Understanding AI (3/4): Ethics, Security, and Bias

Bias appears when a model produces unfair or distorted results, often due to biased training data or imbalanced representation. Example: recruiting tools disadvantaging certain profiles because of historical data.

Ensuring systems are transparent, fair, accountable, and aligned with human values—not just performant.

Adding statistical “noise” to data so individual information can’t be identified, while still learning global patterns.

Encryption that allows computation on encrypted data. Example: processing patient data without direct access to the raw data.

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Also a privacy and ethics lever: models train on devices and share updates rather than centralizing sensitive data.

Tiny perturbations (sometimes invisible) can trick models into wrong predictions. Security must anticipate these manipulations.

Malicious data injected into training can create “backdoors”—like a virus for algorithms.

A robust AI keeps stable performance even under noise, missing data, or edge cases. The more robust, the more trustworthy

Technosolutionism is the belief that technology can solve all problems. With powerful AI, it’s important to keep an informed, balanced, and responsible approach.