Using artificial intelligence to screen candidates, measure performance or recommend terminations does not shift responsibility to the provider. The labor decision remains subject to rules on non-discrimination, dignity, data protection and business justification. In 2026, a prudent organization must know which model it uses, what information feeds it and when a person intervenes. This text proposes controls to take advantage of the technology without turning it into a black box. It is informational material and does not constitute legal advice.
The framework that already binds
The Federal Labor Law prohibits distinctions that nullify or impair rights on protected grounds and requires conditions compatible with decent work. Personal-data legislation also governs the collection, profiling, retention and security of applicants' and employees' information, with greater caution regarding sensitive data. Although proposals on artificial intelligence may guide the public conversation, they do not by themselves replace the rules in force. The labor regulation of digital platforms does contain special duties of algorithmic transparency and human review, but it must not be presented as a general rule for every employer. Outside that regime, it serves as a practical reference, while the specific obligation derives from the use, its effects and the applicable framework.
Govern before automating
Each tool must have an internal owner, a defined purpose and a prior assessment of need. The company needs to know what variables it uses, where they come from, how long they are kept and whether they function as proxies for age, disability, sex, origin or another protected condition. Tests must compare results across relevant groups, review errors and be repeated when the model, the data or the position changes. The contract with the provider must cover instructions, security, sub-processors, audit, continuity and the return or deletion of data. It is also advisable to prevent the system from reusing files to train models without authorization and to require sufficient documentation to explain a recommendation in understandable language.
The decision and its review
Human intervention must be real: a trained person must be able to question the output, consult context and depart from it. For adverse decisions, it is advisable to keep the version of the system, relevant criteria, sources consulted, the review carried out and the final reason, avoiding the retention of unnecessary data. The affected person must have an accessible channel to point out errors and provide information, without complaining leading to retaliation. Human resources and legal must periodically review whether the model produces disproportionate exclusions or encourages practices incompatible with health and safety. When a reliable explanation is not possible, the use of the tool in high-impact decisions must be paused or limited to administrative support.
Key points
- The employer retains responsibility even if the recommendation comes from a third party.
- An apparently neutral variable can produce discriminatory results.
- Human review requires authority, information and time to change the decision.
- Changes of model or data require new tests and documentation.
What to review
- Inventory the AI systems used in recruitment, evaluation, discipline and termination.
- Assess purpose, data, bias, security and explainability before each deployment.
- Create a human-review process and a channel to correct data or challenge results.