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Black-box models make decisions that are difficult for humans to understand or explain. We only see their inputs and outputs, not the reasoning behind them. For example, an algorithm that screens job applicants might reject qualified candidates without clear reasons. This lack of transparency can weaken trust and accountability. Hidden biases may be learned from past data and quietly amplified, leading to discrimination that often goes unnoticed until it causes harm. Since employment decisions are highly regulated and must be fair and auditable, black-box systems complicate compliance and investigations. Therefore, transparency and human oversight are crucial to mitigate these risks.
Sustainability is increasingly vital for companies, addressing regulations, customer expectations, cost, and efficiency. Industry 4.0 introduces powerful tools like Internet of Things (IoT), Artificial...
Cet article étudie la possibilité pour les importateurs de gérer un stock de conteneurs vides afin de permettre la réutilisation directe de ces conteneurs...
La coordination dans les chaînes logistiques a été largement étudiée sous l’angle de l’optimisation des coûts, mais cette pratique est également proposée comme solution...
