Abakar Saidov is co-founder and CEO of Beamery, a leader in talent lifecycle management. In the wake of the "Great Reshuffle," companies continue to reevaluate their approach to recruitment and retention. In order to drive efficiency and remain effective at scale, business leaders are increasingly turning to new technologies for support. One of the most valuable technologies supporting talent management strategies today is artificial intelligence (AI). It has the potential to revolutionize the way in which businesses interact with the wider talent landscape, helping HR teams and recruiters fill much-needed positions and identify the skill sets in most demand.

The goal of this work is to develop a robot equipped with goal-driven explainability, i.e. a robot will explain its behavior to achieve a particular goal in a collaborative setting. The major factor in goal-driven explainability is the human'theory of mind'. In this work, we will employ Leslies' theory of mind model which includes a mechanical agency, an actionable agency and a belief agency. This thesis will focus on explaining the desire of the robot and the belief of the human if its different to the robot's intention or desire. We aim to develop a common theoretical framework for the development of understandable robots which will include learning to generate explanations, non-verbal and verbal ways of communication and explanations in context.

Recent years have seen a surge of interest in the field of explainable AI (XAI), with a plethora of algorithms proposed in the literature. However, a lack of consensus on how to evaluate XAI hinders the advancement of the field. We highlight that XAI is not a monolithic set of technologies -- researchers and practitioners have begun to leverage XAI algorithms to build XAI systems that serve different usage contexts, such as model debugging and decision-support. Algorithmic research of XAI, however, often does not account for these diverse downstream usage contexts, resulting in limited effectiveness or even unintended consequences for actual users, as well as difficulties for practitioners to make technical choices. We argue that one way to close the gap is to develop evaluation methods that account for different user requirements in these usage contexts. Towards this goal, we introduce a perspective of contextualized XAI evaluation by considering the relative importance of XAI evaluation criteria for prototypical usage contexts of XAI. To explore the context dependency of XAI evaluation criteria, we conduct two survey studies, one with XAI topical experts and another with crowd workers. Our results urge for responsible AI research with usage-informed evaluation practices, and provide a nuanced understanding of user requirements for XAI in different usage contexts.

Ontology is a popular method for knowledge representation in different domains, including the legal domain, and description logics (DL) is commonly used as its description language. To handle reasoning based on inconsistent DL-based legal ontologies, the current paper presents a structured argumentation framework particularly for reasoning in legal contexts on the basis of ASPIC+, and translates the legal ontology into formulas and rules of an argumentation theory. With a particular focus on the design of autonomous vehicles from the perspective of legal AI, we show that using this combined theory of formal argumentation and DL-based legal ontology, acceptable assertions can be obtained based on inconsistent ontologies, and the traditional reasoning tasks of DL ontologies can also be accomplished. In addition, a formal definition of explanations for the result of reasoning is presented.

In the last few years, many explanation methods based on the perturbations of input data have been introduced to improve our understanding of decisions made by black-box models. The goal of this work is to introduce a novel perturbation scheme so that more faithful and robust explanations can be obtained. Our study focuses on the impact of perturbing directions on the data topology. We show that perturbing along the orthogonal directions of the input manifold better preserves the data topology, both in the worst-case analysis of the discrete Gromov-Hausdorff distance and in the average-case analysis via persistent homology. From those results, we introduce EMaP algorithm, realizing the orthogonal perturbation scheme. Our experiments show that EMaP not only improves the explainers' performance but also helps them overcome a recently-developed attack against perturbation-based methods.

How we bank, where we bank and who we bank with are changing dramatically. These incredible shifts are being driven by increased customer expectations and the power of disruptive technology to meet them.If evidence were needed for such an assertion, the rise of Buy Now Pay Later (BNPL) would be a good place to start. Consumers today are used to fast, seamless, personalized experiences from global platforms like Netflix or Amazon. It's an experience that's also expected from banking: an intuitive and embeddable journey through everyday transactions. BNPL provides such an experience.

This study investigates the impact of machine learning models on the generation of counterfactual explanations by conducting a benchmark evaluation over three different types of models: decision-tree (fully transparent, interpretable, white-box model), a random forest (a semi-interpretable, grey-box model), and a neural network (a fully opaque, black-box model). We tested the counterfactual generation process using four algorithms (DiCE, WatcherCF, prototype, and GrowingSpheresCF) in the literature in five different datasets (COMPAS, Adult, German, Diabetes, and Breast Cancer). Our findings indicate that: (1) Different machine learning models have no impact on the generation of counterfactual explanations; (2) Counterfactual algorithms based uniquely on proximity loss functions are not actionable and will not provide meaningful explanations; (3) One cannot have meaningful evaluation results without guaranteeing plausibility in the counterfactual generation process. Algorithms that do not consider plausibility in their internal mechanisms will lead to biased and unreliable conclusions if evaluated with the current state-of-the-art metrics; (4) A qualitative analysis is strongly recommended (together with a quantitative analysis) to ensure a robust analysis of counterfactual explanations and the potential identification of biases.

Nowadays Artificial Intelligence (AI) has become a fundamental component of healthcare applications, both clinical and remote, but the best performing AI systems are often too complex to be self-explaining. Explainable AI (XAI) techniques are defined to unveil the reasoning behind the system's predictions and decisions, and they become even more critical when dealing with sensitive and personal health data. It is worth noting that XAI has not gathered the same attention across different research areas and data types, especially in healthcare. In particular, many clinical and remote health applications are based on tabular and time series data, respectively, and XAI is not commonly analysed on these data types, while computer vision and Natural Language Processing (NLP) are the reference applications. To provide an overview of XAI methods that are most suitable for tabular and time series data in the healthcare domain, this paper provides a review of the literature in the last 5 years, illustrating the type of generated explanations and the efforts provided to evaluate their relevance and quality. Specifically, we identify clinical validation, consistency assessment, objective and standardised quality evaluation, and human-centered quality assessment as key features to ensure effective explanations for the end users. Finally, we highlight the main research challenges in the field as well as the limitations of existing XAI methods.

"Black box" artificial intelligence (AI) systems are designed to automate decision-making, mapping a user's features into a class predicting individual behavioral traits such as credit risk, health status, and so on, without revealing why. This is problematic, not only because of the lack of transparency, but also because of potential biases inherited by algorithms from human prejudices or any hidden elements in the training data that may result in unfair or incorrect decisions. As AI continues to proliferate, there is an increasing need for technology companies to demonstrate the ability to trace back through the decision-making process, a functionality called explainable AI. This would essentially help them understand why a certain prediction or decision was made, what the important factors were in making that prediction or decision, and how confident the model is in that prediction or decision. To help instill user confidence that operational decisions are built on a foundation of fairness and transparency, Diveplane claims its products are designed around three principles: predict, explain and show.

Model explainability has become an important problem in machine learning (ML) due to the increased effect that algorithmic predictions have on humans. Explanations can help users understand not only why ML models make certain predictions, but also how these predictions can be changed. In this thesis, we examine the explainability of ML models from three vantage points: algorithms, users, and pedagogy, and contribute several novel solutions to the explainability problem.