Advancements in Artificial Intelligence: The Role of Deep Learning in Automated Decision-Making

The application of deep learning techniques delivers precise data-driven automation decisions to multiple industries. Researchers investigate multiple deep learning structures such as CNNs and RNNs and transformers and GANs to determine their capacities for automated decision processing. This research shows that transformer models deliver top-quality predictions but CNNs operate with higher processing speed. One main aspect of this research involved the development of bias reduction methods using adversarial debiasing and fairness-aware constraints that enhanced fairness measurements to exceed 60% while making decisions through AI less unequal. The study investigates interpretability issues through its analysis of explainability models SHAP and LIME to improve the transparency of deep learning-based choices. The research demonstrates the imperative to establish a balance between precise outcomes and fair processing along with efficient computation when handling accountable AI systems. The study advances discussions about responsible AI through the presentation of methods which reduce AI decision-making bias alongside explainability enhancement approaches. The development of hybrid AI models which unite deep learning approaches with rule-based components represents a vital research area for developing ethical and scalable AI solutions.