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Language data and models demonstrate various types of bias, be it ethnic, religious, gender, or socioeconomic. AI/NLP models, when trained on the racially biased dataset, AI/NLP models instigate poor model explainability, influence user experience during decision making and thus further magnifies societal biases, raising profound ethical implications for society. The motivation of the study is to investigate how AI systems imbibe bias from data and produce unexplainable discriminatory outcomes and influence an individual's articulateness of system outcome due to the presence of racial bias features in datasets. The design of the experiment involves studying the counterfactual impact of racial bias features present in language datasets and its associated effect on the model outcome. A mixed research methodology is adopted to investigate the cross implication of biased model outcome on user experience, effect on decision-making through controlled lab experimentation. The findings provide foundation support for correlating the implication of carry-over an artificial intelligence model solving NLP task due to biased concept presented in the dataset. Further, the research outcomes justify the negative influence on users' persuasiveness that leads to alter the decision-making quotient of an individual when trying to rely on the model outcome to act. The paper bridges the gap across the harm caused in establishing poor customer trustworthiness due to an inequitable system design and provides strong support for researchers, policymakers, and data scientists to build responsible AI frameworks within organizations., Comment: Fresh experiments need to be added to the design of experiments

The purpose of the paper is to provide innovative emerging technology framework for community to combat epidemic situations. The paper proposes a unique outbreak response system framework based on artificial intelligence and edge computing for citizen centric services to help track and trace people eluding safety policies like mask detection and social distancing measure in public or workplace setup. The framework further provides implementation guideline in industrial setup as well for governance and contact tracing tasks. The adoption will thus lead in smart city planning and development focusing on citizen health systems contributing to improved quality of life. The conceptual framework presented is validated through quantitative data analysis via secondary data collection from researcher's public websites, GitHub repositories and renowned journals and further benchmarking were conducted for experimental results in Microsoft Azure cloud environment. The study includes selective AI-models for benchmark analysis and were assessed on performance and accuracy in edge computing environment for large scale societal setup. Overall YOLO model Outperforms in object detection task and is faster enough for mask detection and HRNetV2 outperform semantic segmentation problem applied to solve social distancing task in AI-Edge inferencing environmental setup. The paper proposes new Edge-AI algorithm for building technology-oriented solutions for detecting mask in human movement and social distance. The paper enriches the technological advancement in artificial intelligence and edge-computing applied to problems in society and healthcare systems. The framework further equips government agency, system providers to design and constructs technology-oriented models in community setup to Increase the quality of life using emerging technologies into smart urban environments.

Software testing is an important and valuable part of the software development life cycle. Due to time, cost and other circumstances, exhaustive testing is not feasible that's why there is a need to automate the software testing process. Testing effectiveness can be achieved by the State Transition Testing (STT) which is commonly used in real time, embedded and web-based type of software systems. Aim of the current paper is to present an algorithm by applying an ant colony optimization technique, for generation of optimal and minimal test sequences for behavior specification of software. Present paper approach generates test sequence in order to obtain the complete software coverage. This paper also discusses the comparison between two metaheuristic techniques (Genetic Algorithm and Ant Colony optimization) for transition based testing, Comment: Electronic System Design (ISED), 2010 International Symposium on Issue Date: 20-22 Dec. 2010 On page(s): 235 - 240 Location: Bhubaneswar Print ISBN: 978-1-4244-8979-4 INSPEC Accession Number: 11835766 Digital Object Identifier: 10.1109/ISED.2010.52