Blog: AI and Employment Ethics
Introduction (A description of our chosen topic) :
Many companies apply artificial intelligence in their employee recruiting, hiring and assessments. A 2017 Deloitte study has shown that more than 33% of their company respondents already used the machine in the interviewing process. Although such algorithms reduce human labor and produce somewhat reliable results most of the times, these algorithms could make unfair decisions that affect individual employees significantly. In this blog post, we are going to look at some cases related to AI and employment and touch upon the possible ethical issues introduced by this kind of artificial intelligence.
Relation to class:
The topic of use of AI in hiring process is closely related to everyday life of undergraduates: juniors and seniors are looking for jobs or graduate schools (and use of AI in college admission is another ethical topic). Furthermore, the technology of AI in recruitment only raised recently, and thus there are still many ethical concerns about its application, including the concerns about computer technologies, and about the exercise of professional responsibilities. The ethical analysis to this topic can help develop understanding of computing technologies, ethics and society.
Discussion: (ethical cases we will be analyzing)
In the discussion part, we are going to look at different products related to AI and employment first. For example, one of what the company HirVue is offering is an algorithm to score candidates based on the speech they give in an interviewing video. We are also going to look at several different cases related to the biases in the algorithm. For example, Ibrahim Diallo was fired unfairly by algorithm because of the idleness of his boss, which informs us of potential inaccuracy and lack of transparency of some employee assessment algorithms. For another, Amazon machine learning specialists found that their recruiting engine magnified gender biases. If possible, we are going to touch upon some cases of our Bucknell classmates who were interviewed by AI and talk about their experiences with the companies they interviewed with, etc. For all of the cases, we are going to incorporate our other sources such as Intro to Ethics and ACM code of ethics  to briefly point out the ethical problems related to workforce algorithms such as biases, lack of transparency and inaccuracy, and we will go into more details with all different kinds of sources in the Ethical Analysis section.
Ethical Analysis: (ethical dimensions we will be analyzing using relevant sources)
In the analysis part we will explore more about the inaccuracy and biases of AI in recruitment and employee assessment using a variety of resources, and introduce possible solutions to those potential problems using relevant ethical principles. If we have the capacity, we could look at some specific algorithms, explore the discrepancies when the machine is making decisions, and look for relevant algorimistic solutions if applicable.
First we introduce somes potential problems of AI-centered hiring process. The article “Hiring By Algorithm” introduces that there may be fault in machine. For example, the choice of training examples provided to a data-fitting procedure can have a profound difference on the produced decision rules. There will also be bias in the process, as although role of the human is obscured in decision making, the training data — decisions that are then matched as closely as possible by the algorithm is still made by humans.
Then we will introduce possible solutions to the problems though both ethical practices and algorithms based on our sources.
For example, the ACM Code of Ethics and Professional Conduct puts public good as the primary consideration. Given that 38% of Americans will be looking for a new job this year, making decisions that are subject to discrimination (1.4) could have a strong, negative impact on the public, and it’s against the public will of seeking good life, as introduced in Intro to Data Ethics.
The article “Hiring By Algorithm” introduces solutions to discrimination by algorithm. For technological solution, article Quantifying Program Bias introduces a inventive calculating algorithm to evaluate whether a decision making program is biased or not. And the article introduces a data mining process which modifies the data so that algorithms trained on the data are more likely to make non-discriminatory decisions. For legal solution, the article states that law should be used as part of an anti-discriminatory measure to ensure that hiring policies, as aided by advancements in computing, are not inadvertently excluding well qualified members of protected classes. The article also answers business concerns of maintaining accuracy in decision making.
Acm.org. (2018). ACM Code of Ethics and Professional Conduct. [online] Available at: http://www.acm.org/about-acm/acm-code-of-ethics-and-professional-conduct [Accessed 21 Mar. 2019].
Ajunwa, I. & Venkatasubramanian, S. (2016). Hiring by Algorithm: Predicting and
Preventing Disparate Impact. SSRN Electronic Journal. 10.2139/ssrn.2746078.
Albarghouthi, A., D’Antoni, L., Drews, S., & Nori, A. (2017). Quantifying Program Bias. Retrieved March 21, 2019, from https://arxiv.org/pdf/1702.05437.pdf.
CBC News. Could your next job interview be with a machine? How AI could change hiring,
Retrieved from: https://youtu.be/gPzIF89E1Dg
Fired by an algorithm, and no one can figure out why. (2018, June 20). Retrieved March 21, 2019, from https://boingboing.net/2018/06/20/the-computer-says-youre-dead.html?_ga=2.263479244.1574229553.1553033472-1058922381.1537828952
H. (n.d.). Hiring Intelligence | Assessment & Video Interview Software. Retrieved March 21, 2019, from https://www.hirevue.com/
Kobie, N. (2017, October 04). Who do you blame when an algorithm gets you fired? Retrieved March 21, 2019, from https://www.wired.co.uk/article/make-algorithms-accountable
Koller, D., & Friedman, N. (2012). Probabilistic graphical models principles and techniques. Cambridge, MA: MIT Press.
Miller, C. C. (2015, June 25). Can an Algorithm Hire Better Than a Human? Retrieved March 21, 2019, from https://www.nytimes.com/2015/06/26/upshot/can-an-algorithm-hire-better-than-a-human.html
Reuters. (2018, October 10). Amazon’s job-recruiting engine discriminated against women. Retrieved March 21, 2019, from https://nypost.com/2018/10/10/amazons-job-recruiting-engine-discriminated-against-women/
Riley, T. (2018, March 13). Get ready, this year your next job interview may be with an A.I. robot. Retrieved March 21, 2019, from https://www.cnbc.com/2018/03/13/ai-job-recruiting-tools-offered-by-hirevue-mya-other-start-ups.html
Snyder, B. (2019, January 10). Adina Sterling: How will artificial intelligence change hiring? Retrieved March 21, 2019, from https://engineering.stanford.edu/magazine/article/adina-sterling-how-will-artificial-intelligence-change-hiring?utm_source=YouTube&utm_medium=video&utm_campaign=TheFutureofEverything&utm_content=hiring010719
Stirling, M. Artificial Intelligence and Recruiting, Retrieved from:
Vallor, Shannon, and William J. Rewak. An Introduction to Data Ethics.
Weiss, S. (2018, October 17). Your next job interview might be with a robot. Retrieved March 21, 2019, from https://nypost.com/2018/10/17/your-next-job-interview-might-be-with-a-robot/