"Why did you do that?": Explaining black box models with Inductive Synthesis

By their nature, the composition of black box models is opaque. This makes the ability to generate explanations for the response to stimuli challenging. The importance of explaining black box models has become increasingly important given the prevalence of AI and ML systems and the need to build legal and regulatory frameworks around them. Such explanations can also increase trust in these uncertain systems. In our paper we present RICE, a method for generating explanations of the behaviour of black box models by (1) probing a model to extract model output examples using sensitivity analysis; (2) applying CNPInduce, a method for inductive logic program synthesis, to generate logic programs based on critical input-output pairs; and (3) interpreting the target program as a human-readable explanation. We demonstrate the application of our method by generating explanations of an artificial neural network trained to follow simple traffic rules in a hypothetical self-driving car simulation. We conclude with a discussion on the scalability and usability of our approach and its potential applications to explanation-critical scenarios.Comment: 12 pages, 1 figure, accepted for publication at the Solving Problems with Uncertainties workshop as part of ICCS 2019, Faro, Portugal, June 12-1

An Active Approach for Model Interpretation

Model interpretation, or explanation of a machine learning classifier, aims to extract generalizable knowledge from a trained classifier into a human-understandable format, for various purposes such as model assessment, debugging and trust. From a computaional viewpoint, it is formulated as approximating the target classifier using a simpler interpretable model, such as rule models like a decision set/list/tree. Often, this approximation is handled as standard supervised learning and the only difference is that the labels are provided by the target classifier instead of ground truth. This paradigm is particularly popular because there exists a variety of well-studied supervised algorithms for learning an interpretable classifier. However, we argue that this paradigm is suboptimal for it does not utilize the unique property of the model interpretation problem, that is, the ability to generate synthetic instances and query the target classifier for their labels. We call this the active-query property, suggesting that we should consider model interpretation from an active learning perspective. Following this insight, we argue that the active-query property should be employed when designing a model interpretation algorithm, and that the generation of synthetic instances should be integrated seamlessly with the algorithm that learns the model interpretation. In this paper, we demonstrate that by doing so, it is possible to achieve more faithful interpretation with simpler model complexity. As a technical contribution, we present an active algorithm Active Decision Set Induction (ADS) to learn a decision set, a set of if-else rules, for model interpretation. ADS performs a local search over the space of all decision sets. In every iteration, ADS computes confidence intervals for the value of the objective function of all local actions and utilizes active-query to determine the best one.Comment: NeurIPS 2019 workshop on Human-Centric Machine Learnin

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI

This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions

"Why did you do that?" : Explaining black box models with Inductive Synthesis

By their nature, the composition of black box models is opaque. This makes the ability to generate explanations for the response to stimuli challenging. The importance of explaining black box models has become increasingly important given the prevalence of AI and ML systems and the need to build legal and regulatory frameworks around them. Such explanations can also increase trust in these uncertain systems. In our paper we present RICE, a method for generating explanations of the behaviour of black box models by (1) probing a model to extract model output examples using sensitivity analysis; (2) applying CNPInduce, a method for inductive logic program synthesis, to generate logic programs based on critical input-output pairs; and (3) interpreting the target program as a human-readable explanation. We demonstrate the application of our method by generating explanations of an artificial neural network trained to follow simple traffic rules in a hypothetical self-driving car simulation. We conclude with a discussion on the scalability and usability of our approach and its potential applications to explanation-critical scenarios

Exploring recovery in people with learning disabilities

A literature review examines how mental health difficulties may differ for people with learning disabilities (PwLD) and the general population; with respect to their vulnerability to mental ill-health and the definition, presentation and treatment of mental health problems. Factors which have been found to positively impact on the mental health of PwLD are then explored. The review considers methodological limitations and gaps in our understanding, highlighting a need for further research focusing on mental health recovery for individuals with learning disabilities. Section B presents a study exploring what recovery means for people with learning disabilities and mental health difficulties. Interviews were conducted with nine individuals and Interpretative Phenomenological Analysis used. A model was developed which described participants’ entry to the therapeutic service and their progression towards recovery. The model firstly describes how participants felt entering the service, their Mental Health/Therapy Experience, and explains how these experiences were validated and the recovery process enabled through the therapeutic alliance. Once enabled, the second dimension of the model is detailed, that of the Client Recovery Experience which extends across three phases of recovery. The first phase, Feeling Better describes elements perceived as integral to improved mental health. The second phase, Recovery Ongoing identifies that more input is required. Thirdly, Attainability?: Reality, Ideals and Fantasy, reveals PwLD’s perceptions of recovery and the techniques used in striving to achieve this. Salient features of recovery specific to PwLD are recognised as important to clinical practice, and results suggest there is a need to openly discuss LD identity, and address idealisations surrounding a non-LD one. This would involve setting realistic goals and managing expectations accordingly and focussing on social integration as a way of enabling ongoing recovery

From Swaddling to Swastikas: A Life-Course Investigation of White Supremacist Extremism

