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Howard Levitt: Using AI does not transfer legal responsibility from the employer to the machine
Howard Levitt Employers are rapidly adopting systems that rank applicants, assess performance, identify unusual employee behaviour and recommend management action. decision in your workplace may be the one no one can explain.
Employers are rapidly adopting systems that rank applicants, assess performance, identify unusual employee behaviour and recommend management action. The attraction is obvious. AI can process vastly more information than a manager, almost instantaneously. But using AI does not transfer legal responsibility from the employer to the machine.
If it contributes to a hiring, discipline, accommodation, promotion or termination decision, the employer remains responsible for complying with the law. The critical question is how much the AI influenced the decision and to what extent the employer critiqued the analysis before implementing its conclusions. AI materially influences an employment decision when its output plays a meaningful role in determining what the employer does.
It might narrow the number of candidates considered for a job, flag an employee for investigation, generate a score management relies on or recommend a course of action. That is different from using AI to draft a job advertisement, summarize a meeting or perform some other administrative task that does not meaningfully affect the decision. The trouble begins when a manager stops using AI to help with the work and starts using it to decide what the work means.
Consider an employer that uses AI to identify employees whose performance supposedly warrants further scrutiny. An employee is repeatedly flagged. The manager investigates and ultimately terminates them. The employer may have an uncomfortable answer.
"What did the system identify? " If the answer is a collection of thousands of data points processed through a proprietary model that the employer cannot reconstruct, the employer may have an extraordinarily sophisticated piece of technology and an extraordinarily weak justification for its decision. Traditional management decisions usually leave a trail that someone can explain. A manager observed something.
A customer complained. A supervisor reviewed the employee's work. An investigation uncovered particular conduct. AI can obscure that trail.
It can identify a statistical pattern without establishing what caused it. An employee labelled"high risk" for misconduct has not necessarily committed misconduct. An applicant screened out by an algorithm has not necessarily been shown to be unsuitable. A prediction is not a fact.
A correlation is not misconduct. A risk score is not proof. And a recommendation is not a finding. The greater danger is what happens next.
Once managers receive an AI-generated conclusion, they may start looking for evidence that confirms it rather than asking whether the conclusion is correct. That is an old management problem wearing new technology. There is another problem employers should consider before buying these systems. What happens when the vendor cannot explain the output either?
Employers may assume that purchasing a sophisticated system means they have purchased sophisticated evidence. But they have not. Before deploying AI in a consequential employment process, an employer should understand what the system is designed to predict or identify, what information it uses, what limitations the vendor acknowledges and whether the employer can obtain enough information to investigate an adverse result. A black box may be acceptable for some low-stakes administrative functions.
It is much harder to justify when someone's job depends on its output. Employers should also consider what happens when the system changes. AI tools can be updated, retrained or replaced. An employer investigating a decision made with the help of AI months after the fact may be dealing with a system that no longer produces the same result from the same information.
That makes record keeping particularly important. The employer should preserve the relevant output and enough surrounding information to establish what the system produced at the time, subject to applicable privacy and retention requirements. Before allowing AI to materially influence a consequential employment decision, employers should be able to answer five questions.
First, what role did the AI play? Was it performing an administrative task, or did its output influence management's decision?
Second, what information did it use? The employer should know what information went into the system and preserve the relevant output for potential future litigation.
Third, what does the AI actually establish? A score, prediction or recommendation should be treated as an investigative lead, not as proof of poor performance, dishonesty, misconduct or unsuitability. Fourth, what did the human decision maker independently verify? The employer should be able to identify the facts supporting the decision and anything that might undermine it.
Fifth, can the decision be explained in ordinary language? The decision maker should be able to describe what the AI contributed, what was independently established and explain why they decided to act in the way they did. Those questions should also shape the employer's dealings with the vendor.
"Our algorithm is proprietary" may protect, but it should not leave the employer unable to understand a consequential employment decision. The employer does not need the source code. It does need enough information to understand the system's purpose, relevant inputs, limitations and output, so that it can determine whether a result makes sense in the particular case. AI may become an extraordinarily powerful management tool.
But when the lawsuit arrives, the employer must still be able to explain what happened, what it knew and why it acted. Technology can assist management decisions. It cannot take responsibility for them.
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