Cognitive Fairness-Aware Techniques for Human-Machine Interface

Vithya Ganesan, S. Indu Vadhani, Subrata Chowdhury, Souvik Pal, Vishnu S. Pendyala

• Publisher: Chapman and Hall/CRC
• Year: 2025
• eBook ISBN: 9781040629963
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

This book explores the critical issue of fairness in human-machine interfaces. It delves into the integration of technology and cognitive science to develop AI systems that are unbiased, reliable, and user-friendly. The book also sheds light on emotional data processing in AI accelerators and federated learning modules. Additionally, it covers machine learning, knowledge representation, and the application of knowledge graphs to understand and optimize the behaviour of AI assistance devices.

Features:

• Explains complex issues of Cognitive Fairness Aware Contextual Proactive Federated Protocol collects data and identifies individual emotional issues and resolves them by contextual solitary proactive communication
• Discusses emotional data processing challenges through AI accelerator with federated learning module to generate periodical counselling messages
• Addresses data analysis anomalies in Graph Database Modelling by anom-aly prediction and anomaly detection
• Describes anomaly detection techniques in the form of abnormal data records, messages, events, groups, and/or other unexpected observations in graph database modelling
• Explains how outlier detection for data analysis deals with the detection of patterns in Graph Database

This book is for researchers, academics, students, AI practitioners and developers, ethics experts in AI technology and machine-learning practitioners interested in fairness in human-machine interfaces.

Table of contents

• Cover
• Half-Title
• Series
• Title
• Copyright
• Contents
• About the editors
• Contributors
• Chapter 1 Federated Learning by Contextual Model for Advanced AI Assistance
• Chapter 2 Computational Modeling for Personalized Emotions
• Chapter 3 A Review on Computational Modelling for Personalized Emotion and Visual Analytics to Predicting Habits
• Chapter 4 An impact of AI-Driven Sentiment Analysis Improves Stock Market Trend Predictions, Risk Management, and Ethics
• Chapter 5 Transformative Strategies for AIEd Interaction on AI Learning
• Chapter 6 Comprehensive Overview of Graph Database
• Chapter 7 Context-aware Knowledge Base Engineering for Anomaly Detection and Predictive Maintenance in Graph Databases
• Chapter 8 Context Anomaly Identification Algorithm Using Dirichlet
• Chapter 9 Human–Machine Interaction Failure for Indian Companies
• Chapter 10 Practical Solutions for Data Consistency and Query Performance in Graph Database and Search Engine Integration
• Chapter 11 Proactive Human–Machine Collaboration
• Chapter 12 Graph ML Pipeline for Anomaly Detection
• Chapter 13 Implementing a Graph Machine Learning Pipeline for Anomaly Detection
• Chapter 14 Proactive Human: Machine Collaboration
• Chapter 15 Proactive Assistance between Human and Machine
• Index