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Paper Type

Complete

Abstract

The adoption of AI-enabled systems and services depends on user trust. To achieve this, the design and commercialization of AI must prioritize transparency, fairness, and accuracy to create a positive user impression. This research sheds light on how developer-specific characteristics can impact users' perceptions of AI systems, particularly when they have no prior experience with them. Using a controlled experimental design, this research demonstrates that users expect AI provided by non-profit organizations to be more transparent and fairer but not necessarily more accurate than AI provided by for-profit organizations. The results are explained by perceptions of organizational greed and impressions of warmth. Implications are discussed for algorithm aversion and the managerial practices of AI providers.

Paper Number

1064

Author Connect URL

https://authorconnect.aisnet.org/conferences/AMCIS2024/papers/1064

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Aug 16th, 12:00 AM

Corrupted by Greed: Developer Profit Orientation as a Signal of Fairness and Transparency in AI Systems

The adoption of AI-enabled systems and services depends on user trust. To achieve this, the design and commercialization of AI must prioritize transparency, fairness, and accuracy to create a positive user impression. This research sheds light on how developer-specific characteristics can impact users' perceptions of AI systems, particularly when they have no prior experience with them. Using a controlled experimental design, this research demonstrates that users expect AI provided by non-profit organizations to be more transparent and fairer but not necessarily more accurate than AI provided by for-profit organizations. The results are explained by perceptions of organizational greed and impressions of warmth. Implications are discussed for algorithm aversion and the managerial practices of AI providers.

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