Towards Generalised and Incremental Bias Mitigation in Personality Computing

Jian Jiang, Viswonathan Manoranjan, Hanan Salam, Oya Celiktutan

Research output: Contribution to journalArticlepeer-review


Building systems for predicting human socio-emotional states has promising applications; however, if trained on biased data, such systems could inadvertently yield biased decisions. Bias mitigation remains an open problem, which tackles the correction of a model's disparate performance over different groups defined by particular sensitive attributes (e.g., gender, age, and race). In this work, we design a novel fairness loss function named Multi-Group Parity (MGP) to provide a generalised approach for bias mitigation in personality computing. In contrast to existing works in the literature, MGP is generalised as it features four ‘multiple’ properties (4Mul): multiple tasks, multiple modalities, multiple sensitive attributes, and multi-valued attributes. Moreover, we explore how to incrementally mitigate the biases when more sensitive attributes are taken into consideration sequentially. Towards this problem, we introduce a novel algorithm that utilises an incremental learning framework to mitigate bias against one attribute data at a time without compromising past fairness. Extensive experiments on two large-scale multi-modal personality recognition datasets validate the effectiveness of our approach in achieving superior bias mitigation under the proposed four properties and incremental debiasing settings.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Affective Computing
StateAccepted/In press - 2024


  • Adaptation models
  • Affective computing
  • Bias mitigation
  • Computational modeling
  • Data models
  • incremental learning
  • model fairness
  • multimodal learning
  • multitask learning
  • personality computing
  • Predictive models
  • Task analysis
  • Training

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction


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