Perlocution vs Illocution: Impact on the Evaluation of Explanations and XAI

Introduction to Speech Act Theory

Speech Act Theory, developed by philosophers like J.L. Austin and John Searle, provides a framework for understanding how language functions not merely to convey information but to perform actions. This theory categorizes speech acts into three primary types: locutionary, illocutionary, and perlocutionary acts.  The distinction between perlocution vs illocution is particularly important in evaluating the impact of communication: while illocutionary acts focus on the speaker’s intended meaning or purpose behind the utterance, perlocutionary acts refer to the effects that the utterance has on the listener.

– Locutionary acts focus on the actual words spoken and their literal meaning.

– Illocutionary acts pertain to the speaker’s intended meaning or purpose behind the utterance.

– Perlocutionary acts refer to the effects that the utterance has on the listener, which can vary based on their interpretation and context.

Understanding these distinctions is crucial for evaluating explanations, particularly in fields like Explainable Artificial Intelligence (XAI), where the clarity and effectiveness of explanations can significantly influence user trust and comprehension.

The Role of Explanations in Different Contexts

1. In Education: Perlocution vs Illocution

   The primary goal of explanations is to facilitate understanding and learning. Here, a perlocutionary approach is often most effective, as it focuses on the listener’s comprehension and engagement. For instance, when a teacher explains a complex concept, the effectiveness of the explanation is judged by the students’ understanding and ability to apply the knowledge.

2. In Legal Contexts:

   Explanations must adhere to specific standards of clarity and objectivity. An illocutionary approach is preferred, as it emphasizes the speaker’s intention to convey information that meets legal requirements. Legal explanations must be precise and structured to ensure that all parties understand the rationale behind decisions, particularly in automated decision-making systems.

3. In XAI:

   The focus is on providing insights into the decision-making processes of AI systems. Here, a locutionary perspective is often sufficient, as it involves presenting information that clarifies how a decision was reached, regardless of the personal impact on the user. However, as XAI evolves, there is a growing recognition of the need for explanations that also consider user-centric aspects, blending locutionary and perlocutionary elements.

Perlocution vs Illocution

The distinction between illocutionary and perlocutionary explanations is particularly important in evaluating the effectiveness of explanations in XAI. 

– Illocutionary explanations aim to create understanding and are characterized by the speaker’s intent to convey meaning. For example, a legal document that explains the rationale behind a decision is crafted to ensure that the reader understands the legal basis for that decision.

– Perlocutionary explanations, on the other hand, focus on the actual effects of the explanation on the listener. In educational settings, for instance, the success of an explanation is measured by the listener’s ability to grasp the concept and apply it in practice. This approach necessitates tailoring explanations to the listener’s background and cognitive abilities.

Implications for XAI Evaluation Metrics

The implications of these distinctions extend to the evaluation of XAI systems. Current explainability measures can be categorized based on the speech act they rely on:

1. Locutionary Metrics:

   These metrics assess the clarity and correctness of the information presented. They are crucial for ensuring that the explanation accurately reflects the underlying model’s logic.

2. Illocutionary Metrics: Perlocution vs Illocution

   These metrics evaluate the depth and breadth of information provided, focusing on whether the explanation meets the intended communicative goals. In legal contexts, for example, the adequacy of an explanation is judged based on its ability to convey necessary information clearly.

3. Perlocutionary Metrics:

   These metrics measure the effectiveness of the explanation in achieving the desired impact on the listener. In educational settings, this might involve assessing whether students can apply the knowledge gained from the explanation.

Case Studies: Heart Disease Predictor and Credit Approval System

– Heart Disease Predictor: Perlocution vs Illocution

This system employs models like XGBoost and TreeSHAP to generate explanations. The evaluation focused on usability metrics, which assess how well users can interact with and understand the explanations provided. The findings indicated that while the system produced accurate locutionary explanations, it lacked in meeting the illocutionary and perlocutionary goals of user understanding.

– Credit Approval System:

This system utilized a basic Artificial Neural Network and the Counterfactual Explanations Method (CEM). The evaluation highlighted the importance of providing clear, actionable insights that not only explain the decision-making process but also empower users to understand their options. The explanations’ effectiveness was measured against both illocutionary and perlocutionary metrics.

Conclusion: Towards Contextualized Explanations

The distinctions between illocutionary, perlocutionary, and locutionary acts underscore the need for a nuanced approach to evaluating explanations in various contexts. By recognizing that different situations necessitate different types of explanations, we can develop more effective strategies for generating and assessing explanations across domains.

In educational contexts, a perlocutionary approach may be more suitable, focusing on the learner’s understanding and engagement. Conversely, in legal contexts, an illocutionary approach is essential for ensuring clarity and compliance with regulatory standards. In the realm of XAI, while locutionary explanations provide a foundational understanding, there is a growing need to integrate user-centric elements that enhance the overall effectiveness of explanations.

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