We introduce SMProbLog, a generalization of the probabilistic logic programming language ProbLog. A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs to a randomly sampled program, and these probabilities are mutually independent. The semantics of ProbLog is given by the success probability of a query, which corresponds to the probability that the query succeeds in a randomly sampled program. It is well-defined when each random sample uniquely determines the truth values of all logical atoms. Argumentation problems, however, represent an interesting practical application where this is not always the case. SMProbLog generalizes the semantics of ProbLog to the setting where multiple truth assignments are possible for a randomly sampled program, and implements the corresponding algorithms for both inference and learning tasks. We then show how this novel framework can be used to reason about probabilistic argumentation problems. Therefore, the key contribution of this paper are: a more general semantics for ProbLog programs, its implementation into a probabilistic programming framework for both inference and parameter learning, and a novel approach to probabilistic argumentation problems based on such framework.

Machine learning (ML)-powered AI systems are already affecting every aspect of our lives, from banking, healthcare, industry, and transportation to communications, entertainment, and gaming. The rush to embrace artificial intelligence is at an all-time high. More firms are processing large amounts of data, building models using cutting-edge algorithms, and deploying them into products. We don't often spend the same rigor verifying and validating these models as we do in traditional Software deployment since we're so focused on time-to-market. We will have a team of quality assurance engineers test the system on different combinations of data before putting it in production to ensure it achieves expected outcomes and does not produce undesired results.

For Many People, artificial Intelligence is Something They Only See In Science Fiction Movies, But It's Grown Much More Accessible In Recent Years, Especially In Business. With Technology Evolving Rapidly, ai can Be Utilized In Various Ways To Improve Your Workday. When It Comes To Aiding ... Do You Utilize Social Media Platforms Like Instagram, Facebook, Twitter, And Others To Build An Audience, Generate Interaction With Your Business And Goods, And Convert Leads And Sales? ... Few Topics In Science And Technology Have Sparked As Much Interest As Artificial Intelligence, Which Has The Potential To Revolutionize All Areas Of Our Life, According To Some Of The World's Greatest Thinkers. Artificial Intelligence (ai) Can Have A Significant Influence On Organizations And Consumers All ... Conversational Artificial Intelligence (ai) Is The Technology Underlying Automated Messaging Designed To Mimic Human Interactions, And It Is Frequently Used To Initiate Consumers' Online Conversations With Companies. The Software-as-a-service (saas) Business Is Exploding At An Unprecedented Rate.

In this paper, we present and implement a multi-dimensional, modular framework for performing deep argument analysis (DeepA2) using current pre-trained language models (PTLMs). ArgumentAnalyst -- a T5 model (Raffel et al. 2020) set up and trained within DeepA2 -- reconstructs argumentative texts, which advance an informal argumentation, as valid arguments: It inserts, e.g., missing premises and conclusions, formalizes inferences, and coherently links the logical reconstruction to the source text. We create a synthetic corpus for deep argument analysis, and evaluate ArgumentAnalyst on this new dataset as well as on existing data, specifically EntailmentBank (Dalvi et al. 2021). Our empirical findings vindicate the overall framework and highlight the advantages of a modular design, in particular its ability to emulate established heuristics (such as hermeneutic cycles), to explore the model's uncertainty, to cope with the plurality of correct solutions (underdetermination), and to exploit higher-order evidence.

We propose a new method for generating explanations with Artificial Intelligence (AI) and a tool to test its expressive power within a user interface. In order to bridge the gap between philosophy and human-computer interfaces, we show a new approach for the generation of interactive explanations based on a sophisticated pipeline of AI algorithms for structuring natural language documents into knowledge graphs, answering questions effectively and satisfactorily. With this work we aim to prove that the philosophical theory of explanations presented by Achinstein can be actually adapted for being implemented into a concrete software application, as an interactive and illocutionary process of answering questions. Specifically, our contribution is an approach to frame illocution in a computer-friendly way, to achieve user-centrality with statistical question answering. In fact, we frame illocution, in an explanatory process, as that mechanism responsible for anticipating the needs of the explainee in the form of unposed, implicit, archetypal questions, hence improving the user-centrality of the underlying explanatory process. More precisely, we hypothesise that given an arbitrary explanatory process, increasing its goal-orientedness and degree of illocution results in the generation of more usable (as per ISO 9241-210) explanations. We tested our hypotheses with a user-study involving more than 60 participants, on two XAI-based systems, one for credit approval (finance) and one for heart disease prediction (healthcare). The results showed that our proposed solution produced a statistically significant improvement (hence with a p-value lower than 0.05) on effectiveness. This, combined with a visible alignment between the increments in effectiveness and satisfaction, suggests that our understanding of illocution can be correct, giving evidence in favour of our theory.

The European Union (EU) through the High-Level Expert Group on Artificial Intelligence (AI-HLEG) and the General Data Protection Regulation (GDPR) has recently posed an interesting challenge to the eXplainable AI (XAI) community, by demanding a more user-centred approach to explain Automated Decision-Making systems (ADMs). Looking at the relevant literature, XAI is currently focused on producing explainable software and explanations that generally follow an approach we could term One-Size-Fits-All, that is unable to meet a requirement of centring on user needs. One of the causes of this limit is the belief that making things explainable alone is enough to have pragmatic explanations. Thus, insisting on a clear separation between explainabilty (something that can be explained) and explanations, we point to explanatorY AI (YAI) as an alternative and more powerful approach to win the AI-HLEG challenge. YAI builds over XAI with the goal to collect and organize explainable information, articulating it into something we called user-centred explanatory discourses. Through the use of explanatory discourses/narratives we represent the problem of generating explanations for Automated Decision-Making systems (ADMs) into the identification of an appropriate path over an explanatory space, allowing explainees to interactively explore it and produce the explanation best suited to their needs.

Provide insights into AI-Human communication. Define levels of explanation with identified techniques that align with AI cognitive processes. Over the last few years there has been rapid research growth into eXplainable Artificial Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML). Drivers for this growth include recent legislative changes and increased investments by industry and governments, along with increased concern from the general public. People are affected by autonomous decisions every day and the public need to understand the decision-making process to accept the outcomes. However, the vast majority of the applications of XAI/IML are focused on providing low-level ‘narrow’ explanations of how an individual decision was reached based on a particular datum.

It is well-known that artificial intelligence is reining the throne of cutting-edge technologies in multiple organizations and industries for the last few years. Every organization is instigated to leverage the smart functionalities of AI models to gain a competitive edge in the tech-driven market. But one has to keep Explainable AI or XAI in mind before completing the process of leveraging artificial intelligence in existing systems. Let us explore some of the top characteristics of Explainable AI that are important for organizations to know. At first, organizations need to have sufficient knowledge of Explainable AI before diving into the characteristics to leverage.

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 section highlights the impact of the AIX360 toolkit in the first two years since its release. It describes several different forms of impact on real problem domains and the open source community. This impact has resulted in improvements in multiple metrics: accuracy, semiconductor yield, satisfaction rate, and domain expert time. The current version of the AIX360 toolkit includes ten explainability algorithms described in Table 1 covering different ways of explaining. Explanation methods could be either local or global, where the former refers to explaining an AI model's decision for a single instance, while the latter refers to explaining a model in its entirety.