”Because AI is 100% right and safe”: User Attitudes and Sources of AI Authority in India
Open accessAbstract
Most prior work on human-AI interaction is set in communities that indicate skepticism towards AI, but we know less about contexts where AI is viewed as aspirational. We investigated the perceptions around AI systems by drawing upon 32 interviews and 459 survey respondents in India. Not only do Indian users accept AI decisions (79.2% respondents indicate acceptance), we find a case of AI authority—AI has a legitimized power to influence human actions, without requiring adequate evidence about the capabilities of the system. AI authority manifested into four user attitudes of vulnerability: faith, forgiveness, self-blame, and gratitude, pointing to higher tolerance for system misfires, and introducing potential for irreversible individual and societal harm. We urgently call for calibrating AI authority, reconsidering success metrics and responsible AI approaches and present methodological suggestions for research and deployments in India.
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The wider literature suggests that people may regard AI systems as authoritative sources.
“The wider literature similarly suggests that people may regard AI systems as authoritative sources [2, 60, 76].”
From Co-Writing with Opinionated Language Models Affects Users’ Views (2023) · cites this paper as [60]
The stakes involved in a decision are subjective and personal.
“Since the stakes involved in a decision are subjective and personal [49], we captured participants' task stakes perceptions as a manipulation check.”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [49]
Previous work has shown that cultural and geographical differences play a key role in perceptions towards algorithmic systems.
“Previous work has shown that cultural and geographical differences play a key role in perceptions towards algorithmic systems [10,49,97].”
From Disentangling Fairness Perceptions in Algorithmic Decision-Making: the Effects of Explanations, Human Oversight, and Contestability (2023) · cites this paper as [49]
This study confirms Kapania et al.'s findings in the context of next-phrase suggestion systems.
“Our study confirms these findings in the context of next-phrase suggestion systems.”
From Interacting with Next-Phrase Suggestions: How Suggestion Systems Aid and Influence the Cognitive Processes of Writing (2023) · cites this paper as [40]
Kapania et al. noted a similar attitude of giving AI authority based on analogising its functionality with other technologies.
“A similar attitude of giving AI authority based on analogising its underlying functionality with other technologies was noted by Kapania et al. [40].”
From Interacting with Next-Phrase Suggestions: How Suggestion Systems Aid and Influence the Cognitive Processes of Writing (2023) · cites this paper as [40]
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References 83 · Cited by 90
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- Authors
- Kapania, S., Siy, O., Clapper, G., SP, A. M., & Sambasivan, N.
- Affiliations
- Google (United States) (US)
- Google, India
- Venue
- CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2022-04-27
- Pages
- 1-18
- OpenAlex
- W4224999418
- Cited by
- 90
- References
- 83
- Citations by year
- 2026: 20 · 2025: 28 · 2024: 24 · 2023: 16 · 2022: 2
- FWCI
- 17.39
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- user attitudes toward AI, AI acceptance, human-AI interaction, trust in AI, vulnerability, forgiveness, self-blame, gratitude, responsible AI, India
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