Enhancing Artificial Intelligence Control Mechanisms: Current Practices, Real Life Applications and Future Views

The popularity of Artificial Intelligence has grown lately with the potential it promises for revolutionizing a wide range of different sectors. To achieve the change, whole community must overcome the Machine Learning (ML) related explainability barrier, an inherent obstacle of current sub symbolism-based approaches, e.g. in Deep Neural Networks, which was not existing during the last AI hype time including some expert and rule-based systems. Due to lack of transparency, privacy, biased systems, lack of governance and accountability, our society demands toolsets to create responsible AI solutions for enabling of unbiased AI systems. These solutions will help business owners to create AI applications which are trust enhancing, open and transparent and also explainable. Properly made systems will enhance trust among employees, business leaders, customers and other stakeholders. The process of overseeing artificial intelligence usage and its influence on related stakeholders belongs to the context of AI Governance. Our work gives a detailed overview of a governance model for Responsible AI, emphasizing fairness, model explainability, and responsibility in large-scale AI technology deployment in real-world organizations. Our goal is to provide the model developers in an organization to understand the Responsible AI with a comprehensive governance framework that outlines the details of the different roles and the key responsibilities. The results work as reference for future research is aimed to encourage area experts from other disciplines towards embracement of AI in their own business sectors, without interpretability shortcoming biases. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.