2.7 Transparency and explainability
Transparency is defined in various ways but essentially refers to the importance of technological systems being designed and implemented so that oversight is possible. Transparency is therefore closely linked to accountability. It could extend to matters such as the data that are generated in a particular setting, the system that processes the data, and (where relevant) the business model that makes use of them.390 As an example, Til Wykes and Stephen Schueller have proposed a transparency governance method for apps concerning health; they raised concerns about the overselling of health apps and so-called health apps that may provide little benefit and even harm.391 They presented the ‘Transparency for Trust (T4T) Principles of Responsible Health App’ in the form of a list of questions that can be asked to reveal key matters concerning privacy, security, feasibility, and so on.392 Wykes and Schueller promote the use of the T4T principles by app stores and for presentation ‘in a simple form so that all consumers can understand them.393
The related concept of explainability refers to ‘the translation of technical concepts and decision outputs into intelligible, comprehensible formats suitable for evaluation’.394 Explainability seems particularly important for software that analyses large datasets algorithmically,395 which, again, are a minority of digital initiatives in the mental health context. Explainability is particularly crucial for systems with potential to cause harm or significantly impact individuals, such as impacting health, access to resources and quality of life. A governance example on this point is the AI in the UK: Ready, Willing and Able? report, which notes that if an AI system has a ‘substantial impact on an individual’s life’ and cannot provide ‘full and satisfactory explanation’ for decisions made, then it should not be deployed396—a view which few people, if any, would challenge in the mental health and disability context. Explainability is often linked to promoting nondiscrimination given that the more comprehensible a system is, the more likely discrimination, bias or error can be identified, prevented and rectified.397