Research has shown that, within industry, diverse teams yield better results than homogeneous ones [1,2]. This is particularly true within creative or enterprising endeavors [3,4], which is characteristic of both research and entrepreneurship in the AI Med field.
A well-rounded team representing multiple different backgrounds and perspectives will find better solutions to problems and will generate more novel ideas than a narrowly constructed team. In his landmark study on cognitive diversity , Scott Page explains that when people think alike, they will get stuck at the same locally optimal solutions.
Sheer intelligence is not as powerful as having multiple points of view to find new and better solutions and to help correct for human error and biases. As Nobel Prize-winning author and father of behavioral economics, Daniel Kahneman, put it: “We can be blind to the obvious, and we are also blind to our blindness .”
The field of AI, in particular, has been identified as requiring diverse participants in order to create the best models by avoiding problems with biased training data .
Gender diversity is not the only dimension of diversity required to achieve a well-rounded team. A group that exhibits cognitive diversity will likely also include people of different nationalities, ethnicities, religions, ages, and political views – generally speaking, people with different experiences and preconceptions.
See More on the topic of diversity:
Part 1: The Gender Imabalance in AIMed
or see the whole feature in AIMed Magazine issue 04 here.
 “Innovative Potential: Men and Women in Teams.” The Lehman Brothers Centre for Women in Business at the London Business School, 2007. Retrieved from https://www.lnds.net/blog/images/2013/09/grattonreportinnovative_potential_nov_2007.pdf.
 Padnos, Cindy. “High Performance Entrepreneurs: Women in High Tech.” Iluminate Ventures, 2010. Retrieved from http://www.illuminate.com/whitepaper.
 Dezso and Ross. “Does female representation in top management improve firm performance? A panel data investigation.” Strategic Management Journal, 33(9), 1072-1089, 2012. Retrieved from: http://dx.doi.org/10.1002/smj.1955.
 Desvaux, Devillard-Hoellinger, and Meaney. “A business case for women.” The McKinsey Quarterly, 2008.. Retrieved from http://www.cwf.ch/uploads/press/ABusinessCaseForWomen.pdf.
 Page, Scott E. “The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies.” Princeton University Press, 2007.
 Kahneman, Daniel. Thinking, Fast and Slow. New York: Farrar, Straus and Giroux, 2011.
 Several high-profile examples of machine learning models created from biased training data have proven embarrassing for companies like Google whose photo recognition algorithm misidentified African-American people as gorillas and Apple whose Siri was unaware of women’s health issues. See https://www.wired.com/story/when-it-comes-to-gorillas-google-photos-remains-blind/ and https://www.huffingtonpost.com/cecile-richards/what-siris-blind-spot-on_b_1125377.html.
Article by: Crystal Valentine, PhD,
Crystal served as the VP of Technology Strategy at MapR Technologies for the past two years. She has nearly two decades’ experience in big data and machine learning research and practice. A former professor of computer science at Amherst College, she is the author of several academic publications in the areas of big data, algorithms, computational biology, and high-performance computing, and she holds a patent for Extreme Virtual Memory. Dr. Valentine was named a Person to Watch in 2018 by Datanami and was awarded the Silver Stevie Award in 2017 for Female Executive of the Year in the computer software category. She is a member of the Forbes Tech Council and is a frequent contributor to industry journals. Dr. Valentine was a Fulbright Scholar to Italy.