# Fuzzy Cognitive Maps: Their Role in Explainable Artificial Intelligence
## 4 Results
### FCM and Causality
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Causality** & **ML and DL Suitability** & **FCM Suitability** \\ \hline \multirow{3}{*}{Type I} & Causality between all concepts is & \multirow{3}{*}{Unsuitable:} \\  & known and not disputed. No & & There is no need for learning \\  & hidden relationships are expected. & & \\  & Causality between some concepts is & & Slightly Suitable: \\  & known, but there is a freedom to & & Slight weight-learning modifications \\  & discover the strength. No hidden & & required—post-hoc explainability \\  & relationships are expected. & & algorithms can reveal the outcomes \\  & Causality between some concepts is & & Slightly Suitable: \\  & known. We are keen to discover & & \\  & unexpected cause-and-effect & & \\  & relationships. & & \\  & Causality between concepts is & & \\  & suspected but not verified. & & \\  & Causality between concepts is & & \\  & & & \\  & Causality between concepts is & & \\  & entirely unknown. & & \\  & & algorithms can reveal the outcomes & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different types of expected/unexpected cause-and-effect relationships among the input data and the suitability of each method. Various problems involve known interconnections between concepts and ambiguous relations that we are keen to discover. In such cases, FCMs yield their best potential as they can combine human knowledge modelling with the freedom to learn within specific constraints. ML and DL methods can adapt to this situation, but it would require much effort to narrow the learning freedom and freeze some weights. In the work of [PERSON] [41], the developed FCM revealed that Iran's solar photovoltaics development is affected by severe economic uncertainty, exogenous factors, and proactive political and economic parameters. The authors not only modelled the known causal relationships but were able to suggest further relationships. In the work of [PERSON] et al. [42], the authors demonstrated some policy drivers, such as \"public food waste rules\", \"investments and infrastructure\", and \"small-scale farming\", that are particularly effective in supporting a new and sustainable food consumption model. Their FCM model modelled the causality of the involved attributes successfully. In [43], the authors developed an analytical framework for developing policy strategies to optimize municipal waste management systems (MWMSs) in the case of the Land of Fires (LoF) and considered simulations of different waste management strategies. The authors identified the optimal strategy for the LoF MWMS, such as fostering social innovation through information-based instruments and promoting innovation in material and process design. [PERSON] et al. [44] studied the potential impact of Brexit on the UK's energy, water, and food (EWF) nexus. They designed an FCM that revealed an unexpected decrease in EWF demand under a \"hard-Brexit\" scenario. In cases wherein there are undiscovered cause-and-effect relationships, training data is required. Learning from the training data is a feature of ML and DL models. However, they cannot adequately explain their decisions. On the contrary, FCMs can benefit from learning algorithms and reveal such interconnections, thereby neglecting their nature and becoming learning machines. A plethora of recent FCM-oriented studies implied cause-and-effect relationships.