Research Papers

iSee: Advancing Multi-Shot Explainable AI Using Case-based Recommendations

Anjana Wijekoon, Nirmalie Wiratunga, David Corsar +7

Explainable AI (XAI) can greatly enhance user trust and satisfaction in AI-assisted decision-making processes. Recent findings suggest that a single explainer may not meet the diverse needs of multiple users in an AI system; indeed, even individual users may require multiple explanations. This highlights the necessity for a "multi-shot" approach, employing a combination of explainers to form what we introduce as an "explanation strategy". Tailored to a specific user or a user group, an "explanation experience" describes interactions with personalised strategies designed to enhance their AI decision-making processes. The iSee platform is designed for the intelligent sharing and reuse of explanation experiences, using Case-based Reasoning to advance best practices in XAI. The platform provides tools that enable AI system designers, i.e. design users, to design and iteratively revise the most suitable explanation strategy for their AI system to satisfy end-user needs. All knowledge generated within the iSee platform is formalised by the iSee ontology for interoperability. We use a summative mixed methods study protocol to evaluate the usability and utility of the iSee platform with six design users across varying levels of AI and XAI expertise. Our findings confirm that the iSee platform effectively generalises across applications and its potential to promote the adoption of XAI best practices.

The inherent "black box" nature of deep neural networks (DNNs) compromises their transparency and reliability. Recently, explainable AI (XAI) has garnered increasing attention from researchers. Several perturbation-based interpretations have emerged. However, these methods often fail to adequately consider feature dependencies. To solve this problem, we introduce a perturbation-based interpretation guided by feature coalitions, which leverages deep information of network to extract correlated features. Then, we proposed a carefully-designed consistency loss to guide network interpretation. Both quantitative and qualitative experiments are conducted to validate the effectiveness of our proposed method. Code is available at github.com/Teriri1999/Perturebation-on-Feature-Coalition.

In the realm of human activity recognition (HAR), the integration of explainable Artificial Intelligence (XAI) emerges as a critical necessity to elucidate the decision-making processes of complex models, fostering transparency and trust. Traditional explanatory methods like Class Activation Mapping (CAM) and attention mechanisms, although effective in highlighting regions vital for decisions in various contexts, prove inadequate for HAR. This inadequacy stems from the inherently abstract nature of HAR data, rendering these explanations obscure. In contrast, state-of-th-art post-hoc interpretation techniques for time series can explain the model from other perspectives. However, this requires extra effort. It usually takes 10 to 20 seconds to generate an explanation. To overcome these challenges, we proposes a novel, model-agnostic framework that enhances both the interpretability and efficacy of HAR models through the strategic use of competitive data augmentation. This innovative approach does not rely on any particular model architecture, thereby broadening its applicability across various HAR models. By implementing competitive data augmentation, our framework provides intuitive and accessible explanations of model decisions, thereby significantly advancing the interpretability of HAR systems without compromising on performance.

Bringing more transparency to the judicial system for the purposes of increasing accountability often demands extensive effort from auditors who must meticulously sift through numerous disorganized legal case files to detect patterns of bias and errors. For example, the high-profile investigation into the Curtis Flowers case took seven reporters a full year to assemble evidence about the prosecutor's history of selecting racially biased juries. LLMs have the potential to automate and scale these transparency pipelines, especially given their demonstrated capabilities to extract information from unstructured documents. We discuss the opportunities and challenges of using LLMs to provide transparency in two important court processes: jury selection in criminal trials and housing eviction cases.

This pictorial aims to critically consider the nature of text-to-audio and text-to-music generative tools in the context of explainable AI. As a group of experimental musicians and researchers, we are enthusiastic about the creative potential of these tools and have sought to understand and evaluate them from perspectives of prompt creation, control, usability, understandability, explainability of the AI process, and overall aesthetic effectiveness of the results. One of the challenges we have identified that is not explicitly addressed by these tools is the inherent semantic gap in using text-based tools to describe something as abstract as music. Other gaps include explainability vs. useability, and user control and input vs. the human creative process. The aim of this pictorial is to raise questions for discussion and make a few general suggestions on the kinds of improvements we would like to see in generative AI music tools.

Complex sequential decision-making planning problems, covering infinite states' space have been shown to be solvable by AlphaZero type of algorithms. Such an approach that trains a neural model while simulating projection of futures with a Monte Carlo Tree Search algorithm were shown to be applicable to real life planning problems. As such, engineers and users interacting with the resulting policy of behavior might benefit from obtaining automated explanations about these planners' decisions offline or online. This paper focuses on the information within the Monte Carlo Tree Search data structure. Given its construction, this information contains much of the reasoning of the sequential decision-making algorithm and is essential for its explainability. We show novel methods using information theoretic tools for the simplification and reduction of the Monte Carlo Tree Search and the extraction of information. Such information can be directly used for the construction of human understandable explanations. We show that basic explainability quantities can be calculated with limited additional computational cost, as an integrated part of the Monte Carlo Tree Search construction process. We focus on the theoretical and algorithmic aspects and provide examples of how the methods presented here can be used in the construction of human understandable explanations.

