Artificial Intelligence (AI) is often seen as a revolutionary technology, capable of providing efficiency, precision, and opening new strategic opportunities. However, while companies benefit from AI’s advantages, a critical and sometimes overlooked challenge arises: algorithmic fairness. Hidden biases in these systems can not only compromise the efficiency of business decisions but also generate significant legal, ethical, and social consequences.
The presence of algorithmic biases can be explained by the nature of AI itself, especially in machine learning. Models are trained with historical data, and when this data reflects societal prejudices or distortions, the algorithms naturally end up perpetuating these biases. Beyond biases in the data, the algorithm itself can introduce imbalances in the weighting of factors or in the proxy data used—i.e., data that replaces original information but is not ideal for that analysis.
An emblematic example of this phenomenon is found in the use of facial recognition, particularly in sensitive contexts like public safety. Several Brazilian cities have adopted automated systems to increase the effectiveness of police actions, but analyses show that these algorithms often make significant errors, especially when identifying individuals from specific ethnic groups, such as Black people. Studies by MIT researcher Joy Buolamwini found that commercial algorithms have error rates above 30% for Black women, while for white men, the rate drops drastically to less than 1%.
Brazilian legislation: stricter rules in the future
In Brazil, in addition to the General Data Protection Law (LGPD), the AI Legal Framework (Bill No. 2338/2023) is also under discussion, establishing general guidelines for the development and application of AI in the country.
Although not yet approved, this bill already signals rights that companies will need to respect, such as: the right to prior information (informing users when they are interacting with an AI system), the right to explanations of automated decisions, the right to contest algorithmic decisions, and the right to non-discrimination due to algorithmic biases.
These points will require companies to implement transparency in generative AI systems (e.g., making it clear when a text or response was machine-generated) and auditing mechanisms to explain how the model arrived at a particular output.
Algorithmic governance: the solution to biases
For companies, algorithmic biases go beyond the ethical sphere—they become relevant strategic problems. Biased algorithms have the potential to distort essential decisions in internal processes such as recruitment, credit approval, and market analysis. For example, a branch performance analysis algorithm that systematically overestimates urban regions at the expense of peripheral ones (due to incomplete data or biases) can lead to misdirected investments. Thus, hidden biases undermine the effectiveness of data-driven strategies, causing executives to make decisions based on partially incorrect information.
These biases can be corrected but will depend on an algorithmic governance framework, focusing on data diversity, process transparency, and the inclusion of diverse and multidisciplinary teams in technological development. By investing in diversity in technical teams, for example, companies can more quickly identify potential sources of bias, ensuring different perspectives are considered and flaws are detected early.
Additionally, the use of continuous monitoring tools is essential. These systems help detect algorithmic bias drift in real time, enabling quick adjustments and minimizing negative impact.
Transparency is another essential practice in mitigating biases. Algorithms should not function as black boxes but as clear and explainable systems. When companies opt for transparency, they gain the trust of customers, investors, and regulators. Transparency facilitates external audits, fostering a culture of shared responsibility in AI management.
Other initiatives include adopting frameworks and certifications for responsible AI governance. This includes creating internal AI ethics committees, defining corporate policies for its use, and adopting international standards. For example, frameworks like ISO/IEC 42001 (AI management), ISO/IEC 27001 (information security), and ISO/IEC 27701 (privacy) help structure controls in the data processes used by generative AI. Another example is the set of practices recommended by the U.S. National Institute of Standards and Technology (NIST), which guides algorithmic risk management, covering bias detection, data quality checks, and continuous model monitoring.
Specialized consultancies play a strategic role in this scenario. With expertise in responsible AI, algorithmic governance, and regulatory compliance, these firms help organizations not only avoid risks but also turn fairness into a competitive advantage. Their work ranges from detailed risk assessments to developing internal policies and corporate training on AI ethics, ensuring teams are prepared to identify and mitigate potential algorithmic biases.
Thus, mitigating algorithmic biases is not just a preventive measure but a strategic approach. Companies that prioritize algorithmic fairness demonstrate social responsibility, reinforce their reputation, and protect themselves from legal sanctions and public crises. Unbiased algorithms tend to offer more precise and balanced insights, increasing the effectiveness of business decisions and strengthening organizations’ competitive position in the market.
By Sylvio Sobreira Vieira, CEO & Head Consulting at SVX Consulting
