Fairness and Bias in Selection and Staffing Procedures

1. Selection and Staffing III

2. Learning objectives • Fairness versus bias • Know the legal environment of selection • Distinguish between types of differences • Mean difference • Differential validity • Differential prediction/regression • Differential item/test functioning

3. Fairness and Bias • Some predictors show mean differences between racial/ethnic groups • Example: cognitive ability • Roughly, whites have a mean IQ of 100, Asians about an SD higher, African Americans about an SD lower (SD equals about 15) • Even if this wasn’t the case, we should consider issues of fairness and bias

4. Fairness and Bias • Fairness is a subjective appraisal that reflects societal values about whether a selection decision is just • Bias is a concept that has matured with time, but is now concerned with whether a predictor misrepresents a person’s likelihood of satisfactory future performance • Interrelated issues, but very different

5. First a quick distinction • Adverse (or disparate) treatment • Intentional discrimination • Employer knowingly and willfully treats minority applicants in a different manner than majority applicants • Adverse impact • Unintentional discrimination • Employment practice incidentally treats minority applicants differently than majority applicants

6. Fairness • Since fairness is a subjective judgment, what constitutes a fair selection process is largely defined by the law and by professional guidelines • Several laws have clarified issues • Griggs vs. Duke Power Co. • Civil Rights Act of 1965, Title VII • Civil Rights Act of 1991

7. Griggs vs. Duke Power • Duke Power required high school diploma • At the time, about 12% of African Americans had diplomas, about 34% of Caucasians did • Supreme Court determined the process for analyzing adverse impact • Is there adverse impact? • If so, the burden shifts to the employer; must show selection system is job-related • Duke couldn’t, Court found in favor of Griggs

8. Civil Rights Act of 1964 • Required that selection processes be job related • Bona Fide Occupational Qualifications • Allows for gender, religion, race-based decisions if there is a genuine business necessity • Ex: Catholic Church allowed to accept only males as priests • Ex: A script calls for a Latino actor for a part • Created the EEOC

9. Civil Rights Act of 1991 • Several topics covered • Most important for this class: Made Race- or Gender-Norming illegal • No separate cutoffs for different groups • No separate norms • No bonus points for race/gender/ethnic membership • Considerable impact on how organizations can pursue diversity objectives

10. How to define adverse impact • How different does it take to be different? • If 50% of men pass the test, and 10% of women do, is there adverse impact? • If 50% of men pass, and 45% of women do, is there adverse impact?

11. The 4/5ths Rule • Rule of thumb: If the passing rate for the minority group is 4/5ths (80%) of the passing rate for the majority group, adverse impact is considered to have occurred • Compute: minority pass rate/majority pass rate • If the result is less than 80%, adverse impact • So, from our second example • (45%)/(50%) = .90, or 90%, so no adverse impact

12. Bias • Three issues • What are we measuring? • Does the predictor make unbiased predictions of future performance? • Jargonized • What’s the construct validity of the predictor? • Does the test demonstrate intercept or slope differences between groups

13. Assessing Bias • Mean difference • On average, there is a difference in scores between minority and majority group members • Differential validity • The relationship between the selection test and job performance is different for minority and majority group members • Differential Prediction/ Regression • A particular score on the test is associated with a different level of job performance for minority and majority group members

14. Over- vs. under-prediction • Over-prediction occurs when the regression implies a higher level of job performance than really happens • Under-prediction occurs when the regression implies a lower level of performance than really happens • Rarely find examples of differential regression • When it happens, we usually find over-prediction (Hartigan & Wigdor, 1989)

26. DIF/DTF • Differential item/test functioning • Assessed using item response theory • Item or test works differently for minority vs. majority group members • If a minority person has a lower chance of getting the item right than a majority person of the same ability level, the item displays DIF • If the test has a bunch of biased items, then it will display DTF

27. DIF

28. DIF