Generating Simple, Conservative and Unifying Explanations for Logistic Regression Models (2024)

Abstract: In this paper, we generate and compare three types of explanations of Machine Learning (ML) predictions: simple, conservative and unifying. Simple explanations are concise, conservative explanations address the surprisingness of a prediction, and unifying explanations convey the extent to which an ML model’s predictions are applicable. The results of our user study show that (1) conservative and unifying explanations are liked equally and considered largely equivalent in terms of completeness, helpfulness for understanding the AI, and enticement to act, and both are deemed better than simple explanations; and (2)users’ views about explanations are influenced by the (dis)agreement between the ML model’s predictions and users’ estimations of these predictions, and by the inclusion/omission of features users expect to see in explanations.

## introduction the increased accuracy of machine learning (ml) models has led to their widespread adoption by decision makers in vital domains, such as healthcare and finance. this highlights the need for explanations of the outcomes of these models to support decision making by practitioners and end users. to generate explanations, we adopt the humancentered view in @xcite , whereby an explanation is "not about the model, but about the evidence that led to the prediction" (according to the model). our explanations are aimed value feature: value buying price: conservative explanation even though this car has a high buying price, the ai system deems this car acceptable mainly because it has a seating capacity of four and a medium safety rating. however, if this car had a seating capacity of two, then the ai system would deem it unacceptable. unifying explanation the ai system deems this car acceptable mainly because it has a seating capacity of four and a medium safety rating. in fact, 85 out of 100 cars with a seating capacity of four and a medium safety rating are deemed acceptable by the ai system. at non-expert users, whose goals are to obtain a basic understanding of the reasons for a prediction, and to decide on a course of action. specifically, we generate three types of explanations, simple, conservative and unifying, foot_0 and examine their influence on the achievement of these goals. a simple explanation implements ockham's razor. it presents the most influential feature values that lead to a predicted outcome. these explanations are the baseline in our evaluation (section 4). a conservative explanation decreases the degree to which we find an outcome surprising (increases its expectedness). it comprises a simple explanation plus a concessive-contrastive and a counterfactual component -the former acknowledges feature values that would normally yield an outcome that differs from the predicted one, and the latter mentions the fewest changes required to get the not-predicted outcome. these components have strong support in the explainable artificial intelligence (xai) literature @xcite @xcite @xcite @xcite . finally, a unifying explanation conveys the extent of the coverage of a prediction to other entities -in our case, these are instances that have the same influential feature values as those of the instance at hand (but may differ with respect to other values). it comprises the simple explanation plus a component that communicates the proportion of instances with the same influential feature values and the same predicted outcome as the current instance. this type of explanation has been considered only in @xcite . in this paper, we offer new algorithms for generating simple, conservative and unifying explanations of the outcomes of logistic regression models. these models, which are widely used in healthcare and the social sciences, are considered transparent, i.e., they are "interpretable by a machine learning expert or a statistician" @xcite . it is important to explain the predictions of transparent models because (1) these models are commonly used as local surrogate explainer models that approximate neural networks for an instance of interest (section 2); (2) transparent models are employed when the data are insufficient for neural models; and (3) even if transparent models are understandable by ml experts, they may be unclear to lay practitioners and end users. we conducted a user study to evaluate our explanations. our main findings are that conservative and unifying explanations are deemed largely equivalent, are liked more than simple explanations, and are deemed more complete, more helpful for understanding the ai's reasoning and more enticing to act than simple explanations. also, users' views about explanations are influenced by the (dis)agreement between the ai's predictions and users' estimates of these predictions, and by the inclusion/omission of features users expect to see in explanations. this paper is organised as follows. section 2 discusses related work, section 3 describes our explanation-generation algorithms. our user study appears in section 4 and its results in section 5. section 6 discusses key findings and future work. ## related research the sub-field of xai focuses on explaining the predictions made by ml models. in particular, neural networks have received a lot of attention, owing to their superior performance on one hand, and their opaqueness on the other hand. transparent models as local surrogate explainers. linear regression, decision rules and decision trees have been used to this effect. under linear regression, an explanation is cast as a linear combination of the input features of a model, where the coefficients are learned by perturbing the features in the local neighbourhood of an instance of interest @xcite , or by approximating a feature's shapley value @xcite . the explanations generated by these systems comprise feature attributions that represent the contribution of important features to a model's prediction. looking at decision rules, @xcite search for the smallest set of "anchor rules" that describes the largest part of the input space and respects a precision threshold. the works that approximate the local neighbourhood of an instance via decision trees specify this neighbourhood in different ways; they also consider contrastive and counterfactual explanations @xcite . ## generating explanations our explanation-generation algorithms receive three main inputs: an instance x x x, a logistic regression model denoted f β β β , and an outcome y predicted by the model for the instance in question; the instance x x x comprises features {x 1 , . . . , x n }, each associated with a value. in this section, we specify the logistic regression classifier employed in our research, and describe algorithms that generate simple, conservative and unifying explanations for the outcomes produced by this classifier. ## experimental setup we consider two research questions: rq1: how do the three types of explanations compare to each other in terms of completeness (no missing information), presence of misleading/contradictory/irrelevant information, users' understanding of the ai's reasoning for a predicted outcome, and enticement to act on the prediction @xcite , and the extent to which an explanation is liked? rq2: which independent variables influence users' views about the three types of explanations? we first describe our dataset and classifier, followed by the user study and our results. foot_4 ## results we addressed the research questions as follows. ## conclusion we have offered algorithms that generate simple, conservative and unifying explanations for predictions made by a logistic regressor; and we reported the results of a user study where we evaluated these explanations in terms of the extent to which they were liked and four explanatory attributes, viz completeness, presence of misleading/contradictory/irrelevant information, helpfulness to understand the ai's reasoning, and enticement to act on the ai's prediction. we also considered the influence of three independent variables on users' views about our explanations, viz predicted outcome, (dis)agreement between the ai's prediction and users' estimates of these predictions, and presence/absence of features users expect to see in explanations. comparison between explanation types. our results show that conservative and unifying explanations are better liked than their simple counterparts, and are deemed more complete, more helpful to understand the ai's reasoning, and more enticing to act on the ai's prediction; and that unifying explanations are deemed largely equivalent to conservative explanations. in the future, it would be interesting to compare an explanation that combines conservative and unifying explanations with each of these explanation types. effect of independent variables. firstly, the outcome predicted by the ai has little effect on users' views about our explanations. second, conservative and unifying explanations are deemed better than simple explanations when the ai's predictions agree with users' estimates of these predictions. however, when they disagree, conservative and unifying explanations are only liked better than simple explanations, and are deemed equivalent for the other attributes. this result may be partially attributed to the small number of data points for disagreement. in addition, these findings with respect to conservative explanations, which have a contrastive component, are at odds with those in @xcite , where contrastive explanations of decision-tree predictions were particularly favoured when the ai's predictions and users' estimates of these predictions disagreed. this suggests that the factors that affect users' views about explanations may be more nuanced than simply having a contrastive aspect, e.g., whether a contrastive component explicitly mentions the expectations it is addressing, as done in @xcite . finally, users have domain-specific expectations about features that should appear in explanations, regardless of their effect on the outcome, and not meeting these expectations adversely affects users' views about explanations.