This article is part of an ongoing blog series, titled Inequality in STEM: a Dive Into the Data. In this series, we cover recent research exploring and quantifying inequality in STEM. We'll discuss different aspects of inequality, including barriers to career advancement and a chilly social climate, as well as the efficacy of various interventions to combat bias. Our goal with these pieces is to provide clear summaries of the data related to bias in STEM, giving scientific evidence to back up the personal experiences of URMs in STEM fields.
From courtroom sentencing to graduate school admissions, from voting on the president to choosing who to sit next to on the bus, our biases play a role in the decisions we make and the actions we take. Explicit biases are attitudes and stereotypes that we are aware of and can consciously describe, while implicit biases are those that we are unconscious of. Both shape our thoughts and how we act on them.
Implicit biases, whether favorable or harmful, are hard to pin down and address, yet they shape the attitudes that we have towards people of other races, ethnicities, sexual orientations, genders, or any other group. To measure this implicit bias, Greenwald, McGhee, and Schwartz developed a metric assessed by what they named The Implicit Association Test (IAT) , which is now widely used to identify different implicit biases towards various groups. Many research efforts have attempted to implement strategies that reduce the implicit bias of particular groups against others, as assessed by this test. These are typically ineffective . Most interventions designed to combat implicit bias do not hold up over time or simply do not reduce bias .
Amidst these unsuccessful interventions, Mel Slater’s group at the University of Barcelona found one method that did reduce implicit bias of white people against black people and whose effects persisted for an unusually long time .
The authors had previously found a system to evaluate and reduce implicit bias in this context5. It was done by placing white women in a virtual reality setup where they inhabited the virtual body of either a white or a black woman. Covered in motion-capture devices and wearing a 3D virtual reality headset, the subjects could move in the real world and their virtual self would move in the exact same way. The subjects were given an IAT before and after the virtual embodiment. Only the women who were placed into black bodies had a reduction in their IAT scores.
In a new study, the authors aimed to evaluate whether multiple embodiments would further reduce bias and also attempted to evaluate if the effects lasted longer immediately following the intervention. They repeated their earlier work but had some subjects undergo one, two, or three virtual embodiments and gave them the IAT a week after their final virtual session. In the new experiment, participants practiced tai chi with a virtual instructor, a task designed to reinforce the participants’ perceived synchrony in order to increase feelings of ownership over their virtual bodies.
The authors’ results expanded on their already surprising findings. They first replicated all of their previous results, and then asked questions regarding the benefit of multiple virtual sessions. It turns out that the number of sessions didn’t have a significant effect on the biases of white women against black people. However, as long as the subjects had at least one virtual session embodying a black person, their IAT scores were still decreased a week after the intervention! The authors had been searching for a way to increase their intervention’s effect on various groups and didn’t succeed in that, but they did find that their intervention had some of the longest-lasting effects on IAT scores that have ever been observed.
By replicating their earlier results in a slightly different context and realizing the long-lasting nature of their intervention, the authors put forth one of the most effective interventions to date on reducing implicit bias as measured by the IAT. There’s more work to do - their sample only included white women. By not testing men or other races, they neglected to address the generalizability of their findings. They also did not link the IAT results to other bias metrics that may be important in the real world. Future studies can expand into other races and to men, but as a whole, the authors’ research has shown we can make tangible progress towards slaying the elusive beast that is implicit bias. All that it took was 21st century virtual reality, motion capture, and a willingness to change people’s race.
1. Greenwald, A. G., Mcghee, D. E., & Schwartz, J. L. K. (1998). Measuring Individual Differences in Implicit Cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74(6), 1464–1480. Retrieved from http://psycnet.apa.org/fulltext/1998-02892-004.pdf
2. Lai, C. K., Marini, M., Lehr, S. A., Cerruti, C., Shin, J. E. L., Joy-Gaba, J. A., … Nosek, B. A. (2014). Reducing implicit racial preferences: I. A comparative investigation of 17 interventions. Journal of Experimental Psychology: General, 143(4), 1765–1785. https://doi.org/10.1037/a0036260
3. Lai, C. K., Skinner, A. L., Cooley, E., Murrar, S., Brauer, M., Devos, T., … Nosek, B. A. (2016). Reducing implicit racial preferences: II. Intervention effectiveness across time. Journal of Experimental Psychology: General, 145(8), 1001–1016. https://doi.org/10.1037/xge0000179
4. Banakou, D., Hanumanthu, P. D., & Slater, M. (2016). Virtual Embodiment of White People in a Black Virtual Body Leads to a Sustained Reduction in Their Implicit Racial Bias. Frontiers in Human Neuroscience, 10, 601. http://doi.org/10.3389/fnhum.2016.00601
5. Peck, T. C., Seinfeld, S., Aglioti, S. M., & Slater, M. (2013). Putting yourself in the skin of a black avatar reduces implicit racial bias. Consciousness and Cognition, 22(3), 779–787. https://doi.org/10.1016/J.CONCOG.2013.04.016