E x TRA : Explainable Therapy-Related Annotations (2020)

Abstract: In this paper we report progress on a novel explainable artificial intelligence (XAI) initiative applying Natural Language Processing (NLP) with elements of codesign to develop a text classifier for application in psychotherapy training. The task is to produce a tool that will facilitate therapists to review their sessions by automatically labelling transcript text with levels of interaction for patient activation in known psychological processes, using XAI to increase their trust in the model’s suggestions and client trajectory predictions. After pre-processing of the language features extracted from professionally annotated therapy session transcripts, we apply a supervised machine learning approach (CHAID) to classify interaction labels (negative, neutral, positive). Weighted samples are used to overcome class imbalanced data. The results show this initial model can make useful distinctions among the three labels of patient activation with 74% accuracy and provide insight into its reasoning. This ongoing project will additionally evaluate which XAI approaches can be used to increase the transparency of the tool to end users, exploring whether direct involvement of stakeholders improves usability of the XAI interface and therefore trust in the solution.

## introduction it takes a lot of manual effort to quality-assure psychotherapy sessions @xcite , and therefore assessments of quality are rarely used routinely in psychotherapy practice. this work seeks to produce a tool that can automatically code psychotherapy transcripts, in line with a coding scheme developed by psychotherapists, known to characterise predictors of recovery @xcite . the tool is also being developed to present explanations of the reasons for the coding decisions it makes. explaining algorithms to those taking actions based on their outputs is recognised as good practice in data-driven health and care technology @xcite . the extra-ppolate foot_0 project is the first step in building tools to optimise scarce resources for provision of mental healthcare @xcite by enabling therapists to adhere to good practice @xcite and deliver care tailored to the patient @xcite . ## overall aims of programme the long-term objectives that we aim to achieve throughout our programme are threefold: aim 1 to build the foundation for unobtrusive, objective, transdiagnostic measures of patient activation. ## methods core project team members were separately surveyed as to their initial hypotheses for key language markers of client-therapist interaction that is deemed helpful, focusing on generating features from different perspectives (see ## data analysis data distribution and model selection the data was skewed, and it was necessary to collapse some similar categories to ensure sufficient representation. we employed chi-square automatic interaction detection (chaid), a type of decision tree (dt) classification model that can handle both categorical and numeric data sets. it does not require common statistical assumptions such as normality and non-collinearity @xcite . for imbalanced data, dt models allow weighting samples according to their importance. a sub-category of the outcome variable having smaller number of samples is assigned higher weight than as compare to other category with larger number of samples. since positive and neutral category ratings were more common in the dataset than negative ratings, negatively categorised data were weighted for balance. how decision trees work dt models work by recursively partitioning the samples into a number of subsets. the starting node (at the top of the tree) is termed as "root". any node with outgoing nodes is termed as an internal node, while the nodes without further branches are called "leaves". at each node, the chi-square test for association is applied and the variable having the strongest association with the outcome variable is selected for further split into leaves. the chosen variable is the one that expresses the strongest discrimination between the different levels of outcome variable. the algorithm keeps dividing the full data set into subsets using the depth-first approach until the stopping criterion is not met @xcite . validation for internal validation of the model or when no validation data set is available, the model can perform k-fold cross validation. finally, the results from different k folds were merged to produce a single dt estimation. dt models also offer tree pruning and feature selection based on the chi-squared test to prevent overfitting of the model. a "minimum cases" criteria is used for deciding further split of a branch. discrimination of the original and cross-validated models was evaluated through the generation of receiver operating characteristic (roc) curves and calculation of cstatistics. ## initial findings chaid label classification results are summarised in the classification matrix in agile science approach @xcite repeated engagement with end-users is intended to build trust @xcite and emulates industry best practice. the project leverages specialist support from a social enterprise foot_1 on coproduction aspects @xcite , and a digital health industry partner foot_2 on user experience (ux) design. collaborative (machine) learning in the tool and the process: in combination with the agile, participatory approach, the use of human-in-the-loop techniques will enable refinement of definitions and expose and explore tacit and latent knowledge in assessment of psychotherapy through direct involvement of domain experts in model development. through prototyping a person-centred active learning process, we anticipate a two-way exchange of insights which will clarify what helps and what hinder the psychotherapy process. using evidence-based tools to capture key considerations: trustscapes foot_3 were used to identify the core factors contributing to trust throughout the model pipeline (data, processing, deployment). combined with a prosocial approach foot_4 , this elicited fundamental stakeholder requirements for the qualities of an engaging, interactive feedback interface, and actions needed to mitigate wider concerns about its acceptability. the software usability scale plus (sus+ @xcite ) will be used as a proxy metric to evaluate explainability, supplemented by detailed qualitative feedback through 'think-aloud' exercises @xcite . measurement of trust in xai is a new and developing field @xcite @xcite whereas the sus+ is well established in human computer interaction, seen as a practical compromise to capturing important aspects @xcite (usability and trust are interdependent (acemyan and kortum, 2012)), and has been used as the basis of measures of quality of explanations @xcite .