Agency Law and the Workday Lawsuit
2024-08-10 · NACSHR · 阅读约 26 分钟
- 求职者 Derek Mobley 起诉 Workday,称其 AI 筛选工具因种族、年龄和残疾对他歧视,2017–2024 年申请逾百个职位全部被拒。
- 加州联邦法院 7 月 12 日裁定案件可继续,认为 Workday 可能作为雇主的“代理人”承担责任,因雇主将自动筛除申请人等招聘职能委托给其 AI 工具。
- 法院允许原告以“差别影响”理论推进诉讼;一次凌晨 12:55 提交申请、1:50 即被拒的经历被用来说明缺乏人工审核。
- 法院也指出并非所有软件供应商都会被视为代理人,仅帮助雇主排序申请人的简单表格系统不应被卷入歧视诉讼。
- 目前案件仅在诉答阶段,责任归属尚无定论;建议雇主审查 AI 供应商的训练数据与算法,定期审计,并与律师商定赔偿条款。
AI 根据原文整理,以发布日期为准(2024-08-10)
本文目录(1 节)
文章讨论了在Workday诉讼中,代理法的相关法律问题。原告声称,Workday的AI筛选工具因种族、年龄和残疾而对他进行了歧视。这起案件提出了HR技术供应商是否可以对歧视性结果直接负责的问题。法律的复杂性包括AI在招聘决策中的角色、代理责任以及对雇主和AI开发者的潜在影响。此案件提醒雇主在实施AI招聘工具时要谨慎,并确保避免法律风险。AI开发者也必须确保其产品无歧视行为,因为该诉讼可能会树立重要的法律先例。

Editor's Note
Agency Law and the Workday Lawsuit
Agency law is so old that it used to be called master and servant law. (That's different from slavery, where human beings were considered the legal property of other humans based on their race, gender, and age, which is partly why we have discrimination laws.) Today, agency laws refer to principals and agents. All employees are agents of their employer, who is the principal. And employers can have nonemployee agents too when they hire someone to do things on their behalf. Generally, agents owe principals a fiduciary duty to act in the principal's best interest, even when that isn't the agent's best interest. Agency laws gets tricky fast because you have to figure out who is in charge, what authority was granted, whether the person acting was inside or outside that authority, what duty applies, and who should be held responsible as a matter of fairness and public policy. Generally, the principal is liable for the acts of the agent, sometimes even when the agent acts outside their authority. And agents acting within their authority are rarely liable for their actions unless it also involves intentional wrongs, like punching someone in the nose. Enter discrimination, which is generally a creature of statute that may or may not be consistent with general agency law even when the words used are exactly the same. Discrimination is generally an intentional wrong, but employees are not usually directly liable for discrimination because making employment decisions is part of the way employment works and the employer is always liable for those decisions. The big exception is harassment because harassment, particularly sexual harassment, is never part of someone's job duties. So in harassment cases, the individual harasser is liable but the employer may not be unless they knew what was going on and didn't do anything about it. It's confusing and makes your head hurt. And that's just federal discrimination law. Other employment laws, both state and federal, deal with agent liability differently. Now, let's move to the Workday lawsuit. In that case, the plaintiff is claiming that Workday was an agent of the employer, but not in the sense of someone the employer was directing. They are claiming that Workday has independent liability as an employer too because they were acting like an employer in screening and rejecting applicants for the employer. But that's kinda the whole point of HR Technology—to save the employer time and resources by doing some of the work. The software doesn't replace the employer's decision making and the employer is going to be liable for any discrimination regardless of whether and how the employer used their software.
A California federal court just allowed a frustrated job applicant to proceed with an employment discrimination lawsuit against an AI-based vendor after more than 100 employers that use the vendor’s screening tools rejected him. The judge’s July 12 decision allows the class action against Workday to continue based on employment decisions made by Workday’s customers on the theory that Workday served as an “agent” for all of the employers that rejected him and that its algorithmic screening tools were biased against his race, age, and disability status. The lawsuit can teach valuable lessons to employers and AI developers alike. What are five things that employers can learn from this case, and what are five things that AI developers need to know?
