Are software algorithms more biased than humans?
By: Jasmine Bayani
Tags: Human Resources, Human Behavior, Algorithms, Technology, Personal Biases

I believe that humans are more biased than algorithms. For algorithms, their bias depends on the rules it was programmed with to make decisions and the data set they were trained with. On the other hand, we humans have a more complex decision-making approach because of our System 1, automatic but susceptible to bias, and System 2, takes conscious effort but is analytical and less prone to bias (Douglass). Because of this, algorithm behavior can be more flexible when we adjust it towards a more controllable and favorable level of performance to solve problems, compared to human behavior.

Human bias is part of the technology humans create. In 2015, Amazon realized that their AI recruitment tool was biased against women when showing the top tech job candidates (Dastin). This was mainly because the training data set provided to the AI recruitment tool was based on a male-dominant 10-year resume pool. In the past, human resources have perceived men to be rightfully dominant to land tech jobs such as IT and software development (Pot’Vin-Gorman 23). Although the AI’s biased results were unintentionally programmed, the prevalence of men candidates in the results reflected the human bias that tech jobs are often applied for by men.

After Amazon’s attempts in revising the algorithm to be more gender-neutral, Amazon halted the AI recruitment tool by the start of 2017 “because executives lost hope for the project” (Dastin). Perhaps the loss of hope was not due to not being able to solve the bias of the AI tool, but solving human bias seemed impossible. Diving deeper into the issue of gender discrimination in the workplace, researchers have looked into two types of gender discrimination: statistical discrimination, the perceived average gender group differences in abilities or skills, and taste-based discrimination, driven by stereotypes, favouritism, and bias (Gerdeman). Statistical discrimination shows that when we know little about an individual, we tend to categorize them to a certain group. If individuals belong to a group that is perceived to perform worse than the other group, it creates a bias against them. Between the two, statistical discrimination is much harder to root out (Gerdeman).

Despite the unsuccessful attempts of creating a bias-free AI recruitment tool, I believe Amazon should continue to develop it. According to Moore’s Law, computers can remarkably grow faster over time and become exponentially more powerful (McAfee and Brynjolfsson 35); thus, if the AI recruitment tool becomes more fine-tuned to fairly and reliably recommend candidates, it can significantly minimize hiring costs and hiring issues prone to humans. Using resumè metrics, such as keywords like “women’s chess club captain”, can categorize candidates into groups. An algorithm would then use keywords to decide if the candidate belongs to the perceived inferior group or the superior group. If this would be the main basis for determining how qualified a candidate is for a job position, it can lead to statistical discrimination. To minimize bias, Amazon can implement AI in its recruitment process by adapting Google’s hiring approach (McAfee and Brynjolfsson 57). Instead of resumès, Amazon can use a standardized questionnaire and a consistent rubric for hiring. In replacement of a human interviewer, the AI recruitment tool can indicate how the candidate did on the hiring process based on measurable and comparable results. By understanding better the contributing factors to human bias, we can use methods to eliminate or at least control them better through algorithms that will reduce the bias that exists in these human-created technologies to create faster and more effective solutions.

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