Decision aids for addressing the validity-adverse impact trade-off
Typically, adverse impact (AI) is an after-the-fact analysis: Once predictor scores for a pool of applicants are available, AI is evaluated. Sometimes the analysis is made in real time, as predictor scores are obtained on a set of applicants, and AI calculations are done on a “what if” basis as inpu...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2009
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| Online Access: | https://ink.library.smu.edu.sg/lkcsb_research/5821 https://ink.library.smu.edu.sg/context/lkcsb_research/article/6820/viewcontent/aids.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Typically, adverse impact (AI) is an after-the-fact analysis: Once predictor scores for a pool of applicants are available, AI is evaluated. Sometimes the analysis is made in real time, as predictor scores are obtained on a set of applicants, and AI calculations are done on a “what if” basis as input to decisions about features such as where to set a cutoff score. The focus of this chapter, however, is on attempts to estimate in advance the likely impact of a given selection system. Here, estimates are made based on available information about the features such as the expected magnitude of subgroup differences, expected interpredictor correlations, and expected predictor-criterion correlations. Such information may be local (e.g., group differences observed the last time a predictor was used) or based on a more general research literature (e.g., group differences reported in publisher manuals or in the published literature for a given predictor type and a given job category). |
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