Artificial Intelligence (AI) is often seen as a revolutionary technology, capable of providing efficiency, accuracy, and opening new strategic opportunities. However, as companies benefit from the advantages of AI, a critical and sometimes overlooked challenge also arises: algorithmic fairness. Hidden biases in these systems can compromise not only the efficiency of business decisions but also lead to significant legal, ethical, and social consequences.
The presence of algorithmic biases can be explained by the very nature of AI itself, especially in machine learning. Models are trained with historical data, and when this data reflects biases or social distortions, the algorithms naturally end up perpetuating these biases. In addition to biases in the information, the algorithm itself can cause an imbalance in the weighting of factors, or in the data used as proxies, that is, data that replace the original information but are not ideal for that analysis.
An emblematic example of this phenomenon is found in the use of facial recognition, especially in sensitive contexts such as public security. Several Brazilian cities have adopted automated systems in an attempt 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. Research by MIT's Joy Buolamwini found that commercial algorithms have error rates above 30% for Black women, while for white men, the rate drops dramatically to less than 1%.
Brazilian legislation: more rigidity in the future
In Brazil, in addition to the General Data Protection Law (LGPD), the Legal Framework for AI (Bill No. 2338/2023) is also under consideration, which establishes general guidelines for the development and application of AI in the country.
Although not yet approved, this bill already signals rights that companies must respect, such as: the right to prior information (informing when the user is interacting with an AI system), the right to explanation 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 (for example, clearly indicating when a text or response was machine-generated) and audit 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 significant strategic issues. Biased algorithms have the potential to distort key decisions in internal processes such as recruitment, credit granting, and market analysis. For example, a branch performance analysis algorithm that systematically overestimates urban areas at the expense of peripheral regions (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 they will depend on an algorithmic governance framework, focusing on the diversity of the data used, transparency of processes, and the inclusion of diverse and multidisciplinary teams in technological development. By investing in diversity within technical teams, for example, companies can more quickly identify potential sources of bias, ensuring that different perspectives are considered and that errors are detected early.
Furthermore, 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 bias mitigation. Algorithms should not function as black boxes, but rather as transparent and explainable systems. When companies choose transparency, they gain the trust of customers, investors, and regulators. Transparency facilitates external audits, encouraging a culture of shared responsibility in AI management.
Other initiatives include adherence to frameworks and certifications for responsible AI governance. This includes creating internal AI ethics committees, establishing corporate policies for its use, and adopting international standards. For example, frameworks such as ISO/IEC 42001 (artificial intelligence 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 best practices recommended by the NIST (National Institute of Standards and Technology) of the USA, which guides algorithmic risk management, covering bias detection, data quality checks, and continuous model monitoring.
Specialized consulting plays a strategic role in this scenario. With expertise in responsible artificial intelligence, algorithmic governance, and regulatory compliance, these companies help organizations not only avoid risks but also turn equity into a competitive advantage. The work of these consultancies ranges from detailed risk assessments to the development of internal policies, including corporate training on AI ethics, ensuring that teams are prepared to identify and mitigate potential algorithmic biases.
In this way, mitigating algorithmic biases is not just a preventive measure, but a strategic approach. Companies that care about algorithmic fairness demonstrate social responsibility, strengthen their reputation, and protect themselves against legal sanctions and public crises. Impartial algorithms tend to provide more accurate and balanced insights, increasing the effectiveness of business decisions and strengthening organizations' competitive position in the market.
By Sylvio Sobreira Vieira, CEO & Head of Consulting at SVX Consulting
