<h2><strong><span style="font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">Introduction to Speech Act Theory</span></strong></h2>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">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&#8217;s intended meaning or purpose behind the utterance, perlocutionary acts refer to the effects that the utterance has on the listener.</span></p>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;"><strong>&#8211; Locutionary acts</strong> focus on the actual words spoken and their literal meaning.</span></p>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;"><strong>&#8211; Illocutionary</strong> acts pertain to the speaker&#8217;s intended meaning or purpose behind the utterance.</span></p>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;"><strong>&#8211; Perlocutionary acts</strong> refer to the effects that the utterance has on the listener, which can vary based on their interpretation and context.</span></p>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">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.</span></p>
<h2><span style="color: #000000;"><strong><span style="font-family: arial, helvetica, sans-serif; font-size: 14px;">The Role of Explanations in Different Contexts</span></strong></span></h2>
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<p><span style="font-family: arial, helvetica, sans-serif; font-size: 14px;">Explanations serve various purposes across different domains, such as education, law, and artificial intelligence. Each context necessitates a different approach to explanations, influenced by the underlying speech acts. In the discussion of &#8220;Perlocution vs Illocution,&#8221; understanding the distinct roles these speech acts play is crucial for tailoring explanations to effectively meet the needs of different audiences.</span></p>
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<h3><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;"><strong>In Education: Perlocution vs Illocution</strong></span></h3>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">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&#8217;s comprehension and engagement. For instance, when a teacher explains a complex concept, the effectiveness of the explanation is judged by the students&#8217; understanding and ability to apply the knowledge.</span></li>
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<h3><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;"><strong>In Legal Contexts:</strong> </span></h3>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">Explanations must adhere to specific standards of clarity and objectivity. An illocutionary approach is preferred, as it emphasizes the speaker&#8217;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.</span></li>
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<h3><strong><span style="font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">In XAI: </span></strong></h3>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">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.</span></li>
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<h2><strong><span style="font-family: arial, helvetica, sans-serif; font-size: 14px;">Perlocution vs Illocution</span></strong></h2>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">The distinction between illocutionary and perlocutionary explanations is particularly important in evaluating the effectiveness of explanations in XAI. </span></p>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;"><strong>&#8211; Illocutionary explanations</strong> aim to create understanding and are characterized by the speaker&#8217;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.</span></p>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;"><strong>&#8211; Perlocutionary explanations</strong>, 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&#8217;s ability to grasp the concept and apply it in practice. This approach necessitates tailoring explanations to the listener&#8217;s background and cognitive abilities.</span></p>
<h2><span style="color: #000000;"><strong><span style="font-family: arial, helvetica, sans-serif; font-size: 14px;">Implications for XAI Evaluation Metrics</span></strong></span></h2>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">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:</span></p>
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<h3><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;"><strong>Locutionary Metrics:</strong> </span></h3>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">These metrics assess the clarity and correctness of the information presented. They are crucial for ensuring that the explanation accurately reflects the underlying model&#8217;s logic.</span></li>
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<h3><span style="color: #000000;"><strong><span style="font-family: arial, helvetica, sans-serif; font-size: 14px;">Illocutionary Metrics: Perlocution vs Illocution</span></strong></span></h3>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;"> 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.</span></li>
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<h3><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;"><strong>Perlocutionary Metrics:</strong> </span></h3>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">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.</span></li>
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<h2><span style="color: #000000;"><strong><span style="font-family: arial, helvetica, sans-serif; font-size: 14px;">Case Studies: Heart Disease Predictor and Credit Approval System</span></strong></span></h2>
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<div class="relative p-1 rounded-sm flex items-center justify-center bg-token-main-surface-primary text-token-text-primary h-8 w-8"><span style="font-size: 14px; font-family: arial, helvetica, sans-serif;">To illustrate the practical implications of these theoretical distinctions, we analyzed two real-world XAI systems: a heart disease predictor and a credit approval system. We evaluated both systems using various explanation metrics associated with distinct speech acts.</span></div>
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<h3><span style="color: #000000;"><strong><span style="font-family: arial, helvetica, sans-serif; font-size: 14px;">&#8211; Heart Disease Predictor: Perlocution vs Illocution</span></strong></span></h3>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">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.</span></p>
<h3><span style="color: #000000;"><strong><span style="font-family: arial, helvetica, sans-serif; font-size: 14px;">&#8211; Credit Approval System:</span></strong></span></h3>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;"> 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. </span><span style="font-size: 14px; font-family: arial, helvetica, sans-serif;">The explanations&#8217; effectiveness was measured against both illocutionary and perlocutionary metrics.</span></p>
<h2><span style="color: #000000;"><strong><span style="font-family: arial, helvetica, sans-serif; font-size: 14px;">Conclusion: Towards Contextualized Explanations</span></strong></span></h2>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">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.</span></p>
<p><span style="font-weight: 400; font-family: arial, helvetica, sans-serif; font-size: 14px; color: #000000;">In educational contexts, a perlocutionary approach may be more suitable, focusing on the learner&#8217;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.</span></p>
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<p><span style="font-family: arial, helvetica, sans-serif; font-size: 14px;">Future research should explore the practical applications of these findings, investigating methods for systematically adapting explanations based on context and user needs. By delving deeper into the distinctions between Perlocution vs Illocution, researchers can develop more effective and contextually appropriate explanations. This will ultimately benefit a wide range of fields, from education and law to artificial intelligence and natural language processing.</span></p>
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Perlocution vs Illocution: Impact on the Evaluation of Explanations and XAI