Weighted bipolar argumentation frameworks allow modeling decision problems and online discussions by defining arguments and their relationships. The strength of arguments can be computed based on an initial weight and the strength of attacking and supporting arguments. While previous approaches assumed an acyclic argumentation graph and successively set arguments' strength based on the strength of their parents, recently continuous dynamical systems have been proposed as an alternative. Continuous models update arguments' strength simultaneously and continuously. While there are currently no analytical guarantees for convergence in general graphs, experiments show that continuous models can converge quickly in large cyclic graphs with thousands of arguments. Here, we focus on the high-level ideas of this approach and explain key results and applications. We also introduce Attractor, a Java library that can be used to solve weighted bipolar argumentation problems. Attractor contains implementations of several discrete and continuous models and numerical algorithms to compute solutions. It also provides base classes that can be used to implement, to evaluate and to compare continuous models easily.

We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment. Rather than present a black box model and explain it afterwards, we provide a globally interpretable model that is as accurate as other neural networks. Our "two-layer additive risk model" is decomposable into subscales, where each node in the second layer represents a meaningful subscale, and all of the nonlinearities are transparent. We provide three types of explanations that are simpler than, but consistent with, the global model. One of these explanation methods involves solving a minimum set cover problem to find high-support globally-consistent explanations. We present a new online visualization tool to allow users to explore the global model and its explanations.

Probabilistic argumentation allows reasoning about argumentation problems in a way that is well-founded by probability theory. However, in practice, this approach can be severely limited by the fact that probabilities are defined by adding an exponential number of terms. We show that this exponential blowup can be avoided in an interesting fragment of epistemic probabilistic argumentation and that some computational problems that have been considered intractable can be solved in polynomial time. We give efficient convex programming formulations for these problems and explore how far our fragment can be extended without loosing tractability.

In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics. When asking for projected counts we are interested in counting the number of extensions of a given argumentation framework while multiple extensions that are identical when restricted to the projected arguments count as only one projected extension. We establish classical complexity results and parameterized complexity results when the problems are parameterized by treewidth of the undirected argumentation graph. To obtain upper bounds for counting projected extensions, we introduce novel algorithms that exploit small treewidth of the undirected argumentation graph of the input instance by dynamic programming (DP). Our algorithms run in time double or triple exponential in the treewidth depending on the considered semantics. Finally, we take the exponential time hypothesis (ETH) into account and establish lower bounds of bounded treewidth algorithms for counting extensions and projected extension.

Argumentation-based dialogue systems, which can handle and exchange arguments through dialogue, have been widely researched. It is required that these systems have sufficient supporting information to argue their claims rationally; however, the systems often do not have enough of such information in realistic situations. One way to fill in the gap is acquiring such missing information from dialogue partners (information-seeking dialogue). Existing information-seeking dialogue systems are based on handcrafted dialogue strategies that exhaustively examine missing information. However, the proposed strategies are not specialized in collecting information for constructing rational arguments. Moreover, the number of system's inquiry candidates grows in accordance with the size of the argument set that the system deal with. In this paper, we formalize the process of information-seeking dialogue as Markov decision processes (MDPs) and apply deep reinforcement learning (DRL) for automatically optimizing a dialogue strategy. By utilizing DRL, our dialogue strategy can successfully minimize objective functions, the number of turns it takes for our system to collect necessary information in a dialogue. We conducted dialogue experiments using two datasets from different domains of argumentative dialogue. Experimental results show that the proposed formalization based on MDP works well, and the policy optimized by DRL outperformed existing heuristic dialogue strategies.

Explainable AI (XAI) is getting a lot of attention these days, but it won't deliver the basis for trust you're hoping for. Before we get caught up in the hype, let's examine the wisdom of a sentence I've been hearing a lot lately: "in order to trust AI, we need it to be able to explain how it made its decisions." There are two ways that we can communicate what we want when programming a computer, we can either give explicit instructions -- handcrafted code -- where we tell the computer how to convert the input into the output… or we can say, "Here, look at a bunch of examples and figure it out." That second one is the essence of artificial intelligence: explaining our wishes with data rather than instructions.

THE HAGUE, NETHERLANDS – The global chemical weapons watchdog will in February begin to assign blame for attacks with banned munitions in Syria's war, using new powers approved by member states but opposed by Damascus and its key allies Russia and Iran. The agency was handed the new task in response to an upsurge in the use of chemical weapons in recent years, notably in the Syrian conflict, where scores of attacks with sarin and chlorine have been carried out by Syrian forces and rebel groups, according to a joint United Nations-OPCW investigation. A core team of 10 experts charged with apportioning blame for poison gas attacks in Syria will be hired soon, Fernando Arias, the new head of the Organisation for the Prohibition of Chemical Weapons (OPCW), told the Foreign Press Association of the Netherlands on Tuesday. The Syria team will be able to look into all attacks previously investigated by the OPCW, dating back to 2014. The OPCW was granted additional powers to identify individuals and institutions responsible for attacks by its 193 member states at a special session in June.

How much can anyone trust a recommendation from an AI? Yaroslav Kuflinski, from Iflexion gives an explanation of explainable AI She is lying sedated on a gurney that's bumping towards the operating theater. It squeaks to a halt and a hurried member of hospital staff thrusts a form at you to sign. It describes the urgent surgical procedure your child is about to undergo--and it requires your signature if the operation is to go ahead. At this specific moment, do you think you are owed a reasonable, plain-English explanation of all the inscrutable decisions that an AI has lately been making on your daughter's behalf? in short, do we need explainable AI? There are many other examples where one or more of the actors may consider themselves entitled to an explanation of the reasoning processes behind the decisions of an AI.

We predict credit applications with off-the-shelf, interchangeable black-box classifiers and we explain single predictions with counterfactual explanations. Counterfactual explanations expose the minimal changes required on the input data to obtain a different result e.g., approved vs rejected application. Despite their effectiveness, counterfactuals are mainly designed for changing an undesired outcome of a prediction i.e. loan rejected. Counterfactuals, however, can be difficult to interpret, especially when a high number of features are involved in the explanation. Our contribution is two-fold: i) we propose positive counterfactuals, i.e. we adapt counterfactual explanations to also explain accepted loan applications, and ii) we propose two weighting strategies to generate more interpretable counterfactuals. Experiments on the HELOC loan applications dataset show that our contribution outperforms the baseline counterfactual generation strategy, by leading to smaller and hence more interpretable counterfactuals.

Mathematical optimization offers highly-effective tools for finding solutions for problems with well-defined goals, notably scheduling. However, optimization solvers are often unexplainable black boxes whose solutions are inaccessible to users and which users cannot interact with. We define a novel paradigm using argumentation to empower the interaction between optimization solvers and users, supported by tractable explanations which certify or refute solutions. A solution can be from a solver or of interest to a user (in the context of 'what-if' scenarios). Specifically, we define argumentative and natural language explanations for why a schedule is (not) feasible, (not) efficient or (not) satisfying fixed user decisions, based on models of the fundamental makespan scheduling problem in terms of abstract argumentation frameworks (AFs). We define three types of AFs, whose stable extensions are in one-to-one correspondence with schedules that are feasible, efficient and satisfying fixed decisions, respectively. We extract the argumentative explanations from these AFs and the natural language explanations from the argumentative ones.