Socioduality: A Relational Process Framework for Human-AI Interaction
Quick summary
arXiv:2608.11322v2 Announce Type: replace-cross Abstract: Human-AI research often evaluates individual capabilities, joint performance, or final outputs, but these approaches can lose the interaction process that produced the result. This article introduces socioduality: a sequential, reciprocal, and history-carrying process in which one party's response becomes part of the observable conditions shaping the other party's next contribution, judgement, decision, or action. For human-AI dyads, the framework identifies moves, candidate episodes, confirmed episodes, and maximal pathways. A minimum
Key takeaways
- arXiv:2608.11322v2 Announce Type: replace-cross Abstract: Human-AI research often evaluates individual capabilities, joint performance, or final outputs, but these approaches can lose the interaction process that produced the result.
- This article introduces socioduality: a sequential, reciprocal, and history-carrying process in which one party's response becomes part of the observable conditions shaping the other party's next contribution, judgement, decision, or action.
- For human-AI dyads, the framework identifies moves, candidate episodes, confirmed episodes, and maximal pathways.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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