The disagreement dilemma in explainable AI: can bias reduction bridge the gap

Explainable AI is an emerging field of research since the spread of AI in multifarious fields. The opacity and inherent black-box nature of the advanced machine learning models create a lack of transparency in them leading to the insufficiency in societal recognition. The increasing dependence on AI across diverse sectors has created the need for informed decision-making of the numerous predictive models used. XAI strives to close this divide by providing an explanation of the decision-making process, promoting trust, ensuring adherence to regulations, and cultivating societal approval. Various post-hoc techniques including well-known methods like LIME, SHAP, Integrated Gradients, Partial Dependence Plot, and Accumulated Local Effects have been proposed to decipher the intricacies of complex AI models. In the context of post hoc explanatory methods for machine learning models there arises a conflict known as the Disagreement problem where different explanation techniques provide differing interpretations of the same model. In this study, we aim to find whether reducing the bias in the dataset could lead to XAI explanations that do not disagree. The study thoroughly analyzes this problem, examining various widely recognized explanation methods. Our method aims to understand the effect of bias versus the effect after removing it. It also aims to understand the effect of bias on the explainability of the model predictions in light of the work of other authors on bias removal and fairness.