Role of human collaboration in artificial intelligence with fuzzy based mathematical models and decision making problems.

The rapid development of artificial intelligence (AI) and machine learning (ML) has revolutionized computer technology, enabling it to make intelligent decisions, exhibit adaptive behavior, and foster synergistic human-AI environments. To ensure that human-AI collaboration works effectively, analysis frameworks that can analyze uncertain, imprecise, and multi-perspective data are necessary. Most of the fuzzy decision-making algorithms introduced in this study are machine learning-inspired fuzzy algorithms that allow for the incorporation of human expert opinions by using T-spherical fuzzy sets (TSFS) and Aczel-Alsina aggregation operators. The algorithm represents subtle human opinions in four linguistic grades, including positive, abstention, negative, and refusal, and interprets them reliably in uncertain conditions. The framework utilizes tools of fuzzy logic, including tunable operators and defuzzification methods, to process expert data, prioritize AI tools, and facilitate valuable collaboration. This methodology is validated through a case study, where professionals evaluate AI-based systems at various levels, reporting increased trust, explainability, and flexibility in the human-AI collaboration setting.