Reflecting on Algorithmic Bias with Design Fiction: the MiniCoDe Workshops

T. Turchi, A. Malizia, S. Borsci

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
9 Downloads (Pure)

Abstract

In an increasingly complex everyday life, algorithms – often learnt from data, i.e. machine learning (ML) – are used to make or assist operational decisions. However, developers and designers usually are not entirely aware of how to reflect on social justice while designing ML algorithms and applications. Algorithmic social justice – i.e., designing algorithms including fairness, transparency, and accountability – aims at helping expose, counterbalance, and remedy bias and exclusion in future ML-based decision-making applications. How might we entice people to engage in more reflective practices that examine the ethical consequences of ML algorithmic bias in society? We developed and tested a Design Fiction-driven methodology to enable multi-disciplinary teams to perform intense, workshop-like gatherings to let emerge potential ethical issues and mitigate bias through a series of guided steps. With this contribution, we present an original and innovative use of Design Fiction as a method to reduce algorithmic bias in co-design activities.

Original languageEnglish
Number of pages13
JournalIEEE intelligent systems
DOIs
Publication statusE-pub ahead of print/First online - 11 Jan 2024

Keywords

  • Conferences
  • Decision making
  • Ethics
  • Intelligent systems
  • Machine learning
  • Machine learning algorithms
  • Standards

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