Implementing Causal Perception: Competing SCMs and Situated Fairness
Quick summary
arXiv:2608.03917v1 Announce Type: new Abstract: Causal perception occurs when agents with competing Structural Causal Models (SCMs) of the same system infer different probability distributions, including the hypothetical distributions implied by each agent's SCM under the same set of interventions. It shapes how agents reason about the system and how they perceive its fairness. Causal perception is a promising probabilistic framework, but it has remained purely theoretical. This work provides the first implementation of the causal perception framework of \'Alvarez and Ruggieri (2025). We opera
Key takeaways
- arXiv:2608.03917v1 Announce Type: new Abstract: Causal perception occurs when agents with competing Structural Causal Models (SCMs) of the same system infer different probability distributions, including the hypothetical distributions implied by each agent's SCM under the same set of interventions.
- It shapes how agents reason about the system and how they perceive its fairness.
- Causal perception is a promising probabilistic framework, but it has remained purely theoretical.
Why it matters
“Implementing Causal Perception: Competing SCMs and Situated Fairness” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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