Final answer:
The two scenarios indicating algorithmic bias are the use of algorithms that may adjust heart risk based on race, and a female candidate's résumé being scored lower than male counterparts, despite equal qualifications.
Step-by-step explanation:
The two scenarios most likely to be the result of algorithmic bias are:
- B. Algorithms that screen patients for heart problems automatically adjust points for risk based on race.
- C. The résumé of a female candidate who is qualified for a job is scored lower than the résumés of her male counterparts.
These examples show discrimination in the labor and financial capital markets due to statistical discrimination and possible imperfections in algorithms that unfairly impact individuals based on gender, race, or other characteristics unrelated to their actual qualifications or health risks. In the context of hiring, employers may rely on incomplete information to make hiring decisions, which can introduce bias. For instance, if an employer erroneously believes that women are less productive in specific roles, they may favor male candidates, as exemplified by the preference for a male carpenter over an equally qualified female carpenter.