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Ethics and AI Profiling in Mental Health: The Imperative of Data Anonymisation

By Gian Zambrini, Director of Information Ethics and Technology at Mind Data.

Navigating the Moral Quagmire

Imagine using an app as a confidant, a repository for your most guarded feelings. Trust becomes paramount. Within the realm of mental health AI, the issue of ethical data management is as critical as the engineering algorithms behind it. This is why we adopt best practices to de-identify personal data where feasible.

Why Anonymisation Matters

The digital mask we create for your data serves a dual purpose: it protects your identity while enabling meaningful analytics. Anonymisation techniques strip data of personal identifiers, transforming the information provided into a mere data point among many which cannot be linked back to a specific individual.

The Layers: Anonymisation, Pseudonymisation, and Aggregation

  • Anonymisation: Removes all personally identifiable information where identification of data can not occur without additional information.

  • Pseudonymisation: Replaces private identifiers with fake identifiers, enabling data processing without revealing the actual user.

  • Aggregation: Combines data in such a way that individual particulars cannot be deciphered.

Employer-Employee Data Dynamics

For individuals and organisations, the confidentiality question looms large. Employees, students or athletes may wonder, "Is my employer or Business School Dean privy to my emotional state?" The answer is an unequivocal no. Employers receive only aggregated data that does not identify individuals. Even in smaller teams/cohorts, where deduction could potentially occur, our robust anonymisation and pseudonymisation methods act as bulwarks against such deductions.

Proactive Measures in AI Profiling

Our AI analysis is designed to be ethically conscious, and programmed to adhere to strict guidelines around data manipulation and interpretation. We follow the OECD and the UK’s ICO guidelines for responsible use of AI.

Bridging the Gap between Ethics and Efficacy

In a large sea of data, each individual is like a drop of water—indistinguishable when part of a larger entity but irreplaceable in their uniqueness. The power of our analytics comes from this collective entity, not from exposing individual vulnerabilities.


When you use Mind Data, you're not just an anonymous cog in a data machine; you’re a valued individual whose privacy is our utmost concern. We've instituted a multi-layered approach to ensure your data remains both informative for collective mental health trends and uncompromisingly private. As the adage goes, trust is hard to earn but easy to lose. At Mind Data, we strive every day to earn and maintain that trust.

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