Automated Hiring Systems Presented by: Please put your name and email in a single box below. Note no more than 20 students may work on a single report. Thank you for your generosity in limiting this to 20 students so everyone has a chance to contribute 🙂 Members list: Timothy Kim kim.tim@northeastern.edu Nikita Seth seth.n@northeastern.edu Milena Perez-Gerus perez-gerus.m@northeastern.edu Avirath Bajoria bajoria.a@northeastern.edu Muftu.e@northeastern.edu Boris Sorokorenskii sorokorenskii.b@northeastern.edu Yining Tao tao.yin@northeastern.edu Anant Kovil kovil.a@northeastern.edu Christian Cassanelli (cassanelli.c@northeastern.edu) Wei Ding ding.wei1@northeastern.edu Ruo-Jie Lin lin.ruo-@northeastern.edu Tzu-Tung Chang chang.tz@northeastern.edu Yuna Lee (lee.yuna1@northeastern.edu) Ben Pierce pierce.ben@northeastern.edu Lang Shao shao.la@northeastern.edu Chapin Wilson wilson.chap@northeastern.edu Marcel Johe johe.m@northeastern.edu Links - - https://www.nytimes.com/2019/10/08/opinion/ai-hiring-discrimination.html (New York Times) https://www.hirevue.com/blog/hiring/automated-hiring-processes (Hirevue) https://vervoe.com/ai-in-resume-screening/ https://www.entelo.com/recruiting-automation (Entelo) https://hbr.org/2019/10/using-ai-to-eliminate-bias-from-hiring (Harvard Business Review) https://uwm.edu/news/automated-hiring-systems-could-be-making-the-worker-shortageworse/#:~:text=Automated%20hiring%20systems%20could%20lead%20to%20alienation &text=%E2%80%9CPublic%20opinion%20research%20suggests%20a,interacting%20w ith%20these%20systems%20alienating.%E2%80%9D https://www.reuters.com/article/global-tech-ai-hiring/analysis-ai-is-taking-over-job-hiringbut-can-it-be-racist-idUSL5N2NF5ZC (Reuters) https://www.forbes.com/sites/karadennison/2022/06/27/are-ai-recruitment-tools-ethical-a nd-efficient-the-pros-and-cons-of-ats/?sh=7aa9d602e4ff (Forbes) https://www.theverge.com/2021/9/6/22659225/automated-hiring-software-rejecting-viable -candidates-harvard-business-school (The Verge) Intro: Recruiting is an issue that many companies who are looking to scale face. There are many resources that must be put into a hire. It costs time, money, and effort from many employees to get new people in the door. AI is equipped to help with this problem as it can take in large amounts of data about what a good candidate looks like and what their resume should look like. This program is centered around saving time, costs, and “man power” for a particular company. What functionality does the service offer? (Give brief intro so one understands what they do) “Recruiting automation is a category of technology that allows companies to automate recruiting tasks and workflows so they can increase recruiter productivity, accelerate time-to-fill, reduce cost-per-hire and improve the overall talent profile of their organization. Recruiting automation is a subset of Human Capital Management (HCM), a category of enterprise software-as-a-service (SaaS) used by companies to manage all aspects of their workforce.” (Entelo, What is Recruiting Automation?) Data & Consent Describe the data which is collected How does the tech ask for user's consent? What (if any) are the secondary data uses The data collected is primarily coming from the resumes of people are applying to work at companies that have implemented these AI tools. The tech does not seem to explicitly ask for the user’s consent; they may include a disclaimer before the individual submits their job application, and the company might disclose that they use these tools but it does not seem as though most applicants are explicitly aware of what will occur. Many of the articles did not explicitly mention secondary data uses but the company could definitely leverage this data at a later point (i.e. sell it to recruiting websites, etc.) Beneficence What good / bad does this technology do? - fill in the chart below, give a good paragraph to describe each item - feel free to add rows as needed - to give a sense of scale, try to give a score to each positive or negative aspect Good Bad (+30) employers have wide discretion to decide which qualities are a “cultural fit” for their organization (-100) This tech has a major negative thing . It can lead to missing good candidates for a particular job which, firstly, does not allow the company to hire the best people for the job and also creates a large pool of talented people who are out there looking for a job. (+30) Lower cost (no need to hire people who hire someone else) (-60) Can lead to alienation. There is little regard to applicants. If the hiring process is automated, your resume can be completely alienated. What if someone needs a living wage? Only regard to the employer rather than the prospective employee. (+40) Reduces the amount of time and money a company spends on hiring. Up to 80% of a HR member’s work could be done by automated hiring. Can reinforce hiring biases and increase gender, racial, or other forms of discrimination. (+30) More accurate testing results (-10) Can misevaluate many other important qualitative and quantitative factors that play into identifying qualified candidates (+50) Creates consistency in the hiring process. If multiple different HR representatives are involved in the hiring process, they can judge candidates using their own personal feelings rather than solely looking at the qualifications that the candidate (-5) decreases the size of the workforce, many people lost jobs due to it is or is not meeting for the job. (+50) Could potentially be used to remove factors such as race/age/appearance and create a more neutral hiring process. (-10) can lead to feelings of stress or exhaustion and make it harder to truly relax when we need that rest (-10) Less care about morality and personality, just care about their visible achievements. (-20) In society, a more obvious standard of getting into good companies will be created. Many people will be forced to follow that resume and path to be successful. (-10) Can’t judge how they are passionate about their job (-30) More people cannot participate in the final assessment because of the lack of quantifiable competitiveness (-200) Person whose application is rejected would feel really really bad, because a ‘program’ just judge the achievements of one’s life and expels one. :(( How will the product affect the people who don’t use it? It’s very difficult to get a job, especially in tech or a big firm, due to the ubiquity of these products. Trying to take a stance against automated hiring procedures as a candidate is basically removing any opportunity to get a job. On the other side, these technologies allow corporations to down-size HR departments and efficiently process more resumes, which places companies with more traditional resume processes at a disadvantage. Justice - who will benefit from using our product and who might get hurt? ● employers who are trying to find others that fit the types of people they have already hired will benefit from automated hiring. - who is over/under represented in my data and how will it affect the product? ● those that are not represented in that company will be extremely underrepresented because they don’t fit the profile that already exists in that workplace, leading to discrimination of any other type of individual - does the product work equally well for different types of users? ● While these tools might work similarly for all employers, there is definitely bias depending on the candidate in question. Overall, this bias mirrors human bias during the hiring process. Recommendations What are some potential solutions which minimize the ethical concerns raised? Does your group think this tech is net positive or negative? What conflicts of interest exist in getting your recommendation adopted (e.g. who doesn't want to go along with your suggestion, why?) In training the data there are several key points that can be removed to further eliminate biases. First, names are a key indicator of someone's ethnicity, training a model that includes names may prefer names that are basic as hell to names that are cool and different. Next, limiting the model's knowledge of the location/address can ensure that someone's zip code or neighborhood does not affect whether or not they will get a job. Next, eliminating the age of the person on training sets can get rid of age bias. Maybe what this is showing us is that it is really hard to differentiate yourself on a resume. Especially if AI is reading our resumes, people will just start tailoring it to what the AI wants. Which seems a little backwards. Maybe we need to invent a new system of screenings that encapsulates yourself much better than a resume! Resumes kinda suck right? The person who is supposed to read them doesn't want to. And you don't want to make a resume, you just want the job. Maybe we should just start doing survivor or squid games competitions to see who gets a job. Cuz why not. Could be just as useful of a metric as resumes/ I would recommend using some mix of technology and humans to judge which is the best candidate.
