Report on the Impact of Biased Algorithms in the Workplace

Introduction

The increasing use lgorithms and artifi­brought tight wirsingsomniom exanples of bias, prinmac. 1: and transparency en­sundors.

1.   Amazon Hiring Algorithm

Amazon developed a machine loyrning- based hiring tool was found to biased against wometi-becal.Ise uwas recuiving on resumes from a prevrious decade, which predinimlanttant from ma! applicants- and hus jornilized resumes that included terms aus.

2.  Apple Card Credit Limit

Although not directly workprate-related, the COMPAS authorism issed in the cri1n- 1nal justior system to predict recidivism risk and racial bratars leopnosidersiaf the broarder impunging a risim In dindents.

5.  COMPAS Reridivism Algorithim

Although not directly workplace-related. the Compase algorithm used in Chrowrilal justice system predicting recidivism risk was more. likely to be incorrretelly al farish compared white defendants.

4. Performance Evaluation Tools

Some organizations have elustrati hici cal implen1entation of abissed ago­ rithms are,designed and monitorred lh i b.lias against certain grounch. such as minorities or older en1plovees, due tote data sets on which they trained.

Conclusion

These examples illustrate the critical importance of ensuring that algorithms are designed and monitored to prevent bias.