The trend towards artificial intelligence (AI) and automated processes replacing workers is driven by advancements in technology that allow machines to perform tasks traditionally done by humans. AI systems and automation are increasingly efficient, cost-effective, and capable of handling repetitive or routine tasks with precision and speed. As a result, industries ranging from manufacturing to customer service are adopting these technologies to streamline operations, increase productivity, and reduce labor costs. While AI and automation offer benefits such as improved accuracy and scalability, they also raise concerns about job displacement and the need for reskilling or upskilling the workforce to adapt to changing job roles. None of this makes the worker that was fired and replaced with AI or automation feel any better about losing their job.
How do I prove wrongful termination based on discrimination?
Let’s start with the basics. Absent a contract, most employees are considered at-will, which means that they can be fired for any reason whether right or wrong, as long as that reason is not a violation of the law. Thus, to be a wrongful termination, an employee must point to an employment law that was violated by firing the employee.
Title VII of the Civil Rights Act of 1964 makes it unlawful to fire employees because of their race/color, gender, gender identity, sexual orientation, national origin, and religion. The Americans with Disabilities Act (“ADA”) makes it illegal to fire an employee because of a disability or medical condition, and the Age Discrimination in Employment Act of 1967 (“ADEA”) protects older workers from being fired because of their age.
Absent direct evidence employment discrimination cases, wrongful firing cases are typically proven by the employee initially presenting evidence that: (1) the employee belongs to one of the above protected classes; (2) the employee is qualified for the job or position; (3) the employee suffered an adverse employment action, such as being fired, demoted, losing out on a promotion, or suffering a pay cut; and (4) the employee was either treated less favorably than other similarly situated employees outside the protected class or was fired and replaced by another person outside the protected class.
Herein lies the first problem with a claim being based on being replaced by AI or an automated process. Under the fourth element, when an employee is replaced with automated systems or AI, the employee loses the ability prove that the replacement was “another person outside the protected class.” This does not doom a claim but limits the avenues for victory.
Best Wrongful Termination Lawyer Blogs on Point:
- Wrongful Termination: How Do I Prove That I Am Qualified For The Job?
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Can the reliance on AI or automation be a justifiable business reason to fire a worker?
Yes. If true, a company is entitled to make the decision to rely on AI or an automated process to eliminate jobs.
However, even if the employer states this as the reason for termination following the employee’s proof of the above prima facie elements of employment discrimination, the employee still has the opportunity to so pretext – that the reason is false or did not really motivate the employer to take the adverse employment action.
Best Wrongfully Fired Attorney Blogs on Point:
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Can you share an example of a case dealing with wrongful termination and AI?
Of course! In Akridge v. Alfa Ins. Companies, No. 22-12045, 2024 WL 652747 (11th Cir. Feb. 16, 2024), the employee, Jennifer Akridge, argued that her former employer, Alfa Mutual Insurance Company, wrongfully fired her because to avoid paying healthcare costs related to her multiple sclerosis (“MS”) and severe migraines, which, if true, would amount to disability discrimination. Alfa primarily defended its decision to fire her by asserting that as a result of most of Akridge’s duties became automated, her position was no longer needed, and that it needed to cut business expenses as a result of investing in the development of the automation. Alfa further argued that the decision makers had no know of the costs of her health care.
In 1989, Akridge began her employment at Alfa, and in 1993, she was diagnosed with Multiple Sclerosis (MS) which caused severe migraines. Her employer knew of her disability and she had no employment problems as a result. Over the years, she progressed within the company, eventually reaching a strategic coordinator position in the auto underwriting department by 2015. Her responsibilities included overseeing the strategic underwriting program, collaborating with agents and district managers to identify profitable policies, preparing reports, creating manuals, verifying proofs of insurance, assisting with rate filings, and conducting workshops. Akridge consistently received positive performance evaluations and was recognized as employee of the year in the mid-nineties.
