MatrixX is a solver for Abstract Argumentation Frameworks. Offensive and defensive properties of an Argumentation Framework are notated in a matrix style. Rows and columns of this matrix are systematically reduced by the solver. This procedure is implemented through the use of hash maps in order to accelerate calculation time. MatrixX works for stable and complete semantics and was designed for the ICCMA 2021 competition.

Human environments are often regulated by explicit and complex rulesets. Integrating Reinforcement Learning (RL) agents into such environments motivates the development of learning mechanisms that perform well in rule-dense and exception-ridden environments such as autonomous driving on regulated roads. In this paper, we propose a method for organising experience by means of partitioning the experience buffer into clusters labelled on a per-explanation basis. We present discrete and continuous navigation environments compatible with modular rulesets and 9 learning tasks. For environments with explainable rulesets, we convert rule-based explanations into case-based explanations by allocating state-transitions into clusters labelled with explanations. This allows us to sample experiences in a curricular and task-oriented manner, focusing on the rarity, importance, and meaning of events. We label this concept Explanation-Awareness (XA). We perform XA experience replay (XAER) with intra and inter-cluster prioritisation, and introduce XA-compatible versions of DQN, TD3, and SAC. Performance is consistently superior with XA versions of those algorithms, compared to traditional Prioritised Experience Replay baselines, indicating that explanation engineering can be used in lieu of reward engineering for environments with explainable features.

The novel severe acute respiratory syndrome coronavirus type-2 (SARS-CoV-2) caused a global pandemic that has taken more than 4.5 million lives and severely affected the global economy. To curb the spread of the virus, an accurate, cost-effective, and quick testing for large populations is exceedingly important in order to identify, isolate, and treat infected people. Current testing methods commonly use PCR (Polymerase Chain Reaction) based equipment that have limitations on throughput, cost-effectiveness, and simplicity of procedure which creates a compelling need for developing additional coronavirus disease-2019 (COVID-19) testing mechanisms, that are highly sensitive, rapid, trustworthy, and convenient to use by the public. We propose a COVID-19 testing method using artificial intelligence (AI) techniques on MALDI-ToF (matrix-assisted laser desorption/ionization time-of-flight) data extracted from 152 human gargle samples (60 COVID-19 positive tests and 92 COVID-19 negative tests). Our AI-based approach leverages explainable-AI (X-AI) methods to explain the decision rules behind the predictive algorithm both on a local (per-sample) and global (all-samples) basis to make the AI model more trustworthy. Finally, we evaluated our proposed method using a 70%-30% train-test-split strategy and achieved a training accuracy of 86.79% and a testing accuracy of 91.30%.

Enabling humans to identify potential flaws in an agent's decision making is an important Explainable AI application. We consider identifying such flaws in a planning-based deep reinforcement learning (RL) agent for a complex real-time strategy game. In particular, the agent makes decisions via tree search using a learned model and evaluation function over interpretable states and actions. This gives the potential for humans to identify flaws at the level of reasoning steps in the tree, even if the entire reasoning process is too complex to understand. However, it is unclear whether humans will be able to identify such flaws due to the size and complexity of trees. We describe a user interface and case study, where a small group of AI experts and developers attempt to identify reasoning flaws due to inaccurate agent learning. Overall, the interface allowed the group to identify a number of significant flaws of varying types, demonstrating the promise of this approach.

While recent advances in AI-based automated decision-making have shown many benefits for businesses and society, they also come at a cost. It has for long been known that a high level of automation of decisions can lead to various drawbacks, such as automation bias and deskilling. In particular, the deskilling of knowledge workers is a major issue, as they are the same people who should also train, challenge and evolve AI. To address this issue, we conceptualize a new class of DSS, namely Intelligent Decision Assistance (IDA) based on a literature review of two different research streams -- DSS and automation. IDA supports knowledge workers without influencing them through automated decision-making. Specifically, we propose to use techniques of Explainable AI (XAI) while withholding concrete AI recommendations. To test this conceptualization, we develop hypotheses on the impacts of IDA and provide first evidence for their validity based on empirical studies in the literature.

State of the art Artificial Intelligence (AI) techniques have reached an impressive complexity. Consequently, researchers are discovering more and more methods to use them in real-world applications. However, the complexity of such systems requires the introduction of methods that make those transparent to the human user. The AI community is trying to overcome the problem by introducing the Explainable AI (XAI) field, which is tentative to make AI algorithms less opaque. However, in recent years, it became clearer that XAI is much more than a computer science problem: since it is about communication, XAI is also a Human-Agent Interaction problem. Moreover, AI came out of the laboratories to be used in real life. This implies the need for XAI solutions tailored to non-expert users. Hence, we propose a user-centred framework for XAI that focuses on its social-interactive aspect taking inspiration from cognitive and social sciences' theories and findings. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.

To make Explainable AI (XAI) systems trustworthy, understanding harmful effects is just as important as producing well-designed explanations. In this paper, we address an important yet unarticulated type of negative effect in XAI. We introduce explainability pitfalls (EPs), unanticipated negative downstream effects from AI explanations manifesting even when there is no intention to manipulate users. EPs are different from, yet related to, dark patterns, which are intentionally deceptive practices. We articulate the concept of EPs by demarcating it from dark patterns and highlighting the challenges arising from uncertainties around pitfalls. We situate and operationalize the concept using a case study that showcases how, despite best intentions, unsuspecting negative effects such as unwarranted trust in numerical explanations can emerge. We propose proactive and preventative strategies to address EPs at three interconnected levels: research, design, and organizational.

It is interesting to decipher the working of Machine Learning through a web-based dashboard. Imagine gaining access to the interactive plots displaying information on model performance, feature importance as well as What-if analysis. What is exciting is that one does not need any web development expertise to build such an informative dashboard but simple few lines of python code are sufficient to generate a stunningly interactive Machine Learning Dashboard. This is possible by using a library called'Explainer Dashboard'. The ExplainerDashboard is a python package which generates interactive dashboards which allow users to understand as well as explain how the model works and how it is deciding the outcome.

During a research project in which we developed a machine learning (ML) driven visualization system for non-ML experts, we reflected on interpretability research in ML, computer-supported collaborative work and human-computer interaction. We found that while there are manifold technical approaches, these often focus on ML experts and are evaluated in decontextualized empirical studies. We hypothesized that participatory design research may support the understanding of stakeholders' situated sense-making in our project, yet, found guidance regarding ML interpretability inexhaustive. Building on philosophy of technology, we formulated explanation strategies as an empirical-analytical lens explicating how technical explanations mediate the contextual preferences concerning people's interpretations. In this paper, we contribute a report of our proof-of-concept use of explanation strategies to analyze a co-design workshop with non-ML experts, methodological implications for participatory design research, design implications for explanations for non-ML experts and suggest further investigation of technological mediation theories in the ML interpretability space.

Feedback can help crowdworkers to improve their ideations. However, current feedback methods require human assessment from facilitators or peers. This is not scalable to large crowds. We propose Interpretable Directed Diversity to automatically predict ideation quality and diversity scores, and provide AI explanations - Attribution, Contrastive Attribution, and Counterfactual Suggestions - for deeper feedback on why ideations were scored (low), and how to get higher scores. These explanations provide multi-faceted feedback as users iteratively improve their ideation. We conducted think aloud and controlled user studies to understand how various explanations are used, and evaluated whether explanations improve ideation diversity and quality. Users appreciated that explanation feedback helped focus their efforts and provided directions for improvement. This resulted in explanations improving diversity compared to no feedback or feedback with predictions only. Hence, our approach opens opportunities for explainable AI towards scalable and rich feedback for iterative crowd ideation.