Artificial Intelligence (AI) is often seen as a revolutionary technology, capable of providing efficiency, accuracy and opening up new strategic opportunities. However, while companies benefit from the advantages of AI, a critical and sometimes neglected challenge also arises: algorithmic equity. Hidden biases in these systems can compromise not only the efficiency of business decisions, but 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 these data reflect prejudices or social distortions, algorithms naturally end up perpetuating these biases. In addition to the biases in the information, the algorithm itself can bring a decompensation in the weighting of factors performed, or in the data used as proxy, 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 safety. Several Brazilian cities have adopted automated systems in order to increase the effectiveness of police actions, but analyzes 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 pointed out 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 of AI (PL nº 2338/2023) is also pending, 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: right to prior information (inform when the user is interacting with an AI system), the right to explain automated decisions, the right to contest algorithmic decisions and the right to non-discrimination by algorithmic bias.
These points will require companies to implement transparency in generative AI systems (for example, making it clear when a text or response was machine generated) and audit mechanisms to explain how the model reached a certain output.
Algorithmic governance: the solution for 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 granting and market analysis. For example, a branch performance analysis algorithm that systematically overestimates urban regions at the expense of peripheral regions (due to incomplete data or prejudices) can lead to misdirected investments. Thus, hidden biases undermine the effectiveness of data-driven strategies, causing executives to make decisions based on partially wrong information.
These biases can be corrected, but they will depend on an algorithmic governance structure, focusing on the diversity of the data used, transparency of processes and the inclusion of diversified 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 that different perspectives are considered and that failures are detected early.
In addition, the use of continuous monitoring tools is critical. These systems help detect the drift of real-time algorithmic biases, enabling quick adjustments and minimizing negative impact.
Transparency is another essential practice in mitigating biases. Algorithms should not function as black boxes, but rather as clear and explainable systems. When companies opt for 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 joining frameworks and responsible AI governance certifications. This includes creating internal AI Ethics Committees, defining corporate policies for their 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 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 artificial intelligence, algorithmic governance and regulatory compliance, these companies help organizations not only avoid risks, but transform equity into a competitive advantage. The performance of these consultancies ranges from detailed risk assessments, to the development of internal policies, through corporate training on AI ethics, ensuring that teams are prepared to identify and mitigate possible algorithmic prejudices.
In this way, the mitigation of algorithmic biases is not only a preventive measure, but a strategic approach. Companies that care about algorithmic equity demonstrate social responsibility, reinforce their reputation and protect themselves against legal sanctions and public crises. Unbiased algorithms tend to offer more accurate and balanced insights, increasing the effectiveness of business decisions and strengthening the competitive position of organizations in the market.
by Sylvio Sobreira Vieira, CEO & Head Consulting by SVX Consultoria