From 2012 to 2016, Alfa embarked on the development of Guidewire, a novel computer program that significantly altered the company’s business operations by automating various functions. One notable aspect of Guidewire was its capability to provide agents and district managers with access to the strategic underwriting data that Akridge had previously collected and disseminated. The development of Guidewire cost Alpha between $150 and $160 million. Concurrently, Alfa increased the use of recorded webinars and eLearning to train employees and phased out the workshops that had been presented by Akridge.
In December 2016, Alpha informed Akridge that her position at Alfa was being eliminated affective immediately and cited the company’s expenditure on developing Guidewire and the broader objective of reducing business expenses as the reasons for this decision. Notably, during the meeting, there was no discussion of Akridge’s disabilities or healthcare.
Because Akridge could not point to being replaced by a non-disabled employee with lesser healthcare costs, she attempted to point to other similarly situated non-disabled employees who she believed were treated more favorably. First, Akridge compares herself to Hillery McCaleb, noting similarities in their roles assisting unprofitable agents, managing manuals, and engaging in state insurance department filings. However, Akridge admits that they operated in different segments of the underwriting domain – McCaleb primarily focused on property/home aspects of underwriting while Akridge specialized in the auto sector. Additionally, there was evidence that the automation in home underwriting was less extensive compared to auto underwriting, and certain special projects related to home underwriting, unlike those in the auto sector, could not be automated. Similarly, Akridge pointed to other employees in other departments but could not identify what their duties were, nor the impact Guidewire had on eliminating their job duties.
This problem highlights the importance of not only identifying potential competitors but completing sufficient discovery to establish why they are similarly situated. Had discovery revealed that Guidewire resulted in only older or disabled employees being fired, it may have created enough of question to get passed summary judgment.
But the United States Court of Appeals for the Eleventh Circuit opted not to decide the case on this factor. Instead, it focused on the stated business reason and Akridge’s inability to demonstrate pretext:
As background, “[w]e have made clear that an employer may fire an employee for a good reason, a bad reason, a reason based on erroneous facts, or for no reason at all, as long as its action is not for a discriminatory reason.” In our review of an employer’s proffered reasons for an adverse employment decision, we “do not sit as a super-personnel department that reexamines an entity’s business decisions,” and we may not “analyze whether an employer’s proffered reasons are prudent or fair.”
Here, Alfa’s decisionmakers eliminated Akridge’s position to reduce business expenses because her position was no longer needed. As the decisionmakers testified, the strategic underwriting program Akridge worked on was now automated, including the report she created, and agents and district managers themselves could now access that information over the computer. Akridge confirmed that “[w]orking with the agents and district managers” using “data from the reports was the majority of [her] day.” The decisionmakers also testified that Akridge’s workshops were meant to introduce agents to information in the strategic underwriting report, and that those workshops were no longer necessary and were replaced by increased webinars and eLearning.
Id. at *10-11 (internal citations omitted). In short, the Eleventh Circuit Court of Appeals held that firing an employee whose entire job has been replaced by technology was a legitimate business reason that Akridge could not refute.
It does not appear that Akridge argued that the implementation of the automated process had a disparate impact on disabled or older workers. Maybe discovery did not support that argument, or it is possible that such argument was not even considered. Thus, while simply being replaced by AI or an automated process does not create a wrongful termination claim, it does not necessarily destroy an otherwise supportable claim for employment discrimination and wrongful termination.
Best Disability Discrimination Law Firm Blogs on Point:
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- How Do You Prove Causation In Wrongful Termination Cases?
Disclaimer:
The information provided on this race, gender, age, and disability blog is intended for general informational purposes only and should not be construed as legal advice on your employment situation nor a potential wrongful termination claim. The content of this blog is based on specific scenarios and does not reflect specific legal advice tailored to your individual work circumstances. Readers should not act upon this information without seeking professional legal counsel. Furthermore, laws and legal interpretations may vary depending on jurisdiction and specific case details. We make no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability, or availability of the information contained on this blog. Therefore, any reliance you place on such information is strictly at your own risk.