{"id":142125,"date":"2025-11-15T13:10:33","date_gmt":"2025-11-15T12:10:33","guid":{"rendered":"https:\/\/geab.eu\/ia-predictive-conditions-dune-bonne-utilisation-et-limites-realistes\/"},"modified":"2025-11-16T16:36:40","modified_gmt":"2025-11-16T15:36:40","slug":"predictive-ai-ensuring-proper-use-while-understanding-its-limits","status":"publish","type":"post","link":"https:\/\/geab.eu\/en\/predictive-ai-ensuring-proper-use-while-understanding-its-limits\/","title":{"rendered":"Predictive AI: Ensuring proper use while understanding its limits"},"content":{"rendered":"

TECHNICAL RECOMMENDATIONS<\/strong><\/span><\/p>\n

For AI to become a tool for stabilisation rather than a catalyst for crisis, certain conditions, some of which are difficult to meet, must be fulfilled.<\/p>\n

Diversification, redundancy and model mixing<\/strong><\/p>\n

Some economists, such as Tyler Cowen (Mercatus Centre), believe that the solution is not to limit AI but to diversify its use. According to him, if more players develop their own models, the diversity of approaches will reduce the risk of synchronised spirals. But to achieve this, competition in the development of financial AI must be supported, which remains a pipe dream for the time being. There needs to be methodological diversity (different algorithms, parallel classical statistical approaches); adversarial<\/em> models need to be introduced (robustness tests, extreme scenarios: “What breaks if this factor changes suddenly?”); there must be manual fallback<\/em> mechanisms (in case of doubt, a return to human or semi-automatic decisions); and there must be multiple redundant architectures (if one model fails, another takes over, as in engineering).\t\t

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