No. 1 – Detecting abnormal travel expenses Most existing systems for reporting travel expenses apply pre-defined views, such as time period, service or employee group. While these systems aim to detect abnormal expenses systematically, they usually fail to explain why the claims singled out are judged to be abnormal. To address this lack of visibility into the context of abnormal travel expense claims, Accenture Labs designed and built a travel expenses system incorporating Explainable AI. By combining knowledge graph and machine learning technologies, the system delivers insight to explain any abnormal claims in real-time. No. 2 – Project risk management Most large companies manage hundreds, if not thousands, of projects every year across multiple vendors, clients and partners.

Explanation in machine learning and related fields such as artificial intelligence aims at making machine learning models and their decisions understandable to humans. Existing work suggests that personalizing explanations might help to improve understandability. In this work, we derive a conceptualization of personalized explanation by defining and structuring the problem based on prior work on machine learning explanation, personalization (in machine learning) and concepts and techniques from other domains such as privacy and knowledge elicitation. We perform a categorization of explainee information used in the process of personalization as well as describing means to collect this information. We also identify three key explanation properties that are amendable to personalization: complexity, decision information and presentation. We also enhance existing work on explanation by introducing additional desiderata and measures to quantify the quality of personalized explanations.

We find ourselves surrounded by a rapidly increasing number of autonomous and semi-autonomous systems. Two grand challenges arise from this development: Machine Ethics and Machine Explainability. Machine Ethics, on the one hand, is concerned with behavioral constraints for systems, so that morally acceptable, restricted behavior results; Machine Explainability, on the other hand, enables systems to explain their actions and argue for their decisions, so that human users can understand and justifiably trust them. In this paper, we try to motivate and work towards a framework combining Machine Ethics and Machine Explainability. Starting from a toy example, we detect various desiderata of such a framework and argue why they should and how they could be incorporated in autonomous systems. Our main idea is to apply a framework of formal argumentation theory both, for decision-making under ethical constraints and for the task of generating useful explanations given only limited knowledge of the world. The result of our deliberations can be described as a first version of an ethically motivated, principle-governed framework combining Machine Ethics and Machine Explainability

Geoffrey Hinton dismissed the need for explainable AI. A range of experts have explained why he is wrong. I actually tend to agree with Geoff. Explainable AI is overrated and hyped by the media. A whole industry has sprung up with a business model of scaring everyone about AI being not explainable. And they use words like discrimination which create a sense of shock-horror.

Yes, it is Holy Grail of AI and for the right reason; whether it about losing a High-Value customer due to wrong Churn Prediction or losing dollars due to incorrect classification of a financial transaction. In reality, Customers are the less bothered accuracy of AI model, but their concerns are about Cluelessness of Data Scientist to explain "How do I trust its decision making?" Data Scientists building credit risk models in the consumer space faced the transparency requirement probably for as long as this field existed, due to regulatory compliance which governs the consumer risk. Marketers also have been bound by certain rules which disallow protected categories such as gender or race to enter the models. These regulations were created in US to protect consumers.

The Aristotelian trichotomy distinguishes three aspects of argumentation: Logos, Ethos, and Pathos. Even rich argumentation representations like the Argument Interchange Format (AIF) are only concerned with capturing the Logos aspect. Inference Anchoring Theory (IAT) adds the possibility to represent ethical requirements on the illocutionary force edges linking locutions to illocutions, thereby allowing to capture some aspects of ethos. With the recent extensions AIF+ and Social Argument Interchange Format (S-AIF), which embed dialogue and speakers into the AIF argumentation representation, the basis for representing all three aspects identified by Aristotle was formed. In the present work, we develop the Trichotomic Argument Interchange Format (T-AIF), building on the idea from S-AIF of adding the speakers to the argumentation graph. We capture Logos in the usual known from AIF+, Ethos in form of weighted edges between actors representing trust, and Pathos via weighted edges from actors to illocutions representing their level of commitment to the propositions. This extended structured argumentation representation opens up new possibilities of defining semantic properties on this rich graph in order to characterize and profile the reasoning patterns of the participating actors.

One of NIST's research areas has been the quantification of Each team's system is faced with challenges such as The goal of ARIAC is to solidify the shown in figure 1. The organizers chose kitting field of robot agility, while also progressing the state because of its similarity to assembly. Teams were tasked with assembling a robotic system's (robot, controller, and sensors) ability kit both from bins of stationary parts and from a to respond to a dynamic environment. After the robotic system finished dynamic response includes handling errors like the kit, the kit was placed on an autonomous guided dropped parts or responding to changes in orders, all vehicle (AGV) and taken away. Teams were faced with such challenges as forced The competition addresses the aspect of robot dropped parts and in-process order changes.

As artificial intelligence (AI) systems become increasingly complex and ubiquitous, these systems will be responsible for making decisions that directly affect individuals and society as a whole. Such decisions will need to be justified due to ethical concerns as well as trust, but achieving this has become difficult due to the `black-box' nature many AI models have adopted. Explainable AI (XAI) can potentially address this problem by explaining its actions, decisions and behaviours of the system to users. However, much research in XAI is done in a vacuum using only the researchers' intuition of what constitutes a `good' explanation while ignoring the interaction and the human aspect. This workshop invites researchers in the HCI community and related fields to have a discourse about human-centred approaches to XAI rooted in interaction and to shed light and spark discussion on interaction design challenges in XAI.

For an autonomous system, the ability to justify and explain its decision making is crucial to improve its transparency and trustworthiness. This paper proposes an argumentation-based approach to represent, justify and explain the decision making of a value driven agent (VDA). By using a newly defined formal language, some implicit knowledge of a VDA is made explicit. The selection of an action in each situation is justified by constructing and comparing arguments supporting different actions. In terms of a constructed argumentation framework and its extensions, the reasons for explaining an action are defined in terms of the arguments for or against the action, by exploiting their defeat relation, as well as their premises and conclusions.

The question addressed in this paper is: If we present to a user an AI system that explains how it works, how do we know whether the explanation works and the user has achieved a pragmatic understanding of the AI? In other words, how do we know that an explanainable AI system (XAI) is any good? Our focus is on the key concepts of measurement. We discuss specific methods for evaluating: (1) the goodness of explanations, (2) whether users are satisfied by explanations, (3) how well users understand the AI systems, (4) how curiosity motivates the search for explanations, (5) whether the user's trust and reliance on the AI are appropriate, and finally, (6) how the human-XAI work system performs. The recommendations we present derive from our integration of extensive research literatures and our own psychometric evaluations.