Privacy, Life-Logging & Personal Data
What people will trade for convenience, who refuses, and why the refusers are not simply behind the curve.
Privacy research often frames non-adoption as a deficit to be overcome. The data does not support that reading.
Across studies of life-logging and fitness tracking, users and non-users separate into coherent profiles rather than along a single willing-to-reluctant axis: people who prioritise data protection, people who maximise benefit, and people who are simply curious about the numbers. These groups want different things and are moved by different arguments, and a design that treats the second group as the default quietly excludes the first. Perceived sensitivity also varies sharply by data type in ways that do not track how sensitive the data actually is.
The same questions have followed me into the factory, where they get sharper. Data collected in a smart factory is increasingly about employees, gathered in a setting where consent is complicated by the employment relationship itself. Asking workers what they will accept, and under which conditions, produces answers with real structure — monitoring for safety and monitoring for performance are not the same proposition, and treating them as one is how trust is lost.
The practical output of this thread is guidance for building such systems responsibly, including work on integrating social and ethical requirements into development rather than reviewing them afterwards.
