Developing a catalogue of explainability methods to support expert and non-expert users.

Organisations face growing legal requirements and ethical responsibilities to ensure that decisions made by their intelligent systems are explainable. However, provisioning of an explanation is often application dependent, causing an extended design phase and delayed deployment. In this paper we present an explainability framework formed of a catalogue of explanation methods, allowing integration to a range of projects within a telecommunications organisation. These methods are split into low-level explanations, high-level explanations and co-created explanations. We motivate and evaluate this framework using the specific case-study of explaining the conclusions of field engineering experts to non-technical planning staff. Feedback from an iterative co-creation process and a qualitative evaluation is indicative that this is a valuable development tool for use in future company projects

Metagenomics of the Svalbard Reindeer Rumen Microbiome Reveals Abundance of Polysaccharide Utilization Loci

Lignocellulosic biomass remains a largely untapped source of renewable energy predominantly due to its recalcitrance and an incomplete understanding of how this is overcome in nature. We present here a compositional and comparative analysis of metagenomic data pertaining to a natural biomass-converting ecosystem adapted to austere arctic nutritional conditions, namely the rumen microbiome of Svalbard reindeer (Rangifer tarandus platyrhynchus). Community analysis showed that deeply-branched cellulolytic lineages affiliated to the Bacteroidetes and Firmicutes are dominant, whilst sequence binning methods facilitated the assemblage of metagenomic sequence for a dominant and novel Bacteroidales clade (SRM-1). Analysis of unassembled metagenomic sequence as well as metabolic reconstruction of SRM-1 revealed the presence of multiple polysaccharide utilization loci-like systems (PULs) as well as members of more than 20 glycoside hydrolase and other carbohydrate-active enzyme families targeting various polysaccharides including cellulose, xylan and pectin. Functional screening of cloned metagenome fragments revealed high cellulolytic activity and an abundance of PULs that are rich in endoglucanases (GH5) but devoid of other common enzymes thought to be involved in cellulose degradation. Combining these results with known and partly re-evaluated metagenomic data strongly indicates that much like the human distal gut, the digestive system of herbivores harbours high numbers of deeply branched and as-yet uncultured members of the Bacteroidetes that depend on PUL-like systems for plant biomass degradation

A knowledge-intensive method for conversational cbr

Abstract. In conversational case-based reasoning (CCBR), a main problem is how to select the most discriminative questions and display them to users in a natural way to alleviate users ’ cognitive load. This is referred to as the question selection task. Current question selection methods are knowledge-poor, that is, only statistical metrics are taken into account. In this paper, we identify four computational tasks of a conversation process: feature inferencing, question ranking, consistent question clustering and coherent question sequencing. We show how general domain knowledge is able to improve these processes. A knowledge representation system suitable for capturing both cases and general knowledge has been extended with meta-level relations for controlling a CCBR process. An “explanation-boosted ” reasoning approach, designed to accomplish the knowledge-intensive question selection tasks, is presented. An application of our implemented system is illustrated in the car fault detection domain.

Great Explanations: Opinionated Explanations for Recommendation

Case-based Reasoning Research and Development: 23rd International Conference, ICCBR 2015, Frankfurt am Main, Germany 28-30 September 2015Explaining recommendations helps users to make better, more satisfying decisions. We describe a novel approach to explanation for recommender systems, one that drives the recommendation process, while at the same time providing the user with useful insights into the reason why items have been chosen and the trade-os they may need to consider when making their choice. We describe this approach in the context ofa case-based recommender system that harnesses opinions mined from user-generated reviews, and evaluate it on TripAdvisor Hotel data