Clearing the way for participatory data stewardship in artificial intelligence development: a mixed methods approach

Sage Kelly*, Sherrie Anne Kaye, Katherine M. White, Oscar Oviedo-Trespalacios

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Participatory data stewardship (PDS) empowers individuals to shape and govern their data via responsible collection and use. As artificial intelligence (AI) requires massive amounts of data, research must assess what factors predict consumers’ willingness to provide their data to AI. This mixed-methods study applied the extended Technology Acceptance Model (TAM) with additional predictors of trust and subjective norms. Participants’ data donation profile was also measured to assess the influence of individuals’ social duty, understanding of the purpose and guilt. Participants (N = 322) completed an experimental survey. Individuals were willing to provide data to AI via PDS when they believed it was their social duty, understood the purpose and trusted AI. However, the TAM may not be a complete model for assessing user willingness. This study establishes that individuals value the importance of trusting and comprehending the broader societal impact of AI when providing their data to AI. Practitioner summary: To build responsible and representative AI, individuals are needed to participate in data stewardship. The factors driving willingness to participate in such methods were studied via an online survey. Trust, social duty and understanding the purpose significantly predicted willingness to provide data to AI via participatory data stewardship.

Original languageEnglish
Pages (from-to)1782-1799
Number of pages18
Issue number11
Publication statusPublished - 2023


  • AI
  • human factors
  • participatory data stewardship
  • psychosocial models
  • user acceptance


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