Counting Worlds Branching Time Semantics for post-hoc Bias Mitigation in generative AI
Generative AI systems are known to amplify biases present in their training data. While several inference-time mitigation strategies have been proposed, they remain largely empirical and lack formal guarantees. In this paper we introduce CTLF, a branching-time logic designed to reason about bias in series of generative AI outputs. CTLF adopts a counting worlds semantics where each world represents a possible output at a given step in the generation process and introduces modal operators that allow us to verify whether the current output series respects an intended probability distribution over a protected attribute, to predict the likelihood of remaining within acceptable bounds as new ou...
| Authors | Alessandro G. Buda, Giuseppe Primiero, Leonardo Ceragioli et al. |
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| Primary category | cs.LO |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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