Enhancing machine learning models: Addressing challenges and future directions

Machine learning is considered as a core of modern artificial intelligence with progressive advancements throughout a spectrum including but not limited to healthcare and finance, natural language processing and self-driving cars. However, several problems remain to affect the efficiency, equal opportunities of users, and adaptability of ML models for an even faster-growing era. The limitations include shortage of high quality and access to training data, model complexity that can lead to overfitting, built in bias of the algorithm, interpretability and finally, the computational density needed for such big data models. These problems posed challenges to translate the knowledge derived from the ML systems into real-world use as well as hindering generalization of ML systems, particularly the medical and legal fields that have requirements of fairness and interpretability. These are the basic issues this journal addresses and provides possible ways of enhancing the performance of the ML models. To mitigate the problem of data deficiency, we present various techniques including data augmentation and transfer learning. To mitigate this issue, we present regularization strategies and methods of model validation. Several prevention methods are also mentioned including biasing of Algorithm and models using adversarial biasing, and Fairness-aware learning methods. Furthermore, we explore the increasing relevance of post-hoc model interpretability such as the SHAP and LIME methods which explain model’s outputs in a more detailed manner. The objective of the present Journal is to support further development of more stable, efficient and fair Machine Learning systems. In this paper, recent developments and long-term solutions are discussed to prepare the way for better and more responsible use of AI in the future.