Systematic literature reviews are the highest quality of evidence in research. However, the review process is hindered by significant resource and data constraints. The Literature Review Network (LRN) is the first of its kind explainable AI platform adhering to PRISMA 2020 standards, designed to automate the entire literature review process. LRN was evaluated in the domain of surgical glove practices using 3 search strings developed by experts to query PubMed. A non-expert trained all LRN models. Performance was benchmarked against an expert manual review. Explainability and performance metrics assessed LRN's ability to replicate the experts' review. Concordance was measured with the Jaccard index and confusion matrices. Researchers were blinded to the other's results until study completion. Overlapping studies were integrated into an LRN-generated systematic review. LRN models demonstrated superior classification accuracy without expert training, achieving 84.78% and 85.71% accuracy. The highest performance model achieved high interrater reliability (k = 0.4953) and explainability metrics, linking 'reduce', 'accident', and 'sharp' with 'double-gloving'. Another LRN model covered 91.51% of the relevant literature despite diverging from the non-expert's judgments (k = 0.2174), with the terms 'latex', 'double' (gloves), and 'indication'. LRN outperformed the manual review (19,920 minutes over 11 months), reducing the entire process to 288.6 minutes over 5 days. This study demonstrates that explainable AI does not require expert training to successfully conduct PRISMA-compliant systematic literature reviews like an expert. LRN summarized the results of surgical glove studies and identified themes that were nearly identical to the clinical researchers' findings. Explainable AI can accurately expedite our understanding of clinical practices, potentially revolutionizing healthcare research.

The EU Artificial Intelligence Act (AI Act) came into force in the European Union (EU) on 1 August 2024. It is a key piece of legislation both for the citizens at the heart of AI technologies and for the industry active in the internal market. The AI Act imposes progressive compliance on organisations - both private and public - involved in the global value chain of AI systems and models marketed and used in the EU. While the Act is unprecedented on an international scale in terms of its horizontal and binding regulatory scope, its global appeal in support of trustworthy AI is one of its major challenges.

We introduce CollisionPro, a pioneering framework designed to estimate cumulative collision probability distributions using temporal difference learning, specifically tailored to applications in robotics, with a particular emphasis on autonomous driving. This approach addresses the demand for explainable artificial intelligence (XAI) and seeks to overcome limitations imposed by model-based approaches and conservative constraints. We formulate our framework within the context of reinforcement learning to pave the way for safety-aware agents. Nevertheless, we assert that our approach could prove beneficial in various contexts, including a safety alert system or analytical purposes. A comprehensive examination of our framework is conducted using a realistic autonomous driving simulator, illustrating its high sample efficiency and reliable prediction capabilities for previously unseen collision events. The source code is publicly available.

Mean Opinion Score as a New Metric for User-Evaluation of XAI Methods

Hyeon Yu, Jenny Benois-Pineau, Romain Bourqui +2

This paper investigates the use of Mean Opinion Score (MOS), a common image quality metric, as a user-centric evaluation metric for XAI post-hoc explainers. To measure the MOS, a user experiment is proposed, which has been conducted with explanation maps of intentionally distorted images. Three methods from the family of feature attribution methods - Gradient-weighted Class Activation Mapping (Grad-CAM), Multi-Layered Feature Explanation Method (MLFEM), and Feature Explanation Method (FEM) - are compared with this metric. Additionally, the correlation of this new user-centric metric with automatic metrics is studied via Spearman's rank correlation coefficient. MOS of MLFEM shows the highest correlation with automatic metrics of Insertion Area Under Curve (IAUC) and Deletion Area Under Curve (DAUC). However, the overall correlations are limited, which highlights the lack of consensus between automatic and user-centric metrics.

Automated reasoning is a key technology in the young but rapidly growing field of Explainable Artificial Intelligence (XAI). Explanability helps build trust in artificial intelligence systems beyond their mere predictive accuracy and robustness. In this paper, we propose a cycle of scientific discovery that combines machine learning with automated reasoning for the generation and the selection of explanations. We present a taxonomy of explanation selection problems that draws on insights from sociology and cognitive science. These selection criteria subsume existing notions and extend them with new properties.

Ever since the launch of ChatGPT, Generative AI has been a focal point for concerns about AI's perceived existential risk. Once a niche topic in AI research and philosophy, AI safety and existential risk has now entered mainstream debate among policy makers and leading foundation models developers, much to the chagrin of those who see it as a distraction from addressing more pressing nearer-term harms. This chapter aims to demystify the debate by highlighting the key worries that underpin existential risk fears in relation to generative AI, and spotlighting the key actions that governments and industry are taking thus far to helping address them.