How and to what extent does anonymizing identifiers in recruitment data allow AI to increase fair and diverse hiring practices through the reduction of implicit human bias

Correcting Biases

Implicit Bias :

Identifying implicit bias

Theories

The Implicit Association Test (Greenwald et al. 1998)

Affect Misattribution Procedure (Payne et al.2005)

Go/No-go Association Task (Nosek & Banaji 2001)

Associationist View

Doxastic Model (Mandelbaum 2016)

In recruitment

Biased weighting of information

Evaluation criteria

Changing weighting of categories based on bias for candidate (Hodson et al., 2002;)

Unspecific criteria leaving gaps for bias (Uhlmann & Cohen, 2005)

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Biased Interpretation of information

Non-verbal cues (Hugenberg and Bodenhausen (2003),

Selection of hiring panel (Sommers & Norton, 2008)

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Behaviours (Griggs, 2018)

Job-related traits

Organizational level

Remove biasing category information

Inidividual level

Create unambiguous hiring criteria (Uhlmann & Cohen, 2005)

Highly structured interviews (Bragger et al., 2002)

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AI in Recruitment

Prevalence (Upadhyay
and Khandelwal, 2018))

How does it work? (Raghavan et al. 2000)

Definition (Jarrahi, 2018))

Diversity in Hiring

Likelihood to replicate human bias

Gender Identity(Davison and Bruke 2000)

Ethnicity (Correll, et al. 2007)

Sexuality

Mental or Physical disabilities

In Canada BIPOC

Comprehensive definition of genders

Marketing job opening

Predicting organitzation fit

Screening applicants

Predicting likelihood to leave organization

Indicators used

Age

Gender

Race

Name

Education (in some cases)

Creating postings that use gender-neutral, non-biased language Rab-Kettler & Lehnervp, 2019)