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White Paper: Addressing GDPR Concerns in the Use of Neural Networks for Personalization

Executive Summary

This white paper is intended to address the concerns of an EU regulator on possible GDPR principal violations in our social networking company’s business model regarding the use of neural networks for personalization (Spillane, 2022). The document provides a comprehensive overview of neural networks, their relevance to personalization, an analysis of applicable GDPR principles, legal considerations, and detailed recommendations on transforming our practices by the GDPR requirements.

Understanding Neural Networks

1a. Basic Explanation of Neural Networks

Neural networks are computational models based on the human brain’s neural architecture. Made of input, hidden, and output layers, neurons within the nodes process information. In training, neural networks adjust weights between nodes, which emulate how humans learn to classify objects. In other words, they mimic human learning to make predictions or classifications (Ved, 2023). In our business model, neural networks are critical as we have user data, including mouse clicks, site navigation, links followed, and time spent on pages. These data are passed to several neural networks, which develop models that personalize our website’s user experience.

Neural Networks in Personalization

2a. Use of Neural Networks for Individualization.

Using neural networks, our company analyzes user data and personalizes the experiences by predicting preferences through posts, friend recommendations, groups, articles, etc. (Spillane, 2022). This personalization helps enhance user engagement, which results in greater ad revenues through a highly customized platform.

2b. Ethical Concerns in Personalization

With all the benefits, however, the “black box” nature of neural networks creates ethical challenges, particularly due to hidden biases. Users may not understand how recommendations are created, which could result in discrimination (Dorschel, 2020). The solution to this problem is finding a middle ground between transparency and personalization for ethical AI principles.

GDPR and Personalization

3a. Summary of GDPR Principles Affecting Personalization

Transparency, purpose limitation, data minimization, accuracy, storage limitation, confidentiality, and accountability are the GDPR principles that apply to personalization. Adhering to these principles is essential in preserving user privacy and avoiding punitive actions (ICO, nd ). The business model discussed in the scenario may violate some of these principles. The following analysis evaluates each principle within the context of our operations:

  • Transparency: The company must increase transparency in describing how user data enables personalization.
  • Purpose Limitation: There should be a collection of data for prespecified purposes. It is also important to ensure that the collected data fits their intended purposes and shall not be reused by users without their consent.
  • Data Minimization: As a result, finding some middle ground between the accuracy of personalization and reducing the data collection process is essential. We should gather only the information necessary for such purposes.
  • Accuracy: Wep the users’ trust by updating what we use in our algorithms.
  • Storage Limitation: Data should be kept as long as it is relevant to the stated purposes. We must put reasonable storage limits in place to follow this principle.
  • Confidentiality: Measures to protect user data, confidentiality, and privacy should be strengthened. This can also involve better encryption, access controls, and regular security audits.
  • Accountability: We must put effective accountability structures into practice within our organization. This includes good internal governance, records, and data protection officers.

Legal Concerns and Company Practices

4a. Specific Legal Concerns

Using neural networks as classifiers in our company’s personalization algorithms creates a particular legal problem under GDPR. The transparency issues come about because these networks are “black boxes,” which goes against the right to know how data is processed. Purpose limitation might pose a few challenges since the data collected for training can unintentionally be repurposed, contradicting GDPR’s principle of collecting information only for pre-determined purposes (Intersoft Consulting, 2022). The hazard of the balancing act known as data minimization is that its very nature threatens the GDPR’s injunction to collect only relevant information. The uncertainties of the predictions could result in infringement of user rights, whereas extended data retention may violate the principle of storage limitation. The legal difficulties are further complicated by security risks and responsibility issues, which emphasize the need to make corresponding strategic changes to comply with GDPR standards.

4b. Feasibility of Not Collecting Data

The scenario outlines the potential GDPR violations; nevertheless, ceasing data collection is a drastic measure that brings into question our business model foundation (Capgemini, 2023). The balance between compliance and the accuracy of personalization is crucial.

Adapting Practices for GDPR Compliance

5a. Current Trends in AI and Privacy Preservation

AI trends also show the importance of a growing interest in preserving user privacy using advanced techniques. An important technique is federated learning, which allows the training of models among decentralized devices without centralizing raw data (Ved, 2023). The decentralized learning paradigm reduces the risks of data aggregation, thereby improving user privacy. Moreover, the other important trend is differential privacy, where noise is intentionally added to data points during analysis, and it protects specific information about a single contributor. To achieve an effective balance between delivering quality personalized experiences and ensuring user privacy, the AI community emphasizes these approaches to protect user data as part of a commitment to rigorous privacy preservation.

5b. Proposed Changes for GDPR Compliance

To comply with GDPR, the company should:

  • Enhance Transparency: It should be clearly stated to the users how their data is used for personalization through understandable and concise explanations.
  • Strict Purpose Limitation: Ensure that data is collected for specific purposes that are clearly defined and not used inappropriately without permission from users.
  • Data Minimization Strategies: Personalization can be derived from the collection technologies that only collect essential data while balancing effectiveness and minimizing acquisition (Dorschel, 2020).
  • Accuracy Checks: Ensure the personalization algorithms have up-to-date and verified user data.
  • Storage Limitation: Outline the fair and reasonable retention time for data based on the storage limitations principle.
  • Enhanced Confidentiality Measures: To keep user information as classified as possible, revise encryption and access control measures regularly, and perform continuous security audits.
  • Establish Accountability Mechanisms: Set up internal governance, contracts, and documentation and recruit data protection officers to ensure responsibility.


This detailed analysis reveals a broad picture of the tortuous relationship between neural networks, personalization, and GDPR principles. The changes would provide GDPR compliance and establish trust with users that our company respects people’s privacy and uses AI ethically.


Capgemini. (2023, September 4). Expert perspectives.

Dorschel, A. (2020, May 8). Data Privacy in Machine Learning: A technical deep-dive. Medium.

ICO. (n.d.). UK GDPR guidance and resources.

Intersoft Consulting. (2022, September 27). Official Legal Text. General Data Protection Regulation (GDPR).

Spillane, J. (2022, December 8). How GDPR can undermine personalization and user experience – business … Business Community.

Ved, A. (2023, April 19). How to develop artificial intelligence that is GDPR-friendly. TechGDPR.


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