Till no designing approach to this fuzzy logic.
Basically, if logics are simple, then one can understand it.
Also, suitable for problems which do not have high accuracy.

When discussing the disadvantages of Fuzzy Logic Systems in an artificial intelligence interview, it’s essential to provide a balanced perspective. Here are some potential disadvantages to consider:

Complexity: Fuzzy logic systems can become quite complex, especially when dealing with multiple input variables and rules. Managing and maintaining such systems may require significant computational resources and expertise.
Difficulty in Interpretation: Fuzzy logic systems often produce results that are not immediately intuitive or easy to interpret, especially for those unfamiliar with fuzzy logic concepts. This can make it challenging for users to understand and trust the output of these systems.
Knowledge Acquisition and Rule Base Development: Constructing the rule base for a fuzzy logic system requires expertise in the domain under consideration. Acquiring the necessary knowledge and defining appropriate linguistic variables and membership functions can be time-consuming and may require input from domain experts.
Performance Issues: Depending on the complexity of the system and the computational resources available, fuzzy logic systems may suffer from performance issues, such as slow execution times or high memory usage.
Sensitivity to Parameter Selection: The performance of a fuzzy logic system can be sensitive to the selection of parameters, such as the shape and width of membership functions. Finding the optimal values for these parameters may require extensive experimentation and tuning.
Limited Formalism: Fuzzy logic lacks the mathematical rigor of classical logic, which can make it challenging to analyze and reason about formally. This lack of formalism may be a disadvantage in applications where strict logical reasoning is required.
Integration with Traditional Systems: Integrating fuzzy logic systems with existing traditional systems can be challenging, especially if those systems are based on crisp, binary logic. Compatibility issues may arise, requiring additional effort to bridge the gap between fuzzy and non-fuzzy components.

When discussing these disadvantages, it’s important to acknowledge that fuzzy logic systems also have numerous advantages and can be highly effective in certain applications, particularly those involving uncertainty, imprecision, and incomplete information. The suitability of fuzzy logic depends on the specific requirements and characteristics of the problem domain.