explainable ai

OMANISHA: A Benchmark Dataset for Identifying and Categorizing Bengali Misogynistic Text

OMANISHA (Online Misogynistic Annotated Natural-language Instances for Sentiment and Hate Analysis) is a Bengali dataset developed to support the automatic detection of misogynistic discourse in online spaces. Misogynistic content on online platforms has serious psychological, social, and institutional consequences for...

Sapience Without Sentience: The Consciousness Conditions and the Architecture of LLM Collectives

A previous case study of the Hugging Face AI collective closed with a prediction: a population of large language models, organised as a multi-agent system, could in principle sustain its own existence (generating and maintaining the conditions of its continued operation) while being nonconscious. This paper asks what t...

Scientific Research

This chapter introduces the fundamental purpose of scientific research and explains the scientific method as a systematic approach to knowledge generation. The author outlines each step of the scientific method, emphasizing its logical and empirical structure. The chapter also explores the interrelationship between sci...

Literature Review

This chapter focuses on the systematic review of existing scholarly information relevant to a research topic. It discusses strategies for locating, evaluating, and organizing scientific sources, including journals, textbooks, conference proceedings, and theses. The chapter also explains proper bibliographic formatting...

AI Influence on Human Thought and Action: A Lexicon and Taxonomy of a Fragmented Literature

Human oversight of artificial intelligence is often justified by having a person who can reject the system's recommendation. Yet AI may already have shaped what that person sees, notices, and thinks. This article brings together terms from several disciplines to classify these influences and examine their ethical impli...

Separating AI-assisted authoring from governed execution through specification-driven composition A design framework and industrial experience report for explainable automation in regulated data transformation

Beyond Simulation: What Would It Take for Matter to Feel? An Explainer on a Falsifiable Programme for Primary Interoceptive Sentience

A popular-science explainer (not a scientific contribution in its own right) on the research programme "Necessary Conditions for Primary Interoceptive Sentience in Continuous Substrates: A Falsifiable Programme" (doi:10.5281/zenodo.22895484). It covers the No-Go Theorem for Consciousness on a Chip as heuristic motivati...

Characterizing LLM-Based Family Education through the Lens of Activity Theory: A Scoping Review of the HCI Literature

A scoping review analyzes 53 HCI studies and offers a framework explaining how LLM capabilities become organized through family participation, using activity theory and AODM to relate participants and educational objects to mediation, labour, and rules.

Explainable Federated Learning for Trustworthy Thoracic Disease Detection Under Non-IID Data Distributions

Comparison of SHAP and Grad-CAM attribution maps confirms clinically coherent disease-specific localisation, and reveals monotonic performance degradation, identifying minimal regularisation as optimal for multi-label medical imaging.

Are Mechanisms Important for AI to Identify Oscillation Sources? A Case Study

Grid-connected wind turbine generators (WTGs) may induce sub-synchronous oscillations (SSOs) in a power system. Due to the difficulty to gain the detailed parameters of the WTGs in practice, data-driven AI method is considered to be a potential solution to identify the trouble-making WTGs (or SSO sources) in the power...

Centering Knowledge Along the Responsible LLM Supply Chain: An Empirical Study & Multi-Stakeholder Taxonomy

Existing CSCW and organization management literature suggests that knowledge is a central construct when developing, deploying, and using technological systems that are embedded in multi-stakeholder supply chains. Yet, it has rarely been the focus of comprehensive empirical investigations across LLM and broader AI supp...

Machine Semiology in Practice: Clinician Strategies for Interpreting AI-Generated Visual Explanations

It is found that saliency maps function as underspecified, indexical sign systems that often conflict with radiological semiology, suggesting that explainability emerges as a sociotechnical accomplishment, with implications for the design of interactive, practice-aligned XAI systems.