GLANCE: Global Actions in a Nutshell for Counterfactual Explainability

GLANCE addresses a specific gap in Explainable AI (XAI): moving from individual counterfactuals (e.g., 'If you increased your income by $5k, you would get the loan') to global, policy-oriented actions for subgroups. While theoretically valuable for regulatory compliance (GDPR/AI Act), the project currently lacks a moat. With 0 stars and 12 forks, it is clearly an academic artifact tied to its arXiv paper (2405.18921) rather than a production-ready tool. It competes with established XAI libraries like Microsoft's DiCE or Seldon's Alibi, which are more likely to integrate 'global' strategies as they mature. Frontier labs are unlikely to prioritize this niche tabular-data problem, but the project faces high displacement risk from other researchers or enterprise ML platforms (SageMaker, Vertex AI) that could implement similar subgroup recourse logic as part of their fairness/bias toolkits. The 12 forks indicate some academic peer interest for replication, but without an active maintenance community or a pip-installable package, it remains a reference implementation.