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Case Study Rubric: Added the case study rubric in the attachments. Case Study Do

by | Oct 8, 2021 | Ethics | 0 comments

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Case Study Rubric: Added the case study rubric in the attachments. Case Study Document: A Discriminating Algorithm A software developer designs software that screens the résumés of candidates applying for a job at her company. She later discovers that the software may be having a disparate impact on minority communities. She brings the issue up with her boss, who is reluctant to change the software. Sandra is a software developer for Emporia, a large retailer that has recently experienced high attrition rates in their sales department. Her boss tasks her with designing software that the company can use to screen the résumés of candidates applying for sales positions. Sandra is supposed to ensure that the software awards a higher recommendation score to applicants who are more likely to stay on the job longer. Sandra begins by applying a Principal Component Analysis (PCA) to data from the résumés of current and past employees to identify the minimal set of features that best correlate with length of employment. Based on this data, Sandra designs her software so that it classifies résumés possessing a high number of these features as “recommended candidates,” and those lacking them as “non-recommended candidates.” Among the features associated with longer employment are the distance the person lives from the store where they work, previous retail sales experience, and a record of professional development. One year after implementing Sandra’s software, the company’s salesperson attrition rate falls by nearly 15%. While analyzing the results, however, Sandra notices that 92% of the new sales employees hired have been white. Concerned that the company may be violating the legal standards for fair access to employment, she tries to figure out why her software is not recommending more applicants from minority groups. After some research, Sandra thinks she has found the underlying cause. The software is only recommending applicants who live in zip codes less than one mile away from Emporia stores. This is because the PCA she applied to employee data identified employee zip codes as the metric best correlated to length of tenure. She infers that this is because employees living in neighborhoods closest to Emporia stores have much shorter commute times, and thus tend to stay on the job longer. These neighborhoods, however, have mostly white, middle-class residents. Black and Latino applicants, who make up about 80% of the candidates applying for sales jobs, tend to live in areas that are further away from Emporia stores. Because their zip codes are located over a mile away from Emporia stores, the software is classifying many of these candidates as “non-recommended candidates.” As a member of the Association for Information Science and Technology (ASIS&T), Sandra feels as though it is her ethical and professional responsibility to make sure that the software she has programmed does not have a disparate impact on minority groups. She presents her findings to Annette, the head of human resources. She explains that she thinks the company may inadvertently be violating the disparate impact principle of the Civil Rights Act of 1964. This principle prohibits employers from using any employment practices that have unjustified adverse impacts on members of a protected class, such as lower-income persons, minority groups, or women. “I’m not only worried that the software is excluding well-qualified applicants,” says Sandra. “If Emporia continues to use it to screen résumés, I think that the company will risk facing employment discrimination litigation.” Annette is skeptical about Sandra’s claims. “I think it would be a bad idea to change the software in any way,” he says. “It has done everything we wanted it to,” she continues. “Not only is our attrition rate at the lowest it has been in years, but our sales are up as well. Plus, is it even possible for a computer program to discriminate?” Annette thinks that the application test is only using objective criteria to identify best qualified candidates and does not believe that the geographic selection criteria is injecting a discriminatory bias into the application recommendations. Sandra tells her that she thinks that there are more appropriate metrics that the software can use to recommend applicants, but Annette remains unwilling to modify it in any way that might undo its recent success. Additional Information There are a number of important points of view within this case, especially within the company. Most of these are not mentioned in this write-up, but that does not mean that you should ignore them in your analysis. There are also several questions you might consider as you start your analysis. Answering these questions does not constitute an analysis – they are only provided to help you get started thinking about this scenario. As the software developer, what are Sandra’s ethical responsibilities to those who are applying for positions at Emporia? What are her professional and legal responsibilities as an employee of Emporia? How reliable is Sandra’s inference about why zip codes are the best metric? Is there a way to confirm that inference and should Sandra have done that before making her case to Annette? Is it possible that zip code distance might be correlated with length of employment but not a causative factor? Does the solution to Emporia’s attrition problem necessarily need to be a technical one? What other forms of expertise can the company bring in to analyze why sales employees are leaving their jobs? Was the input data (resumes of current and past employees) a valid data set to use as a basis for this analysis? Adapted from: Solon Barocas * & Andrew D. Selbst ** (June, 2016). ARTICLE: Big Data’s Disparate Impact. California Law Review, 104, 671. https://advance.lexis.com/api/document?collection=analytical-materials&id=urn:contentItem:5K49-2F50-02BN-013K-00000-00&context=1516831. (provided as a reference. You are not expected to read this article!) Intended Audience You are an independent Data Science specialist contracted to review Sandra’s recommendation system. Your analysis report will be presented to the senior executive leadership team of Emporia Stores. As the head of the company’s human resources unit, Annette is part of this group, as are the heads of the other administrative units in the company. Be sure to review the Case Study Rubric before starting and while you are working. You may also need to do a little bit of background research to identify who else typically might be part of the group receiving your report. This might help you identify any objections they might have to your analysis.

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