## 7 Related Work  
As machine learning models become increasingly complex, ensuring their transparency is crucial, particularly in high-stakes domains such as healthcare and finance [23; 24; 25]. Transparency in machine learning encompasses both procedural transparency, which involves clear documentation of data collection, preprocessing and model training, and algorithmic transparency, which focuses on understanding how models arrive at their decisions [26; 27]. Both aspects are essential for assessing fairness, reliability, and accountability in predictive systems [28]. A key aspect of transparency is UQ, which provides tools to measure and communicate the confidence of model predictions [27]. UQ involves estimating different types of uncertainty, including aleatoric uncertainty, which arises from inherent randomness in the data, and epistemic uncertainty, which stems from a lack of knowledge about the model or data [27; 29; 30; 31]. Proper UQ ensures that predictions are not only accurate but also informative, helping decision-makers identify cases requiring additional validation or caution [32]. Various UQ methods exist, broadly categorized into Bayesian and Frequentist approaches [27]. CP provides a formal framework for UQ by constructing prediction sets with a guaranteed probability of containing the true label [4; 33; 34]. Unlike traditional point predictions,which provide a single best estimate, conformal methods generate set-valued predictions that account for uncertainty in a rigorous, distribution-free manner. The fundamental principle involves computing non-conformity scores, which measure how unusual a prediction is relative to past observations, and using these scores to define prediction sets that maintain finite-sample validity under the assumption of exchangeability [33; 34]. This ensures that, at a chosen confidence level, the true label falls within the prediction set with a probability at least equal to the specified threshold, making CP a well-calibrated approach to UQ [34; 35]. A key advantage of CP is its model-agnostic nature, allowing integration with any machine learning model without requiring modifications to its internal structure [4; 33]. Several variations have been introduced to improve its efficiency and applicability [34]. SCP partitions the data into separate training and calibration sets, enabling efficient computation of prediction sets without extensive retraining [34]. Mondrian conformal prediction conditions the non-conformity scores on predefined categories, such as class labels, ensuring category or class-wise valid coverage and improving performance on imbalanced datasets [36; 37]. More recent approaches focus on adaptive and distribution-free conformal methods, which refine prediction sets dynamically to improve informativeness while preserving theoretical coverage guarantees [34; 38]. Probability calibration is an essential process in machine learning that ensures the predicted probabilities of outcomes align with the actual observed frequencies. In the context of UQ and CP, proper calibration is crucial for generating reliable prediction intervals and quantifying uncertainty accurately [39; 40]. Temperature scaling, for instance, has been shown to influence CP, suggesting that overconfident models may sometimes yield different prediction sets compared to well-calibrated models [39; 41]. Venn-Abers calibration has been explored as an approach to improve probability estimates while maintaining the theoretical guarantees of CP [10]. PPM as a subdomain of Process Mining addresses fundamental aspects of process execution with a central focus on predicting upcoming events and performance indicators relevant to operational and strategical business goals [42; 43; 44]. Leveraging predictive models, predominant tasks encompass next event prediction [45; 46], identification of anomalies [47], and the prediction of possible process outcomes [48]. Early implementations in PPM relied on conventional classification and regression techniques.