To date, most terrorism research concerned with the long-term development of extremist behavior focuses on patterns of terrorist attacks, long-term responses to extremist violence or organizational longevity of extremist groups. The current study addresses this void in the existing literature by relying on life-history interviews with 91 North American-based former white supremacists to examine the developmental conditions associated with extremist onset. My attention is primarily focused on individual-level experiences; particularly how childhood risk factors (e.g., abuse, mental illness) and racist family socialization strategies generate emotional and cognitive susceptibilities toward extremist recruitment. This type of investigation contributes to terrorism research by emphasizing some of the early childhood and adolescent experiences that may heighten a person’s vulnerabilities to certain pulls associated with ideology and group dynamics more broadly. Overall, findings from the current dissertation build upon developmental-life course criminology and studies within terrorism that address the role of childhood and adolescent risk factors. In particular, I elaborate on the work of Simi and colleagues (2016) and offer additional context as to the precursors that influence extremist onset

Young people’s experiences of abuse and conflict within their intimate partner relationships

Research shows Adolescent Intimate Partner Abuse (AIPA) is a widespread problem with potential to impact significantly upon wellbeing. This thesis aimed to further the existing body of knowledge from a psychologically orientated perspective. Section one presents a meta-synthesis of qualitative studies exploring young people’s experiences of the intersection between AIPA and new technologies. This followed the seven-step meta-ethnographic approach of Noblit and Hare (1988). Twelve eligible papers were identified that yielded three themes relating to technology as a platform for creating jealousy, and enabling the subsequent monitoring, and control of partners. Production of an overarching theme referring to technology and protection of ‘self-interests’ was enabled. Findings suggest technology represents a motivator and means for carrying out AIPA and that this occurs against a backdrop of adolescent development, including acquisition of gendered roles. Section two presents a descriptive study, employing semi-structured interviews, that set out to explore young people’s experiences of psychological wellbeing in relation to AIPA. Participants were sixteen young people, aged 13 to 17 years, who had encountered self- defined ‘difficulties’ within their relationships. Data gathered were analysed using thematic analysis, resulting in the emergence of three themes that, when viewed as a whole, suggested events surrounding abusive acts cause considerable negative emotional impact of an anxious nature, and technology is regarded as integral to how problems manifest, contributing a significant burden for mental health. Section three is an extended critique of the research paper, considering the role of personal positionality in shaping the research process and concluding that reflexivity in this area is of particular relevance to those working within caring professions and undertaking research. Taken together, the thesis offers insights relevant to practice, policy, and future research, including furthering of theory. Clinical psychologists are well positioned to implement and further these findings within their practice and wider activities

Teacher educator perceptions of mathematical knowledge for teaching – A phenomenographic study

Teacher education is a complex policy and practice landscape and teacher educator roles are changing. This thesis investigates variation in how teacher educators in England account for their practices within initial teacher education, in the context of secondary mathematics. It sets out a view of their experiences of teacher education in relation to different ways of accounting for relations between knowledge about teaching and learning mathematics and knowledge of mathematics. Outcomes are based on interviews with sixteen participants, including both school-based and university-based teacher educators. Teacher educator accounts comprised four, hierarchically inclusive descriptions of learning in teacher education: (1) understanding teaching and learning in mathematics (2) linking teaching and learning with the subject discipline of mathematics (3) integrating teaching and learning with the subject discipline of mathematics (4) reconceptualisation of teaching and learning with mathematics. Accounts of teacher education are structured as expanding opportunities for student teachers to engage critically with the process of reconceptualising mathematical knowledge into ‘school maths’ knowledge. The most developed accounts position teacher education as drawing on opportunities for student teachers to experience interplay between mathematics and school mathematics through advanced and multiple mathematics perspectives. This interplay is a site of learning in teacher education. Participants hold a range of teacher educator roles, including school-based and university-based roles. Teacher educators in school-led roles were less able to draw on this interplay in practice and so this thesis is a further and timely contribution to the research about university-based teacher educators. This thesis contributes a curriculum forms perspective that focuses on teacher educator positions on the role of the curriculum in developing new teachers

Ethical Decision-Making Accounting Competencies: Practitioners’ Perspectives

Recent accounting and corporate scandals call into question the ethicality of accounting practice, likewise accounting graduates. Advances in ethical accounting education are not emphasized in today’s business schools leaving accounting graduates lacking necessary ethical accounting competencies to make ethical accounting decisions (Abend, 2013; Mastracchio, Jiménez-Angueira, & Toth, 2015; Waples, Antes, Murphy, Connelly, & Mumford, 2009). Cognitive moral development theory (Kohlberg & Hersh, 1977), stakeholder theory (Freeman, 2004), and organizational mindfulness theory (Ray, Baker, & Plowman, 2011) provide a theoretical framework supporting issues of accounting graduates’ ethicality, practitioner in the role of ethical decision-maker, and organizational awareness and performance of business schools. The purpose of this study is to determine practitioners’ perspectives of ethical accounting competencies required for making ethical accounting decisions. An additional purpose is to identify ethical accounting issues and behaviors experienced by practitioners. Finance and accounting practitioners, through qualitative interviews, provide professional perspectives relevant to ethical accounting practice topics to demonstrate practice-oriented understanding and knowledge of ethical accounting decision making. Interpretative phenomenological analysis was used to investigate ethical business experiences and interpret professional’s perceptions. Practitioners recognize problems due to lack of trust within their organizations. Additionally, they provide experiences where amoral decision making resulted in inappropriate treatment of accounting standards and principles. Study practitioners identified six ethical accounting competencies useful in ethical decision making. These competencies and other strategies could be valuable to accounting educators in developing ethical accounting curriculum