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to be Forgotten, and Inclusive Insurance", "url": "https://freakonometrics.hypotheses.org/88069", "published_at": "2026-02-04T07:41:56+00:00" }, { "id": "01a0c51c-a713-7119-999b-c57f85c80edd", "title": "Correlation, Causation, Circumstance, Context", "url": "https://freakonometrics.hypotheses.org/87986", "published_at": "2026-02-04T03:25:19+00:00" }, { "id": "01a0c51c-a713-7119-999b-c57f865258be", "title": "Corrélation et causalité, circonstance et contexte", "url": "https://freakonometrics.hypotheses.org/87943", "published_at": "2026-02-04T02:57:34+00:00" }, { "id": "01a0c51c-a713-7119-999b-c57f86d35fa2", "title": "Insurance, biases, discrimination and fairness", "url": "https://freakonometrics.hypotheses.org/87909", "published_at": "2026-02-02T09:28:50+00:00" }, { "id": "01a0c51c-a713-7119-999b-c57f87539ce0", "title": "Balance and Calibration of Probabilistic Scores: From GLM to Machine Learning", "url": "https://freakonometrics.hypotheses.org/87493", "published_at": 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