Reducing Bias in Academic Job Searches Barbara F. Walter Adapted from a presentation by Professor Jen Burney, UCSD, Faculty Equity Advisor

Goals: 1. To point out implicit biases in hiring process. 2. To discuss ways to help change the structure of the hiring process to reduce the effects of these biases.

We all have “blind spots” (schemas) that lead to bias.

The trick is to set up structures that reduce these biases.

What are Schemas? Patterns of thought that: ► Help us organize social information ► Allow us to make mental shortcuts ► Widely culturally shared Both men and women hold them about gender Both men and women hold them about gender Both whites and people of color hold them about race Both whites and people of color hold them about race ► Valian: Why so Slow? The Advancement of Women. MIT Press, ► Kahneman: Thinking, Fast and Slow, ►

Schemas Affect: (1) Letters of Recommendation  Letters for women shorter, provide “minimal assurance”  Women faculty described as caring, refreshing, diligent, male faculty praised for research brilliance and career achievements. Trix and Psenka, Discourse & Society, 2003.

Schemas Affect: (2) Evaluation of CVs ► White names favored over African-American names for interview callback (3:2) ► Bertrand & Mullainathan, American Economic Review, ► “Brian” preferred over “Karen” in academic hiring (2:1) ► Steinpreis, Anders, & Ritzke, Sex Roles, ► Female post-doc applicants had to be 2.5 times more productive to rate equally in scientific competence as the average male applicant. ► Wenneras & Wold, Nature, 1997.

Schemas Affect: (3) Perceptions of Competence ► Blind symphony auditions increased probability of women advancing from preliminary rounds by 50% ► Goldin & Rouse, American Economic Review, ► Science faculty selected male version of file for lab manager position more frequently, rated male as more competent, offered higher starting salary, offered more career mentoring (both male and female faculty) ► Moss-Racusin et al, PNAS, ► Motherhood penalty: all else equal, mothers rated as less competent (though fathers may get a boost). ► Correll, Bernard, & Paik, American Journal of Sociology, 2007.

Schemas Affect: (4) Salary & Advancement ► Women earned $10,000-$30,000 less than men in a cohort of elite, early- career K-award research grants for physician scientists even after adjusting for academic rank, work hours, research time and other factors. ► Jagsi et al, Academic Medicine, ► Landmark MIT study: women discriminated against in promotion, salaries, lab space, committee service. ► The MIT Faculty Newsletter special edition XI, 4 (1999). ► Men cite men more than women; women cite themselves less than men. ► Maliniak, Powers, & Walter, International Organization, ► Teaching evaluations: Premiums for being male, native English speaker, being “good looking”. ► files/gsebibliography.pdf

Our Job 1. Understand mechanisms by which this plays out in the hiring process (and evaluations beyond). 2. Agree to adopt best practices to mitigate the influence of unconscious biases throughout the search, hiring, and promotion process. 11

How does this play out in the hiring process? 3 mechanisms by which schemas  fewer women 1.Stereotyping 2.Elitism in Evaluations 3.Re-Jiggering Criteria

Mechanism: (1) Stereotyping Negative Stereotype ► “Perennial probation” for women and minorities ► Presumption of incompetence ► Suspicion, more aggressive questioning Positive Stereotype ► “Halo effect” - presumed to be competent and absolutely authentic ► Given benefit of the doubt Moody, Rising Above Cognitive Errors, 2010

Mechanism: (2) Elitism in Evaluations ► Upgrading/Downgrading based on regional accent, dress, jewelry, social class, ethnic background, marital status, kids, religion, etc. ► Judging based on first impressions: “She seemed like kind of a snob” “I don’t think I can stand his accent” “The fact that she went to Stanford gives her an edge” ► The Longing to Clone ► Giving more weight to letters written by members of one’s own circle. ► Discomfort with traits outside existing experience: “Would we really hire someone from XXX University??” ► Making assumptions about fit: “Minorities wouldn’t like it here” “There’s a two-body problem” ► Moody, Rising A bove Cognitive Errors, 2010

Mechanism: (3) Re-jiggering Criteria ► Raising the bar (often related to negative stereotyping) ► Seizing a pretext - assigning excessive weight to something trivial ► Emphasizing character over context - “Energy level was low” ► Premature ranking, digging in, refusal to update. ► Structuring interactions so we receive information congruent with our assumptions ► Treating candidates differently (making calls to certain set of faculty, aggressive questioning by a subset) ► Priming situations - setting people up to fail (“I wonder why you didn’t do X?” where X is an entirely different project) ► Re-weighting: “I know X has 10 publications, but we should really consider the quality of Y’s one publication.” ► Moody, Rising Above Cognitive Errors, 2010.

The Takeaway There is hope. Pre-commitment mechanisms; i.e., making evaluators lay out criteria before seeing files & candidates helps to eliminates bias. ► Uhlmann & Cohen, Psychological Science,

What Can We Do? Before the search even starts: ► Truly broaden the pool ► Think about ways in which we can “widen” the funnel ► Take an implicit association test (IAT)

What Can We Do? Making long and short lists: ► Establish basic criteria and ground rules for evaluation before looking at files. ► Explicitly acknowledge the potential for bias to affect the decision. ► Judge how people will do *at* your university, where they will have resources, reasonable teaching loads, great mentoring, inspiring peers, etc. Does the candidate have this at his/her current institution? ► Be accountable, and prepared to explain every step of the process

What Can We Do? Flyouts/Visits: ► Don’t rush to rank ► Dissuade herd momentum ► Treat people equally when they visit ► Don’t ask illegal questions! ► Consider the value of diversity

What Can We Do? Making Offer Decisions: ► Explicitly acknowledge potential implicit biases among finalists ► Revisit initial criteria ► Structure feedback – “Canvassing” is an example of worst practice ► Account for how faculty interacted with candidates

Scorecard Example - Health Sciences 21

Scorecard Example - Health Sciences 22 Committee sets the standard for feedback.

One Final Note ► Chairs should be required to document all the steps taken to ensure open and unbiased searches. ► “We just picked the best candidate” should no longer be acceptable given mountains of evidence to the contrary. ► Acknowledging that we - like everyone - have unconscious biases is the first step.