Are non-diverse AI research and development teams risking bias in innovated algorithms and artefacts?

Shah, H. (Speaker)

Activity: Talk or presentationInvited talk

Description

Nascent AI technologies have exposed some bias issues stemming from possible limited range in data sets used to train algorithms and teach machines. The news conveys the results of inadequately trained algorithms, for example: “Black man is stunned after passport photo checker mistakes his lips for an open mouth as Home Office facial recognition system is accused of bias” (MSN, 2019).

MSN: https://www.msn.com/en-gb/money/technology/black-man-is-stunned-after-passport-photo-checker-mistakes-his-lips-for-an-open-mouth-as-home-office-facial-recognition-system-is-accused-of-bias/ar-AAHx5on?li=AABMOzg
Period5 Feb 2020
Held atAI4EU, Belgium
Degree of RecognitionInternational

ASJC Scopus subject areas

  • Computer Science(all)

Keywords

  • AI
  • AI ethics
  • Bias
  • Algorithms
  • diversity
  • data science
  • Machine Learning
  • facial recognition