The U.S Capitol is seen at sunrise. Imagine there is a group of people in Congress with more influence over whether laws are passed and rules are changed, than any official committee or subcommittee in the House and Senate. Now, imagine the members of this powerful group are not even members of Congress – in fact, they're not elected officials at all. Finally, imagine this group operates in secret, refuses to explain its decisions in detail to anyone, and has shown a consistent bias against free market principles. Unfortunately, you don't have to imagine this scenario.

Conversational agents, also known as chatbots, are versatile tools that have the potential of being used in dialogical argumentation. They could possibly be deployed in tasks such as persuasion for behaviour change (e.g. persuading people to eat more fruit, to take regular exercise, etc.) However, to achieve this, there is a need to develop methods for acquiring appropriate arguments and counterargument that reflect both sides of the discussion. For instance, to persuade someone to do regular exercise, the chatbot needs to know counterarguments that the user might have for not doing exercise. To address this need, we present methods for acquiring arguments and counterarguments, and importantly, meta-level information that can be useful for deciding when arguments can be used during an argumentation dialogue. We evaluate these methods in studies with participants and show how harnessing these methods in a chatbot can make it more persuasive.

We expect an increase in frequency and severity of cyber-attacks that comes along with the need of efficient security countermeasures. The process of attributing a cyber-attack helps in constructing efficient and targeted mitigative and preventive security measures. In this work, we propose an argumentation-based reasoner (ABR) that helps the analyst during the analysis of forensic evidence and the attribution process. Given the evidence collected from the cyber-attack, our reasoner helps the analyst to identify who performed the attack and suggests the analyst where to focus further analyses by giving hints of the missing evidence, or further investigation paths to follow. ABR is the first automatic reasoner that analyzes and attributes cyber-attacks by using technical and social evidence, as well as incomplete and conflicting information. ABR was tested on realistic cyber-attacks cases.

This paper presents a taxonomy of explainability in Human-Agent Systems. We consider fundamental questions about the Why, Who, What, When and How of explainability. First, we define explainability, and its relationship to the related terms of interpretability, transparency, explicitness, and faithfulness. These definitions allow us to answer why explainability is needed in the system, whom it is geared to and what explanations can be generated to meet this need. We then consider when the user should be presented with this information. Last, we consider how objective and subjective measures can be used to evaluate the entire system. This last question is the most encompassing as it will need to evaluate all other issues regarding explainability.

A counterfactual query is typically of the form 'For situation X, why was the outcome Y and not Z?'. A counterfactual explanation (or response to such a query) is of the form "If X was X*, then the outcome would have been Z rather than Y." In this work, we develop a technique to produce counterfactual visual explanations. Given a 'query' image $I$ for which a vision system predicts class $c$, a counterfactual visual explanation identifies how $I$ could change such that the system would output a different specified class $c'$. To do this, we select a 'distractor' image $I'$ that the system predicts as class $c'$ and identify spatial regions in $I$ and $I'$ such that replacing the identified region in $I$ with the identified region in $I'$ would push the system towards classifying $I$ as $c'$. We apply our approach to multiple image classification datasets generating qualitative results showcasing the interpretability and discriminativeness of our counterfactual explanations. To explore the effectiveness of our explanations in teaching humans, we present machine teaching experiments for the task of fine-grained bird classification. We find that users trained to distinguish bird species fare better when given access to counterfactual explanations in addition to training examples.

Explainable Artificial Intelligence (XAI) brings a lot of attention recently. Explainability is being presented as a remedy for lack of trust in model predictions. Model agnostic tools such as LIME, SHAP, or Break Down promise instance level interpretability for any complex machine learning model. But how certain are these explanations? Can we rely on additive explanations for non-additive models? In this paper, we examine the behavior of model explainers under the presence of interactions. We define two sources of uncertainty, model level uncertainty, and explanation level uncertainty. We show that adding interactions reduces explanation level uncertainty. We introduce a new method iBreakDown that generates non-additive explanations with local interaction.

Abstract Persuasion Argumentation (APA) is a dynamic argumentation formalism that extends Dung argumentation with persuasion relations. In this work, we show through two-counter Minsky machine encoding that APA dynamics is Turing-complete.

In this work, we formulate the process of generating explanations as model reconciliation for planning problems as one of planning with explanatory actions. We show that these problems could be better understood within the framework of epistemic planning and that, in fact, most earlier works on explanation as model reconciliation correspond to tractable subsets of epistemic planning problems. We empirically show how our approach is computationally more efficient than existing techniques for explanation generation and also discuss how this particular approach could be extended to capture most of the existing variants of explanation as model reconciliation. We end the paper with a discussion of how this formulation could be extended to generate novel explanatory behaviors.

As Artificial Intelligence (AI) becomes an integral part of our life, the development of explainable AI, embodied in the decision-making process of an AI or robotic agent, becomes imperative. For a robotic teammate, the ability to generate explanations to explain its behavior is one of the key requirements of an explainable agency. Prior work on explanation generation focuses on supporting the reasoning behind the robot's behavior. These approaches, however, fail to consider the cognitive effort needed to understand the received explanation. In particular, the human teammate is expected to understand any explanation provided before the task execution, no matter how much information is presented in the explanation. In this work, we argue that an explanation, especially complex ones, should be made in an online fashion during the execution, which helps to spread out the information to be explained and thus reducing the cognitive load of humans. However, a challenge here is that the different parts of an explanation are dependent on each other, which must be taken into account when generating online explanations. To this end, a general formulation of online explanation generation is presented. We base our explanation generation method in a model reconciliation setting introduced in our prior work. Our approach is evaluated both with human subjects in a standard planning competition (IPC) domain, using NASA Task Load Index (TLX), as well as in simulation with four different problems.

Artificial intelligence (AI) is a transformational $15 trillion opportunity. Today AI is becoming more sophisticated, decisions of machine or action thereby have a far-reaching impact on individual, society, or government. However, decision making is being performed by an algorithmic'black box'. Today's AL/ML models are mostly opaque, non-intuitive and difficult for people to understand and therefore suffer through key issues like trustworthiness, reliability, rationality, and transparency of models. To have confidence in the outcomes, cement stakeholder trust and ultimately capitalise on the opportunities, it is quite necessary to know the rationale of how the algorithm arrived at its recommendation or decision.