world are keen to build more diverse teams of scientists, reflecting the wider world and bringing fresh perspectives to research. Often, efforts to achieve this focus on tackling bias, which are hidden assumptions or stereotypes, that might consciously or unconsciously influence who gets hired or promoted. Many universities around the world use initiatives like mandatory implicit bias training to ensure the race is fair by checking if the starting blocks are level for everyone. This, of course, assumes that unfair biases are a key reason why women and ethnic or racial minorities are underrepresented in science. But what do scientists themselves think? Do they agree that bias is the main hurdle? A recent study decided to ask them directly.
Researchers from Georgia State University and Catholic University of America in the USA surveyed nearly 2,000 physicists and biologists working at universities and research institutes in four countries: the United States, the United Kingdom, Italy, and India. They asked these scientists how much they agreed with the statement that insufficient gender and ethnic/racial diversity in science "reflects bias (whether implicit or explicit) in hiring and promotion."
Using a scale from 'completely disagree' to 'completely agree', they gathered a wide range of opinions. Then, using statistical tools, they sorted through all the answers and identified any clear patterns to find what factors influenced a scientist's view. They found that while, on average, scientists tended to ‘somewhat agree’ that bias was a factor, there wasn't universal consensus. In fact, only about one in four completely agreed, while nearly one in five disagreed or were neutral.
The study revealed some differences in thinking among minority groups and others. Women scientists were significantly more likely than men to believe bias was a problem. Similarly, scientists from Black/African/Caribbean backgrounds showed the strongest tendency to attribute underrepresentation to bias compared to their White colleagues. However, the number of respondents from this group was small. Scientists identifying as South Asian also perceived bias as more of a factor than White scientists did.
Political views mattered too: scientists leaning towards the liberal end of the spectrum were much more likely to agree that bias was an issue compared to their more conservative peers. Interestingly, where a scientist worked also played a role. Compared to scientists in the US, those in India and Italy were significantly less likely to see bias as the explanation for a lack of diversity. Perhaps surprisingly, the researchers didn't find a significant difference between biologists and physicists in their views. Junior faculty members also stood out as being particularly likely to believe bias affected hiring and promotion.
Earlier work has shown that bias exists in hiring and that certain training programs have mixed results, sometimes even backfiring, making people resentful. This study systematically maps out the perceptions of the people involved in the hiring process – the faculty. It gives us a clearer picture, across different countries and scientific fields, of how much buy-in there is for the idea that bias is the primary obstacle.
However, the study does have limitations. A key one is that the survey question combined gender and race/ethnicity. Someone might strongly dislike one but not the other, making their simple 'yes' or 'no' hard to interpret fully. Also, the survey response rate varied between countries, and the sample sizes for some minority groups were small.
Understanding these varying perceptions is crucial. Scientists act as gatekeepers for their professions through hiring and promotion committees. If many scientists are skeptical that bias is the main problem holding back diversity, then simply mandating bias training might be insufficient. It could not only be ineffective, but even breed resistance, potentially harming the goal of increasing diversity.
The study suggests that university leaders need more nuanced strategies. Instead of a one-size-fits-all approach, they might need to engage faculty in open discussions, use data transparently to show where disparities exist, and tailor initiatives that acknowledge the different viewpoints shaped by personal identity, political leanings, and national context. Ultimately, acknowledging this complex landscape of beliefs is a vital step towards finding more effective ways to build a truly inclusive and excellent scientific community that benefits everyone.
This research article was written with the help of generative AI and edited by an editor at Research Matters.