Features of Human-Centred Algorithm Design

Algorithms are pervasive, unseen influencers of decisions. Algorithmic features can fluctuate widely, depending on use, user or criteria applied. This paper considers the nascent field of human-centred algorithm design (HCAD), intersecting human-centred design and algorithmic systems. Human-centred, more-than-metric feature selection approaches, create fairer and deeper meaning. More value is created. The unique impact of this paper is to integrate feature selection within a technology HCAD strategy, for a novel, innovative HCAD approach to machine learning. This flexible and evaluative approach can support data advances with human-social nuance, designed for purpose with knowledge for data-driven decisions. The design of machine learning algorithms to the uses in which they will be employed is user-centric. This is important within environments utilising automated, semi-automated or high-performance analytics.