Investigating Bias in Facial Analysis Systems: A Systematic Review

Ashraf Khalil, Soha Glal Ahmed, Asad Masood Khattak, Nabeel Al-Qirim

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


Recent studies have demonstrated that most commercial facial analysis systems are biased against certain categories of race, ethnicity, culture, age and gender. The bias can be traced in some cases to the algorithms used and in other cases to insufficient training of algorithms, while in still other cases bias can be traced to insufficient databases. To date, no comprehensive literature review exists which systematically investigates bias and discrimination in the currently available facial analysis software. To address the gap, this study conducts a systematic literature review (SLR) in which the context of facial analysis system bias is investigated in detail. The review, involving 24 studies, additionally aims to identify (a) facial analysis databases that were created to alleviate bias, (b) the full range of bias in facial analysis software and (c) algorithms and techniques implemented to mitigate bias in facial analysis.

Original languageEnglish
Article number9130131
Pages (from-to)130751-130761
Number of pages11
JournalIEEE Access
Publication statusPublished - 2020


  • Algorithmic discrimination
  • bias
  • classification bias
  • facial analysis
  • unfairness

ASJC Scopus subject areas

  • General Engineering
  • General Computer Science
  • General Materials Science


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