Is your research diverse or biased?


Scientific discovery is based on the novelty of questions you ask, that means if you want to discover something new, you have to ask a different question. The session Of mice, men and machines explores why diversity in research is crucial to make new discoveries.

Research depends on the questions asked, the models used, and the details considered. For this reason, it is important to reflect why certain variables are analysed or which aspects might play a role. An example for this is Machine Learning, which can be biased if the data the machines are trained with is biased too. For example, applications to diagnose skin cancer in medicine fail more often to recognize darker skin correctly because they are trained with pictures of white skin. Therefore, it is essential to consider aspects of diversity like gender, age, ethnicity, etc.

Also read the blogpost on Common Challenges in Neuroscience, AI, Medical Informatics, Robotics and New Insights with Diversity & Ethics if you want to know more about the role of diversity in neuroscience.

What about your research - is it diverse or biased? Do you know examples for diversity in your work in the HBP?