{"id":2042,"date":"2026-03-16T12:08:14","date_gmt":"2026-03-16T15:08:14","guid":{"rendered":"https:\/\/peckadv.com.br\/?p=2042"},"modified":"2026-03-16T12:22:18","modified_gmt":"2026-03-16T15:22:18","slug":"algoritmos-opacos-e-personalizacao-invasiva-o-novo-desafio-do-consumo-digital","status":"publish","type":"post","link":"https:\/\/peckadv.com.br\/en\/algoritmos-opacos-e-personalizacao-invasiva-o-novo-desafio-do-consumo-digital\/","title":{"rendered":"Opaque Algorithms and Invasive Customization: The New Challenge of Digital Consumption"},"content":{"rendered":"

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INTRODUCTION
\n<\/b>The popularization and improvement of AI models have offered significant gains for digital consumers: greater convenience, increased speed, and highly personalized experiences. However, this same movement has made room for risks that are becoming increasingly relevant in 2026\u2014risks that are often not even perceived by the average consumer. In this brief text, we analyze the greatest challenge identified to date: opaque algorithms and invasive personalization, their impacts on consumption, and the legal developments that companies must observe.<\/p>\n

DEVELOPMENT<\/b><\/p>\n

What is changing in digital behavior?<\/b>
\nRecent market trend reports show that consumption is becoming increasingly predictive, driven by generative AI and intelligent assistants [1]. Systems are now understanding preferences and anticipating decisions even before the consumer expresses them. While this advancement brings efficiency, it also increases risks when automated decisions happen invisibly, involuntarily, and sometimes with bias. Social platforms and e-commerce environments, increasingly integrated into the act of buying, subscribing, and signing up, make this effect undeniable.<\/p>\n

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  1. Algorithmic discrimination<\/b> Algorithms learn from historical data\u2014and when this data carries biases, the result can be unequal treatment among consumers. This includes non-transparent differences in offers, pricing, or access to services. It is a silent risk, yet one amplified by hyper-personalization.<\/li>\n
  2. Behavioral manipulation<\/b> Digital behavior, emotions, and browsing patterns are now influencing recommendations. Studies indicate that consumption is merging with social experience, increasing exposure to stimuli designed to maximize engagement, often without the user’s awareness. The result can be a loss of autonomy, unclear persuasive stimuli, and profound information asymmetries.<\/li>\n
  3. Privacy violations and excessive collection<\/b> Digital signals\u2014many of them inferred, not directly provided\u2014feed increasingly sophisticated algorithms. The risk: unnecessary collection, the combination of sensitive data, and inferences that go beyond informed consent and sometimes lack a legal basis or adequate justification for processing.<\/li>\n<\/ol>\n

    The Legal Approach: What must companies observe?
    \n<\/b>With the intensification of AI use, the alignment between the General Data Protection Law (LGPD) and Consumer Law gains even more prominence.<\/p>\n

    Transparency and explainability
    \n<\/b>The LGPD requires clear information regarding the criteria used in automated decisions and prohibits discrimination resulting from the use of personal data. From a consumerist perspective, this connects to the duty of information and the prohibition of abusive practices, both of which are provided for in the Consumer Defense Code\u2014an innovative piece of legislation since its enactment that remains fully applicable.<\/p>\n

    Algorithmic governance<\/b>
    \nCompanies that use AI in recommendation, segmentation, or pricing processes must adopt mechanisms for:<\/p>\n