As AI is becoming a more robust technology, the challenges of comprehending and interpreting the algorithm to determine how it derives the results are increasing.
The ability of Machine Learning (ML) and Artificial Intelligence (AI) to evaluate a huge volume of diverse data; also comes up as a weakness despite the overall potential of the technology. The intricacy of the decision-making process is usually opaque before making the decision. The resolution to this challenge revolves around ‘explainability.’ If ML could describe its functioning at each stage and ensure the explainability inherent to the framework, it will assist in gaining confidence even when the algorithm is working on complex tasks.
Explainability is a logical, significant, and fascinating aspect of AI. Explainable AI (XAI) is a robust descriptive tool that offers in-depth insights way more than what traditional linear models could provide. But irrespective of the benefits, XAI has its own sets of challenges. Here are a few XAI challenges and ways to overcome them:
Challenges of XAI
Explainable AI (XAI) resembles a black box.
A lot of machine learning systems are difficult to interpret, and it becomes a challenging task for the experts as well to understand a logical explanation of the algorithm’s decisions. Implementation of black box strategies to make a decision that is unexplainable can create legal, ethical, and operational hurdles. ML machines with black-box models are not verifiable or auditable before implementation, which means it is challenging to make guarantees of their behavior. Additionally, suppose the ML system makes a subpar decision. In that case, it becomes a tough task to evaluate what factors made the machine make such a poor decision or understand what changes need to be made to correct the decision.
It is a tough task to ensure that the AI algorithm does not learn biased or unbiased world views depending on the gaps in the training data, model, and objective function.
To determine whether the decision taken by AI systems was fair or not is a challenge for XAI, because the perception of fairness is contextual and depends on the information fed to the machine learning algorithms.
It is difficult to determine if AI is reliable or not without evaluating the process of how it reached a conclusion. This is a challenge because of generalization in the statistical learning theory, essentially showing how organizations cover the gaps in unseen information.
Potential ways to overcome the XAI challenges
Following are two possible ways to overcome the challenges of XAI to offer a meaningful explanation.
Model agnostic technique
This strategy can be implemented to the complete set of algorithms or learning ways. The model agnostic approach will assist the enterprises in treating the internal functioning of XAI as an unknown black box.
This strategy can be implemented for a few or particular sets of algorithms. The model agnostic approach treats the internal functioning of XAI as a white box.
While looking at model agnostic or specific approaches, the global interpretation concentrates more on common patterns across all data points. The local interpretation concentrates on interpreting specific individual data points.