Addressing Implicit Bias among Women Statisticians and Data Scientists
The Kirwan Institute for the Study of Race and Ethnicity at the Ohio State University defines implicit bias as:
The attitudes or stereotypes that affect our understanding, actions, and decisions in an unconscious manner. These biases, which encompass both favorable and unfavorable assessments, are activated involuntarily and without an individual’s awareness or intentional control. Residing deep in the subconscious, these biases are different from known biases that individuals may choose to conceal for the purposes of social and/or political correctness. Rather, implicit biases are not accessible through introspection.
While implicit biases play a significant role and impact in gender relations in the statistics and data science communities, a variety of other factors differentiate women in these fields so that unconscious characterizations may produce implicit biases. These factors motivate this article.
One such classification that potentially lends itself to introducing implicit bias is career choice (academia, government, or industry) and trajectory. Students, particularly at the graduate level, are naturally influenced by their advisors, other departmental faculty, or other academics with whom they interact. Those exchanges can influence a student’s perception about career opportunities outside academia, perhaps implying such opportunities to be less appealing or fulfilling in some way.Some content is only viewable by Chance Subscribers