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

We are in 2025: some contact centers already have advanced technologies for analyzing customer experience (CX, in the English acronym) and processes to analyze the Voice of the Customer (VoC) – meanwhile, these data, they are quite rich, are rarely used to assess the performance of the operation. Instead of that, we are still using traditional quality assurance metrics for this measurement

Traditionally, contact centers measure quality assurance through metrics such as average response time, average service time, first call resolution rate, possibility of the client recommending the service, and the customer effort score in being served. Relevant data that can be extracted from CX analysis has not been used to assess service quality. Why

Because even with so much information, without a solution, appropriate vision and strategy, contact centers end up becoming a "black hole" of data

Without the proper treatment, the data remains fragmented in silos, making it difficult to have a holistic view of performance and customer experience

Data from various channels, like phone calls, e-mails, chats and social networks, often they are not correlated effectively, resulting in superficial and disconnected analyses. Furthermore, the lack of standardization in the collection and processing of these data can generate inconsistencies and harm the quality of the information used in evaluations

According to the Brazilian Association of Teleservices (ABT), the national contact center market employs millions of people and generates considerable amounts, especially after the growth of e-commerce and the digitization of consumer relationship processes. In this complex reality, the search for efficiency is no longer limited to reducing operating costs, but to ensure a more satisfying experience for the customer and to collect valuable insights for strategic decision-making

Quality Intelligence: how to measure

In last June, an analytical report from Gartner proposed a completely new measurement for contact centers: Quality Intelligence

The report prepared by the company brings some interesting insights, results of a survey conducted by Gartner with leaders in support services and contact centers. The first point is that only 19% of respondents consider the agent's performance as the main driver of service quality assurance, while 52% highlight CX and VoC as essential measures

Furthermore, the quality measurement processes today end up focusing on the analysis of voice channels, putting digital interactions aside. To complete this scenario, at least 85% of leaders rely solely on manual evaluations

Fundamentally, the measurement of Quality Intelligence in the contact center brings together three main information flows: traditional quality analysis data; Speech Analytics data, that bring sentiment analysis, identify the emotional tone of the conversations, and allows companies to better understand customer reactions; and the VoC data, that represent the feedback provided directly by the customer

In this sense, Quality Intelligence is an innovative approach that integrates advanced technologies and holistic strategies, transforming the vast volume of data from the contact center into actionable insights – and this happens because this analytical methodology not only consolidates data from different communication channels, but also applies advanced analyses to identify patterns and trends that can significantly improve the overall service performance

Furthermore, Quality Intelligence enables the correlation of data from various sources, like phone calls, e-mails, chats and interactions on social media. By unifying this information, it is possible to obtain a more complete and accurate view of the customer experience, allowing companies to take proactive actions to solve problems and improve customer satisfaction. This unification is possible due to better standardization in data collection and processing, with the establishment of uniform criteria for the capture and analysis of information, eliminating inconsistencies and ensuring that all data is considered in the evaluations

How a CX platform can contribute to the process

It is possible to note that the Quality Intelligence approach has roots in technological advancements that enable the analysis of large amounts of data in an agile manner

While in the past it was common to evaluate customer service agents through modest samples of calls or interactions, today there are tools that analyze 100% of the contacts, let them be by voice, chat, email or social media

The most current CX platforms offer robust tools for data collection, integration and analysis of data from multiple communication channels. The use of an intelligent customer experience management platform allows contact centers to establish uniform criteria and optimize their processes, resulting in a more cohesive and satisfying customer experience

In general, more robust CX solutions include integrated Speech Analytics solutions – and discourse and sentiment analysis can, for example, predict which customers are more likely to cancel a service or which type of agent generates the highest satisfaction among the public that makes contact. If a certain pattern of conversation or approach proves to be more efficient, these insights can be used to train the entire team, raising the overall level of performance

Thus, Quality Intelligence not only measures what happened, but indicate which actions can be implemented for better results. This type of intervention is essential for managers who need to make high-impact decisions in competitive environments. In the Brazilian scenario, in which the turnover of professionals is notably high, this type of insight provides support for retention strategies, more refined training and selection of personnel

With all these considerations, it is possible to conclude that Quality Intelligence represents a significant evolution in the way contact center performance is viewed

The analysis no longer focuses solely on evaluating productivity metrics, but in understanding emotional factors, contextual and strategic aspects present in the relationship between companies and customers. This broader and deeper understanding has the potential to directly impact financial results, consumer satisfaction and institutional image

Despite the initial effort to migrate from a merely quantitative model to an integrated assessment of data and behaviors, the benefits are significant and support informed and assertive decisions. In this way, Quality Intelligence tends to establish itself as a reference for managers who see customer service as a pillar of differentiation and added value, far beyond the traditional operational indicators that once guided the sector's strategies