2.4.2 Fairness
Fairness is a broadly shared aspiration for governing algorithmic technologies. Definitions of ‘fairness’ differ, and the notion of ‘algorithmic fairness’ itself is an increasingly expanding field of research.337 There are reportedly between 15 and 25 plausible definitions of ‘fairness’ of relevance to algorithmic technologies, each with different and often mutually exclusive emphases.338 Depending on the context, some definitions are constructed in highly technical ways, centred on data science expertise, while others draw on the common-usage (and equally broad) aims of equitable and impartial treatment.339 There is a risk that vague references to ‘fairness’ may hide important political decisions about how fairness is understood, including – importantly – who defines it.
Such choices ought to be transparent when used in the design of mental health related technologies, partly to clarify objectives, but also to highlight who may gain (or lose) political and decision-making power depending on the approach to fairness that is adopted. If addressing fairness becomes highly technical, for example, requiring the expertise of computer scientists, mathematicians, and so on, there is seemingly less scope for those most affected to determine the parameters of what is considered fair and unfair