Smart decisions?: How to detect and prevent bias in AI

The legal future anticipates that biases will be increasingly debated in courts and tribunals around the world.

That's why it's important for companies to understand how these biases occur, how to identify them, and what to do to prevent them. Some of the most common biases include gender bias, age bias, ethnicity or socioeconomic origin bias, disability bias, linguistic or cultural bias, marital status or maternity bias, and physical appearance bias. However, there are other biases that often go unnoticed, such as those derived from the translation of language in inputs, the limited geographical scope of the samples used by AI, or operational errors in processing. In addition, companies should pay attention to how they formulate their requests to AI, as poorly crafted prompts can also lead to biased or inaccurate results.

It is also becoming increasingly common for companies to make decisions based on artificial intelligence, which carries the risk of replicating its biases. Therefore, they must be fully aware of the impact on fundamental rights and act responsibly when implementing and using these technologies.

When these biases are not detected or corrected, the consequences for the company are real. Wrong decisions are made, diversity is limited, competitive talent is lost, and operational efficiency is directly affected. In addition, systematic errors in automated processes can lead to internal complaints, loss of trust from staff or customers, and even exposure in the media or social networks that damage reputation and credibility. All of this can translate into additional costs, legal processes, or lost strategic opportunities.

To reduce these risks, companies must adopt concrete, sustainable and cross-cutting measures.

Some key recommendations include:

• Conduct impact assessments on the use of technology in specific processes, i.e. specific decisions.
• Request technical reports from technology providers detailing how the model was trained, what variables it uses, and what bias or impact tests it has passed, incorporating contractual clauses that require the delivery of this information and updates.
• Document all relevant automated decisions, including how they are generated, what criteria they follow, what controls are in place, and how a person can request review or correction.
• Include explicit warnings in internal processes and forms, informing when a decision will be fully or partially automated, and offering a clear channel to request review by a person.
• Implement an internal policy on the responsible use of AI, which establishes principles of fairness, non-discrimination, and human review in sensitive decisions, and which is binding on all areas that develop or contract technology.
• Conduct periodic audits of the algorithmic systems used, with the participation of legal, compliance, technology and diversity teams, to identify biases and propose adjustments before they generate real consequences.
• Train HR, technology, legal, and leadership teams in bias identification and AI operation, so they don't delegate complex decisions without assessing the human context.
• Create a cross-sectional review committee or group that oversees the implementation of automated tools, evaluates results, and detects anomalous patterns or unforeseen discriminatory effects.
• Design internal reporting channels where employees or candidates can alert leadership to unfair or unclear automated decisions, and follow up on them with defined and transparent procedures.
• Regularly update the data that feeds AI systems, preventing them from reflecting obsolete patterns, stereotypes or dynamics that are not representative of the current environment.

Artificial intelligence is a powerful tool for scaling processes and making more informed decisions, but only if it is used with proper controls. Preventing bias is not a technical option, it is a business responsibility. Companies that take action now will not only reduce their risks, but will also strengthen the quality of their decisions, internal and external trust, and position themselves as fair, modern, and prepared organizations for a demanding environment.

Authored by Guillermo Larrea and Victoria Villagómez.