Artificial Intelligence (AI) is often seen as a revolutionary technology, capable of providing efficiency, precision and opening new strategic opportunities. However, as companies benefit from the advantages of AI, a critical challenge also arises and, sometimes, neglected: algorithmic equity. Hidden biases in these systems can compromise not only the efficiency of business decisions, but generate legal consequences, significant ethical and social.
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 prejudices or social distortions, algorithms naturally end up perpetuating these biases. In addition to biases in information, the algorithm itself can cause an imbalance in the weighting of factors performed, or in the data used as a proxy, that is, data that replaces the original information, but they 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 analyses show that these algorithms often make significant errors, especially when identifying individuals from specific ethnic groups, like black people. Studies by researcher Joy Buolamwini, do MIT, it was pointed out 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: more rigidity in the future
In Brazil, além da Lei Geral de Proteção de Dados (LGPD) também está em tramitação o Marco Legal da IA (PL nº 2338/2023), that 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 (to inform when the user is interacting with an AI system), right to explanation of automated decisions, right to contest algorithmic decisions and right to non-discrimination due to algorithmic biases.
These points will require companies to implement transparency in generative AI systems (for example, making it clear when a text or response was generated by a machine) and audit mechanisms to explain how the model arrived at a certain 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 granting and market analysis. For example, an algorithm for branch performance analysis that systematically overestimates urban areas to the detriment 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 structure, 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 in technical teams, for example, companies can identify potential sources of bias more quickly, ensuring that different perspectives are considered and that failures are detected early
Furthermore, the use of continuous monitoring tools is essential. These systems help detect the drift of algorithmic biases 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 rather as clear and explainable systems. When companies choose transparency, gain the trust of clients, 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 ethics committees in AI, define corporate policies for its use, and adopt international standards. For example, frameworks como: a ISO/IEC 42001 (gestão de inteligência artificial, a ISO/IEC 27001 (segurança da informação) e ISO/IEC 27701 (privacidade) ajudam a estruturar controles nos processos de dados usados por IA generativa. Another example is the set of practices recommended by NIST (National Institute of Standards and Technology) of the USA that guides algorithmic risk management, covering bias detection, data quality checks and continuous monitoring of models
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 to avoid risks, but transforming equity into a competitive advantage. The work of these consultancies ranges from detailed risk assessments, up to the development of internal policies, going through corporate training on AI ethics, ensuring that teams are prepared to identify and mitigate potential algorithmic biases
In this way, the mitigation of algorithmic biases is not just a preventive measure, but rather a strategic approach. Companies that care about algorithmic equity demonstrate social responsibility, reinforce 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 the competitive position of organizations in the market
By Sylvio Sobreira Vieira, CEO & Head Consulting of SVX Consulting
