A Theoretical Framework for AI Models Explainability

EXplainable Artificial Intelligence (XAI) is a vibrant research topic in the artificial intelligence community, with growing interest across methods and domains. Much has been written about the subject, yet XAI still lacks shared terminology and a framework capable of providing structural soundness to explanations. In our work, we address these issues by proposing a novel definition of explanation that is a synthesis of what can be found in the literature. We recognize that explanations are not atomic but the combination of evidence stemming from the model and its input-output mapping, and the human interpretation of this evidence. Furthermore, we fit explanations into the properties of faithfulness (i.e., the explanation being a true description of the model's inner workings and decision-making process) and plausibility (i.e., how much the explanation looks convincing to the user). Using our proposed theoretical framework simplifies how these properties are operationalized and it provides new insight into common explanation methods that we analyze as case studies

VISION DIFFMASK: Faithful Interpretation of Vision Transformers with Differentiable Patch Masking

The lack of interpretability of the Vision Transformer may hinder its use in critical real-world applications despite its effectiveness. To overcome this issue, we propose a post-hoc interpretability method called VISION DIFFMASK, which uses the activations of the model's hidden layers to predict the relevant parts of the input that contribute to its final predictions. Our approach uses a gating mechanism to identify the minimal subset of the original input that preserves the predicted distribution over classes. We demonstrate the faithfulness of our method, by introducing a faithfulness task, and comparing it to other state-of-the-art attribution methods on CIFAR-10 and ImageNet-1K, achieving compelling results. To aid reproducibility and further extension of our work, we open source our implementation: https://github.com/AngelosNal/Vision-DiffMaskComment: Accepted in the XAI4CV Workshop at CVPR 202

Economic Analysis and Organised Religion

This chapter analyses some phenomena in organized religion from the point of view of economics. It is argued that religious activity derives from the individual's quest for sense and justification that molds institutional and other features of religious activity, as brought about by competitive forces. The underlying concern regards the interrelation of economic and cultural processes

Interpretivism and norms

This article reconsiders the relationship between interpretivism about belief and normative standards. Interpretivists have traditionally taken beliefs to be fixed in relation to norms of interpretation. However, recent work by philosophers and psychologists reveals that human belief attribution practices are governed by a rich diversity of normative standards. Interpretivists thus face a dilemma: either give up on the idea that belief is constitutively normative or countenance a context-sensitive disjunction of norms that constitute belief. Either way, interpretivists should embrace the intersubjective indeterminacy of belief

Economic Analysis and Organised Religion

This chapter analyses some phenomena in organized religion from the point of view of economics. It is argued that religious activity derives from the individual's quest for sense and justification that molds institutional and other features of religious activity, as brought about by competitive forces. The underlying concern regards the interrelation of economic and cultural processes.economics of religion; division of labor; institutional economics; culture and economics

The Textual-Visual Thematic Analysis: A Framework to Analyze the Conjunction and Interaction of Visual and Textual Data

Visual methods offer an innovative approach to qualitative research through their potential to prompt dialogue, enrich verbal and textual data, and enable participants to communicate about difficult topics. However, the use of visual methods requires that researchers rethink methodological aspects of data generation and analysis, especially when working with participant-generated images. Although there are now many analytical frameworks and guidebooks providing instructions on the analysis of textual and visual materials, detailed descriptions of how these elements are brought together are often missing from research reports, precluding novice and other researchers from understanding how findings were attained. Our aim in this article is to describe and illustrate the Textual-Visual Thematic Analysis (TVTA), a framework we developed to collaboratively analyze the conjunction and interaction of textual and visual data in a photo-elicitation study. Given that the ethical and methodological aspects are deeply entwined, we begin the article by contextualizing the data obtained from the photo-elicitation study and then consider confidentiality and approaches to valuing participants\u27 voices. Next, we share the TVTA framework, its procedural implementation, and insights derived from evolving our data analysis approach. We conclude by offering reflections on the limitations and possibilities for future research