1 Can Artificial Intelligence be Used to Reduce Employment Discrimination? Matthew Vallejo The University of Arizona Global Campus PHI103 Informal Logic Professor Wise May 16, 2022 2 Can Artificial Intelligence Be Used to Reduce Employment Discrimination? An increasingly popular topic for debate is whether Artificial Intelligence (AI) has a place in employment screening. Some have argued that using AI to determine which candidates move on in the employment screening process promotes discrimination and adds even more bias to an already flawed system. In contrast, others believe that AI can be used to reduce the amount of bias and discrimination already present in the system we currently use. With the use of AI becoming more prevalent, we must tackle this issue before it has time to snowball into a problem that is too big for us to handle. This paper will present strong arguments on both sides of the issue and my argument for whether AI should be used in employment screening. This paper will also analyze all arguments and an objection to my argument. Argument that AI Increases Employment Discrimination Below is an argument that shows findings that support the conclusion that AI increases employment discrimination. The argument is listed in standard form as follows: Premise 1: The U.S. Equal Employment Opportunity Commission (n.d.) defines employment discrimination as treating someone differently or less favorably based on race, color, religion, sex, national origin, disability, age, or genetic information. Premise 2: AI systems amplify the biases of real-world data. (Shaw, 2019) Premise 3: Programmers sometimes create models that produce more favorable results for a particular group of individuals to compensate for bias present in the data. (Ferrer et al., 2021) Premise 4: Machine learning programs favor European-sounding names over African-sounding names. (Hadhazy, 2017) 3 Premise 5: Amazon’s artificial intelligence tool favored male over female candidates. (Hamilton, 2018) Premise 6: Microsoft and Face++’s Facial recognition software views black people as more contemptuous and angrier than white people. (Rhue, 2018) Conclusion: Artificial intelligence systems increase discrimination and bias when screening candidates for employment. Argument that AI Decreases Employment Discrimination On the other side of the argument are those who feel AI has the potential to decrease employment discrimination. This argument also has an equal amount of research to support its claim and is listed in standard form as follows: Premise 1: AI systems can be designed to eliminate unconscious human bias as long as certain specifications are met. (Polli, 2019) Premise 2: If AI systems are transparent in their codebase and ethical design, they will produce fairer results in employment screening. (Trindel et al., n.d.) Premise 3: AI can be trained to ignore the name, skin color, race, or gender when analyzing candidates, which is something a human cannot do. (Xie, 2019) Premise 4: Vermont’s Artificial Intelligence Task Force (2020) states that a permanent AI commission and the adoption of a code of ethics would help keep AI systems fair. Premise 5: Researchers at Penn State and Columbia University have created an artificial intelligence tool to detect racial and gender discrimination. (LaJeunesse, 2020) 4 Conclusion: Artificial intelligence systems decrease discrimination and bias when screening candidates for employment Analysis of Both Arguments Both of the arguments listed above are relatively sound. They both provide a profound amount of research that can be used to support their respective conclusions. However, both arguments are not exactly opposite each other. Both can be true at the same time. The first argument states that AI increases employment discrimination while the second conclusion states it decreases it. Both can be true because it has shown that AI can do both of these things at times. We now should be asking whether AI does more harm than good when it comes to employment discrimination. Further analysis shows that one argument may be stronger than the other. The first argument has a flaw in its reasoning. The hasty generalization fallacy may be committed in the premises listed in the first argument. In premise five and six of the first argument, it states that Microsoft, Amazon, and Face++’s AI systems have produced discrimination during employment screening. While these premises are true, it may be an over generalization to state that since three companies have created AI systems that produced negative results, all companies that create AI systems will produce these same negative results. A better approach would be to analyze the pitfalls these companies have faced and learn from them. If businesses can learn why Amazon, Microsoft, and Face++’s systems failed, they can create better AI systems that produce fairer results. However, this is not to say that the first argument’s conclusion is false, and the second argument is undoubtedly better. Both arguments have specific cases where some may see them as correct. Despite this, however, it does seem like the second argument is slightly sounder since 5 it offers a solution to fight employment discrimination. In contrast, the first argument offers no solution other than saying that using AI to screen for employees can cause problems. My Argument I believe the best argument can be listed in my words below. This argument contains pieces from the first and second arguments in addition to my interpretation of a solution to the issue that is AI use in employment screening: Premise 1: The U.S Equal Opportunity Commission (2021) states that AI has great potential to improve lives in employment screening. Premise 2: When misused, AI has the potential to reinforce biases. (Shaw, 2019) Premise 3: Traditional methods of hiring are incredibly biased. (Polli, 2019) Premise 4: If a set of standards are created and followed, then AI will produce fairer results than traditional methods of employment screening. Premise 5: If organizations create open-source auditable AI systems, then these systems will be able to be tested for any discriminatory behavior Premise 6: If AI systems can be tested for any discriminatory behavior, then AI systems will decrease employment discrimination. Conclusion: Therefore, AI systems can decrease employment discrimination if suitable systems, methods, and checks and balances are put into place to limit how organizations use AI systems. Response to an Objection to the Argument While I presented a sound argument with research to back up my premises, there can still be some objections to my argument. One of the strongest objections would be to the fourth 6 premise of my argument. This premise states that if a set of standards are created and followed, then AI will produce fairer results than traditional employment screening methods. However, one can argue that creating a set of standards for AI to produce unbiased results would require clean data with no biases present. Finding data with no biases is a highly challenging problem that many data scientists struggle with when creating fair algorithms. Many AI systems work to highly rate applicants with a set of qualities that represent the ideal candidate for a position. Most of the time, these qualities are taken from high-performing employees to find applicants similar to these highperforming employees. The issue arises when you have a skewed workforce that underrepresents certain groups of people. If, for example, a company had a predominately white male workforce, then it is likely that any AI system they use will favor white male applicants. The issue of creating a fair algorithm for AI systems is a catch-22. To create an algorithm that produces a diverse workforce, you must present data from an already diverse workforce. This is a problem because to create a system that produces fair results. You must already have a system that produces fair results. The best response to this objection is to create an AI system that ignores race, age, sex, gender, and other qualities that can be discriminated against. (Barton, 2019) Eventually, suppose this information is filtered out, and there is enough time to keep adjusting the algorithm. In that case, we should see a system that can produce results that can produce a diverse workforce. The main thing one must consider when creating an AI system to screen for employees is that this will not be a system that can be created and perfected overnight. At first, there may be some discrimination present in the algorithm, but companies must be willing to closely watch the 7 system and correct any errors in the system's judgment. Only then can a system that produces fair results be created. Conclusion In conclusion, issues have yet to be solved when dealing with artificially intelligent systems that can produce fair results. Some believe the risks are too significant and that we can create worse results for individuals if AI systems are used in employment screening. However, evidence suggests that the opposite may be true. A human cannot parse through the thousands of applicants that may come their way, so AI must inevitably be used to filter out some applicants to a hiring manager. The big question that must be answered is how AI can be used to decrease the discrimination that is already present in employment screenings. This is not an easy question to answer, and there are many ways to view and tackle this issue, but one thing remains true. In order to solve challenging issues like these, we must all have an open mind to all solutions. If we fail to assess all sides of an argument critically, we will not likely come to an optimal solution. 8 References Barton, N. T. L., Paul Resnick, and Genie. (2019, May 22). Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms. Brookings. https://www.brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practicesand-policies-to-reduce-consumer-harms/ Ferrer, X., Nuenen, T. van, Such, J. 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