The Nature of Inference and Personal Data

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Under the UK GDPR, "personal data" includes not only directly provided information but also inferred or derived data if it relates to an identified or identifiable natural person. This means that if a business processes mobile data (such as call patterns, data usage, device type, location data, or payment methods) and uses algorithms to deduce characteristics like income level, that inferred income level can be considered personal data. The Information Commissioner's Office (ICO) has clarified that whether inferred data counts as personal data depends on whether there's an intention to make an inference linked to an individual or to treat someone differently based on that inference. If the inference relates to an individual, then it falls under the scope of the UK GDPR.

2. Lawful Basis and Transparency Challenges:

For any processing of personal data, including slovenia mobile database inferred income levels, a lawful basis is required under UK GDPR. Common lawful bases include:

Consent: Explicit, informed consent from the individual would be the strongest basis, but obtaining this for complex inferences like income level would be extremely challenging. Individuals would need to fully understand what data is being used, how inferences are made, and the specific purpose for which their inferred income level will be used.
Legitimate Interests: A business might argue a legitimate interest in inferring income for certain purposes (e.g., targeted advertising, credit scoring). However, this requires a rigorous Legitimate Interests Assessment (LIA) to ensure that the business's interests are balanced against the individual's rights and freedoms. Inferring income is highly intrusive, and it's difficult to justify that the legitimate interests of the business outweigh the privacy rights of the individual, especially if the inferences are not accurate or are used for discriminatory purposes.
Contractual Necessity or Legal Obligation: These bases are generally not applicable for inferring income from mobile data unless it's explicitly required for a service or by law (e.g., for creditworthiness assessments by financial institutions, but even then, strict rules apply).
Furthermore, the principle of transparency is paramount. Individuals have a right to know that their data is being used to infer their income level, how this inference is made, and what the consequences are. Providing this level of detail in an understandable way for complex algorithmic inferences is a significant challenge.

3. Risk of Discrimination and "Special Category Data":

Inferring income levels from mobile data carries a high risk of discrimination. If these inferences lead to individuals being denied access to services, offered different prices, or subjected to unfair treatment based on a potentially inaccurate or biased income assessment, it raises serious ethical and legal concerns.