In academia and in health care contexts Implicit Biases: In academia and in health care contexts Dr Jules Holroyd Department of Philosophy University of Sheffield

Context: gender in UK academia Low numbers of women in STEM, especially certain fields e.g. engineering (23% of lecturers, 5% of professors) Also other fields, e.g Philosophy, Economics, Theology. (Philosophy in UK: 24% of permanent staff) Low numbers of women at the top in almost every field.

Context: race in UK academia Low numbers of BME students, staff in academia Russell Group universities 2.6% black UG students (youth population 3.9% black) 92.39 per cent of professors (15,905) in UK academia are White, 5% Asian, and 0.49 per cent (85) are Black, with just 17 of those being women (Runnymede Trust 2015)

Explanations for under-representation? -Structural explanations: structures of workplace & parenting norms; race-linked economic inequalities -Explicit sexism and racism -Implicit biases, and their role in institutional contexts

What are implicit biases? -fast, automatic, difficult to control and difficult to notice processes: triggering of affective responses; associations between social identity and certain traits (stereotypic) -Implicit biases have been pervasively found on experimental measures; -And certain behavioural impacts have been reliably reproduced (Jost et al 2009)

Experimental measures: E.g. Implicit Association Test https://implicit.harvard.edu/implicit/takeatest.html

Why care about implicit biases? -non-distorted judgments; -non-discriminatory behaviour; -better institutional practice; -avoiding 'chilly' working environments; -you might be targeted by others' biases; -there are things that can be done to avoid the negative impact of implicit bias.

Behavioural impact: -Relevant to academic practice CV studies: the gender (Moss-Racusin et al 2012) or race (Bertrand & Mullainathan 2003) of the CV affects the evaluation of it. [in the 2012 study, for a lab manager position, faculty offered a higher starting salary and more mentoring to (identical) male candidates]

Behavioural impact: -Relevant to academic practice Gender of author affected evaluation of the quality of abstracts for science publications, and ratings of interest in collaboration with the author (Knobloch-Westerwick et al 2013)

Behavioural impact: -Relevant to academic practice Gender of author affects rates of citation (Knobloch-Westerwick et al 2013) From International Relations Average paper by an untenured male: cited 26.7 times. Average paper by an untenured woman: cited 21.5 times.

Behavioural impact: -Relevant to academic practice Reference writing seems affected by social identity of candidate (for medical faculty in the US): Women presented as 'teachers and learners'; men as 'researchers and professionals'; Women's reference writers gave 'minimal assurance' rather than 'solid recommendation' Trix & Psenka, (2003)

Behavioural impact: -Relevant to academic practice So, our own judgement of academic quality may be skewed by implicit biases; And others evaluation of us may be skewed by implicit biases.

Behavioural impact: -Relevant to healthcare contexts 1. For white physicians, pro-white bias negatively correlated with the likelihood they would recommend effective treatment options to black patients (thrombolysis) (Green et al 2007)

Behavioural impact: -Relevant to healthcare contexts 2. For white physicians, pro-white bias negatively correlated with the likelihood they would prescribe pain relief medication for black patients (Sabin & Greenwald 2012)

Behavioural impact: -Relevant to healthcare contexts 3. Clinicians with higher levels of pro-white bias were evaluated as delivering poorer quality of care and poorer clinical communication with black patients (Cooper et al 2012)

Behavioural impact: Microbehaviours In interracial interactions (white-black), white participants showed more indicators of tension (higher eye-blink rates), and fewer markers of attentiveness (lower rates of eye contact) with black interlocutors. They did not report on this, but black interlocutors did, and rated the quality of interaction as lower (Dovidio et al 2002)

What to do about it? Some DON'Ts: Don’t think that your awareness of the problem is enough. Don’t just tell yourself “ don’t be biased”. Don’t just tell yourself not to see gender or race. Don’t just tell yourself to be objective. All of these can make it worse.

What to do about it? Some DOs There is no 'silver bullet' that fixes implicit biases. But you can: -find out what biases you have any may be influenced by; -think carefully about contexts where implicit biases might affect you and your working; -investigate strategies that might help to avoid bias in those contexts.

What to do about it? Different kinds of strategy (1): -removing the possibility for bias, e.g. anonymising; -consciously countering biases, e.g. seeking out work from women or BME scholars to reference; or people to invite to your conference/workshop; quotas (or aims) for reading lists or short lists...

What to do about it? NB: Objection: then we're not concerning ourselves with merit, but social identity! Response: our judgments are already based on something other than merit, if implicit biases are influencing selections.

What to do about it? Different kinds of strategy (2): -making decision procedures more robust: having clear criteria and weighting for those criteria, in advance; allocating a certain amount of time to each stage of the decision; making decisions under good conditions (e.g. not tired, hungry).

What to do about it? Different kinds of strategy (3): -working on your own cognitions (mixed results, often short term): Countering problematic associations by exposure to 'counter-stereotypical exemplars'; 'Retraining' your associations; Using 'implementation intentions' (cued cognitive responses)

What to do about it? Different kinds of strategy (4): -helping each other notice bias: People are better at detecting bias in others than in themselves... ...but can do so when prompted to reflect (Hann et al 2013); Find constructive ways of confronting each other – and being open to this – when bias may creep in?

What to do about it? Also: Raise awareness with colleagues and in your departments about implicit bias, the effects it may be having, and possible ways of tackling it.

What to do? De-bias Mitigate Individual Interpersonal Institutional Insulate Individual Interpersonal Institutional

What to do? De-bias Mitigate Individual Interpersonal Institutional Insulate Individual cognitive training (e.g. relearning associations) avoiding risk factors (hunder, tiredness); articulate reasoning; ‘imagine the opposite’ remove information that activates bias Interpersonal de-biasing ‘agents’; contact hypothesis identifying others’ biases is easier; challenging conversations sub-divide tasks to create anonymity; independence of procedures Institutional avoiding biased outcomes (e.g. quotas?) tracking outcomes; clear procedures; strong norms of fairness procedures that remove bias activating information

Useful further resources: Discovering your biases: Project Implicit: https://implicit.harvard.edu/implicit/ Bias and Blame project openIAT (build your own): https://blogs.nottingham.ac.uk/biasandblame/2014/05/28/run-your- own-iat-openiat/ Combating biases in institutions: http://wiseli.engr.wisc.edu/pubtype.php#product De-biasing interventions: e.g. Lai et al 'Reducing Implicit Racial Preference...' http://www.fas.harvard.edu/~mrbworks/articles/2014_Lai_JESPG.pdf