Features of Human-Centred Algorithm Design

Marianne Cherrington, David Airehrour, Joan Lu, Qiang Xu, David Cameron-Brown, Ihaka Dunn

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2020 30th International Telecommunication Networks and Applications Conference, ITNAC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728188270, 9781728188263
ISBN (Print)9781728188287
Publication statusPublished - 25 Nov 2020
Event30th International Telecommunication Networks and Applications Conference - Held virtually due to COVID19, Melbourne, Australia
Duration: 25 Nov 202027 Nov 2020
Conference number: 30

Publication series

NameInternational Telecommunication Networks and Applications Conference
ISSN (Print)2474-1531
ISSN (Electronic)2474-154X


Conference30th International Telecommunication Networks and Applications Conference
Abbreviated titleITNAC 2020
Internet address


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