AI Job Screening Tool Leads to 100+ Rejections
Here is a quick rundown of the allegations contained in the complaint. It’s important to remember that this case is in the very earliest stages of litigation, and Workday has not yet even provided a direct response to the allegations – so take these points with a grain of salt and recognize that they may even be proven false.
- Derek Mobley is a Black man over the age of 40 who self-identifies as having anxiety and depression. He has a degree in finance from Morehouse College and extensive experience in various financial, IT help-desk, and customer service positions.
- Between 2017 and 2024, Mobley applied to more than 100 jobs with companies that use Workday’s AI-based hiring tools – and says he was rejected every single time. He would see a job posting on a third-party website (like LinkedIn), click on the job link, and be redirected to the Workday platform.
- Thousands of companies use Workday’s AI-based applicant screening tools, which include personality and cognitive tests. They then interpret a candidate’s qualifications through advanced algorithmic methods and can automatically reject them or advance them along the hiring process.
- Mobley alleges the AI systems reflect illegal biases and rely on biased training data. He notes the fact that his race could be identified because he graduated from a historically Black college, his age could be determined by his graduation year, and his mental disabilities could be revealed through the personality tests.
- He filed a federal lawsuit against Workday alleging race discrimination under Title VII and Section 1981, age discrimination under the ADEA, and disability discrimination under the ADA.
- But he didn’t file just any type of lawsuit. He filed a class action claim, seeking to represent all applicants like him who weren’t hired because of the alleged discriminatory screening process.
- Workday asked the court to dismiss the claim on the basis that it was not the employer making the employment decision regarding Mobley, but after over a year of procedural wrangling, the judge gave the green light for Mobley to continue his lawsuit.
- Vet Your Vendors – Ensure your AI vendors follow ethical guidelines and have measures in place to prevent bias before you deploy the tool. This includes understanding the data they use to train their models and the algorithms they employ. Regular audits and evaluations of the AI systems can help identify and mitigate potential biases – but it all starts with asking the right questions at the outset of the relationship and along the way.
- Work with Counsel on Indemnification Language – It’s not uncommon for contracts between business partners to include language shifting the cost of litigation and resulting damages from employer to vendor. But make sure you work with counsel when developing such language in these instances. Public policy doesn’t often allow you to transfer the cost of discriminatory behavior to someone else. You may want to place limits on any such indemnity as well, like certain dollar amounts or several months of accrued damages. And you’ll want to make sure that your agreements contain specific guidance on what type of vendor behavior falls under whatever agreement you reach.
- Consider Legal Options – Should you be targeted in a discrimination action, consider whether you can take action beyond indemnification when it comes to your AI vendors. Breach of contract claims, deceptive business practice lawsuits, or other formal legal actions to draw the third party into the litigation could work to shield you from shouldering the full responsibility.
- Implement Ongoing Monitoring – Regularly monitor the outcomes of your AI hiring tools. This includes tracking the demographic data of applicants and hires to identify any patterns that may suggest bias or have a potential disparate impact. This proactive approach can help you catch and address issues before they become legal problems.
- Add the Human Touch – Consider where you will insert human decision-making at critical spots along your hiring process to prevent AI bias, or the appearance of bias. While an automated process that simply screens check-the-box requirements such as necessary licenses, years of experience, educational degrees, and similar objective criteria is low risk, completely replacing human judgment when it comes to making subjective decisions stands at the peak of riskiness when it comes to the use of AI. And make sure you train your HR staff and managers on the proper use of AI when it comes to making hiring or employment-related decisions.
- Commit to Trustworthy AI – Make sure the design and delivery of your AI solutions are both responsible and transparent. This includes reviewing marketing and product materials.
- Review Your Work – Engage in a risk-based review process throughout your product’s lifecycle. This will help mitigate any unintended consequences.
- Team With Your Lawyers – Work hand-in-hand with counsel to help ensure compliance with best practices and all relevant workplace laws – and not just law prohibiting intentional discrimination, but also those barring the unintentional “disparate impact” claims as we see in the Workday lawsuit.
- Develop Bias Detection Mechanisms – Implement robust testing and validation processes to detect and eliminate bias in your AI systems. This includes using diverse training data and regularly updating your algorithms to address any identified biases.
- Lean Into Outside Assistance – Meanwhile, collaborate with external auditors or third-party reviewers to ensure impartiality in your bias detection efforts.
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