The consideration of personal qualities in admissions for Canadian master's counselling and counselling psychology programs

Research demonstrates the need for counsellors and psychologists to possess certain personal qualities to be effective practitioners (e.g., Jennings & Skovholt, 1999). However, the degree to which some of these characteristics can be taught during graduate school is questionable (Pope & Kline, 1999), and, thus, some researchers argue that personal qualities are an important factor to assess during the admissions process (Halinski, 2009; McCaughan & Hill, 2015). However, little is known about how personal quality considerations are incorporated into the admissions process for Canadian master’s programs in counselling and counselling psychology. This study serves as a preliminary exploration of how a subset of Canadian faculty consider and assess personal qualities during admissions reviews for said programs. Participants were interviewed to explore the counsellor personal qualities deemed important by each individual as well as how such considerations currently, and might ideally, play into admissions decisions. Themes arising from these semi-structured interviews and their implications for future research and practice are explored

Multi-Objective Counterfactual Explanations

Counterfactual explanations are one of the most popular methods to make predictions of black box machine learning models interpretable by providing explanations in the form of `what-if scenarios'. Most current approaches optimize a collapsed, weighted sum of multiple objectives, which are naturally difficult to balance a-priori. We propose the Multi-Objective Counterfactuals (MOC) method, which translates the counterfactual search into a multi-objective optimization problem. Our approach not only returns a diverse set of counterfactuals with different trade-offs between the proposed objectives, but also maintains diversity in feature space. This enables a more detailed post-hoc analysis to facilitate better understanding and also more options for actionable user responses to change the predicted outcome. Our approach is also model-agnostic and works for numerical and categorical input features. We show the usefulness of MOC in concrete cases and compare our approach with state-of-the-art methods for counterfactual explanations