Trustworthy Artificial Intelligence
2025
Trustworthy AI: Responses to Commentators
publication
Evidence: moderate
Author Information
Author(s): Christoph Kelp, Mona Simion
Primary Institution: University of Glasgow
Hypothesis
How can AI be trustworthy and what does it take for an AI to be trustworthy?
Conclusion
The study argues that AIs can be trustworthy by fulfilling their functions and obligations.
Supporting Evidence
- AIs have functions that imply obligations, which can make them trustworthy.
- The paper discusses the distinction between trust and reliance in the context of AI.
- Trustworthiness can be understood in terms of fulfilling obligations.
- The authors argue against the idea that current AIs cannot be trustworthy.
Takeaway
This paper talks about how we can trust AI if it does what it's supposed to do, like a heart pumping blood.
Methodology
The authors respond to comments on their previous work regarding AI trustworthiness.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website