Pml 2: A modular explanation interlingua

… of the American Association for Artificial …, 2003

2008

The Inference Web infrastructure for web explanations together with its underlying Proof Markup Language (PML) for encoding justification and provenance information has been used in multiple projects varying from explaining the behavior of cognitive agents to explaining how knowledge is extracted from multiple sources of information in natural language.

Abstract In this position-paper, we have outlined our initial explorations into provisioning for Spoken Natural Language Explanations in Inference Web (IW). We present our motivation for considering speech as an effective modality for presenting explanations, in terms of the interesting usecases that we believe are enabled by the addition of this modality. We then describe the design and implementation of our prototype system. We highlight the salient features of our approach using an illustrative running example.

in Proc. Conf. ALGORITMY'97, …, 2004

28th International Conference on Intelligent User Interfaces

The right to obtain an explanation of the decision reached by an Artificial Intelligence (AI) model is now an EU regulation. Different stakeholders of an AI system (e.g. managers, developers, auditors, etc.) may have different background knowledge, competencies and goals, thus requiring different kinds of interpretations and explanations. Fortunately, there is a growing armoury of tools to interpret ML models and explain their predictions, recommendations and diagnoses which we will refer to collectively as explanation strategies. As these explanation strategies mature, practitioners will gain experience that helps them know which strategies to deploy in different circumstances. What is lacking, and is addressed by iSee, is capturing, sharing and re-using explanation strategies based on past positive experiences. The goal of the iSee platform is to improve every user's experience of AI, by harnessing experiences and best practices in Explainable AI. CCS CONCEPTS • Human-centered computing → Natural language interfaces; • Computing methodologies → Knowledge representation and reasoning.

2012

Semantic Web applications use interconnected distributed data and inferential capabilities to compute their results. The users of Semantic Web applications might find it difficult to understand how a result is produced or how a new piece of information is derived in the process. Explanation enables users to understand the process of obtaining results. Explanation adds transparency to the process of obtaining results and enables user trust on the process. The concept of providing explanation has been first introduced in expert systems and later studied in different application areas. This paper provides a brief review of existing research on explanation in the Semantic Web.

2008

Abstract If users are expected to depend on answers from applications and services on the web, they need to have methods for asking for information that will increase their understanding and their trust in the answers. One way of increasing a user's understanding and trust is to provide a summarized or detailed description of how the answer was obtained.

2020

Interest in the field of Explainable Artificial Intelligence has been growing for decades and has accelerated recently. As Artificial Intelligence models have become more complex, and often more opaque, with the incorporation of complex machine learning techniques, explainability has become more critical. Recently, researchers have been investigating and tackling explainability with a user-centric focus, looking for explanations to consider trustworthiness, comprehensibility, explicit provenance, and context-awareness. In this chapter, we leverage our survey of explanation literature in Artificial Intelligence and closely related fields and use these past efforts to generate a set of explanation types that we feel reflect the expanded needs of explanation for today's artificial intelligence applications. We define each type and provide an example question that would motivate the need for this style of explanation. We believe this set of explanation types will help future system ...

Relation, 2008

If users are expected to depend on answers from applications and services on the web, they need to have methods for asking for information that will increase their understanding and their trust in the answers. One way of increasing a user's understanding and trust is to provide a summarized or detailed description of how the answer was obtained. This process would typically depend on some kind of proof structure defending the answer but this alone (no matter how understandable and/or thorough the proof is) is not ...

Semantic Web

In the past decade, trustworthy Artificial Intelligence (AI) has emerged as a focus for the AI community to ensure better adoption of AI models, and explainable AI is a cornerstone in this area. Over the years, the focus has shifted from building transparent AI methods to making recommendations on how to make black-box or opaque machine learning models and their results more understandable by experts and non-expert users. In our previous work, to address the goal of supporting user-centered explanations that make model recommendations more explainable, we developed an Explanation Ontology (EO). The EO is a general-purpose representation that was designed to help system designers connect explanations to their underlying data and knowledge. This paper addresses the apparent need for improved interoperability to support a wider range of use cases. We expand the EO, mainly in the system attributes contributing to explanations, by introducing new classes and properties to support a broad...