Trust as a Vector
Reported by Dev.to — AI: Trust Isn't a Scalar: Typed Provenance for Agent Chains. Analysis and context written by TickrWire.
The author proposes a new model for trust, replacing a boolean trust tag with a vector-based approach, and introduces the concept of typed provenance for agent chains.
- Trust is not a scalar value, but rather a vector over multiple axes
- Typed provenance is proposed as a way to propagate trust information through agent chains
- The consumer of the information applies the policy, rather than relying on a boolean trust tag
In a previous post, the author presented a boolean trust tag, which was critiqued by a commenter. The author now presents an improved model, where trust is represented as a vector over multiple axes. This approach allows for more nuanced and context-dependent trust assessments. The concept of provenance is also introduced, which refers to the origin and history of data or information. The author argues that provenance is what propagates through agent chains, and that the consumer of the information applies the policy. This post is part of a series that is being co-written with the comment section.
This new model for trust can help developers design more robust and secure systems
The concept of typed provenance can help businesses better understand and manage the flow of information
This research can inform investment decisions in the field of artificial intelligence and data security
This post provides a unique perspective on the concept of trust and its representation in complex systems
The idea of trust as a vector rather than a scalar can have far-reaching implications for how we think about security and information exchange
- Provenance
- The origin and history of data or information
- Agent chains
- A series of agents or entities that handle and propagate information
AI bias estimate: The author's tone is informative and neutral, with a focus on presenting a new idea (Automated estimate, not a definitive judgement.)
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