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Questions 1. Suppose a manufacturer using analytics learns that employees who graduated from one technical college are slower to get up to speed than employees who graduated from another technical college. How might applying the data to hiring decisions raise the risk of discrimination? 2. How could the employer in question 1 use the information in a nondiscriminatory way?

Questions 1. Suppose a manufacturer using analytics learns that employees who graduated from one technical college are slower to get up to speed than employees who graduated from another technical college. How might applying the data to hiring decisions raise the risk of discrimination? 2. How could the employer in question 1 use the information in a nondiscriminatory way?

Understanding Business
Understanding Business
12th Edition
ISBN:9781259929434
Author:William Nickels
Publisher:William Nickels
Chapter1: Taking Risks And Making Profits Within The Dynamic Business Environment
Section: Chapter Questions
Problem 1CE
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HR How To Using Data Analytics to Support Fair Hiring Decisions As companies get increasingly sophisticated about collecting and analyzing data, they can apply those skills to employee selection. But the selection criteria have to be consis- for workers with a 3.5 grade- point average can gather data to see whether this requirement is actually associated with success. Perhaps someone with a 3.0 average and certain experiences performs even better. • Use analytics methods to remove irrelevant criteria from the selec- applications submitted on paper or in person should be screened using the same criteria. Questions tent with fair employment under the law. Here are some tips for smart hir- ing that is also legal: 1. Suppose a manufacturer using analytics learns that employees who graduated from one technical college are slower to get up to speed than employees who graduated from another technical college. How might applying the data to hiring decisions raise the risk of discrimination? • Understand the jobs, company, and data well enough to develop a model explaining why criteria matter. A company that collects a lot of data can eventually find relationships betwe ables and superior performance. Suppose you find that people from certain zip codes are less likely to quit. If you don't dig fur- ther into what it is about people from those zip codes, you could wind up making hiring choices that show a pattern of discrimina- tion-and don't select for impor- tant qualities behind the numbers. Use analytics to test whether assumptions about job require- ments really are relevant. A company that routinely selects tion process. For example, hav- ing a computer do the initial screening removes the potential for unconscious bias related to factors like people's names or photographs. Create robust processes. Just knowing that you want certain me vari- 2. How could the employer in question 1 use the information in a nondiscriminatory way? characteristics doesn't mean the company will hire great people. Make sure the selection tools, including questions asked in interviews, are effective (reliable, valid, and so on). Ensure that the process is efficient and treats all candidates with respect. • Apply the analytic methods to all applicants. If applications submitted online are screened Sources: LydellI C. Bridgeford, "Experts Discuss Big Data's Effect on Hiring, Blas Clalms," HR Focus, September 2015, pp. 4-6; Kurt Naasz, "Advances In 'Big Data' and Analytics Can Unlock Insights and Drive HR Actions," HR Focus, May 2015, pp. 1-4; Murad Hemmadi, "The End of Bad Hiring Decisions," Canadian Business, January 2015, EBSCOhost, http://web.a.ebscohost.com. by a computer system, then
Transcribed Image Text:HR How To Using Data Analytics to Support Fair Hiring Decisions As companies get increasingly sophisticated about collecting and analyzing data, they can apply those skills to employee selection. But the selection criteria have to be consis- for workers with a 3.5 grade- point average can gather data to see whether this requirement is actually associated with success. Perhaps someone with a 3.0 average and certain experiences performs even better. • Use analytics methods to remove irrelevant criteria from the selec- applications submitted on paper or in person should be screened using the same criteria. Questions tent with fair employment under the law. Here are some tips for smart hir- ing that is also legal: 1. Suppose a manufacturer using analytics learns that employees who graduated from one technical college are slower to get up to speed than employees who graduated from another technical college. How might applying the data to hiring decisions raise the risk of discrimination? • Understand the jobs, company, and data well enough to develop a model explaining why criteria matter. A company that collects a lot of data can eventually find relationships betwe ables and superior performance. Suppose you find that people from certain zip codes are less likely to quit. If you don't dig fur- ther into what it is about people from those zip codes, you could wind up making hiring choices that show a pattern of discrimina- tion-and don't select for impor- tant qualities behind the numbers. Use analytics to test whether assumptions about job require- ments really are relevant. A company that routinely selects tion process. For example, hav- ing a computer do the initial screening removes the potential for unconscious bias related to factors like people's names or photographs. Create robust processes. Just knowing that you want certain me vari- 2. How could the employer in question 1 use the information in a nondiscriminatory way? characteristics doesn't mean the company will hire great people. Make sure the selection tools, including questions asked in interviews, are effective (reliable, valid, and so on). Ensure that the process is efficient and treats all candidates with respect. • Apply the analytic methods to all applicants. If applications submitted online are screened Sources: LydellI C. Bridgeford, "Experts Discuss Big Data's Effect on Hiring, Blas Clalms," HR Focus, September 2015, pp. 4-6; Kurt Naasz, "Advances In 'Big Data' and Analytics Can Unlock Insights and Drive HR Actions," HR Focus, May 2015, pp. 1-4; Murad Hemmadi, "The End of Bad Hiring Decisions," Canadian Business, January 2015, EBSCOhost, http://web.a.ebscohost.com. by a computer system, then
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