In the rapidly evolving landscape of artificial intelligence (AI), the principles of accountability and transparency are pivotal in ensuring ethical and responsible development. This chapter delves into the fundamental concepts and practical applications of accountability and transparency within AI systems. It begins by outlining the importance of these principles in mitigating risks such as bias, privacy infringement, and unintended consequences. The discussion progresses to explore methodologies and frameworks that promote transparency in AI algorithms, decision-making processes, and data usage. Additionally, the chapter examines the role of stakeholders—developers, policymakers, and users—in fostering a culture of accountability throughout the AI lifecycle. Through case studies and real-world examples, this chapter aims to provide a comprehensive guide for practitioners, researchers, and policymakers striving to navigate the ethical complexities of AI development while upholding societal trust and responsibility.

This research explores the improvement of tsunami occurrence forecasting with machine learning predictive models using earthquake-related data analytics. The primary goal is to develop a predictive framework that integrates a wide range of data sources, including seismic, geospatial, and ecological data, toward improving the accuracy and lead times of tsunami occurrence predictions. The study employs machine learning methods, including Random Forest and Logistic Regression, for binary classification of tsunami events. Data collection is performed using a Kaggle dataset spanning 1995–2023, with preprocessing and exploratory analysis to identify critical patterns. The Random Forest model achieved superior performance with an accuracy of 0.90 and precision of 0.88 compared to Logistic Regression (accuracy: 0.89, precision: 0.87). These results underscore Random Forest’s effectiveness in handling imbalanced data. Challenges such as improving data quality and model interpretability are discussed, with recommendations for future improvements in real-time warning systems.

This study compares the volatility of two prominent cryptocurrencies, Dogecoin (DOGE) and Solana (SOL), using historical price data spanning five years from June 3, 2019, to June 3, 2024. By leveraging detailed daily trading information, the analysis provides a comprehensive understanding of the risk profiles associated with each cryptocurrency. The methodology involves data preprocessing, exploratory data analysis (EDA), volatility calculation using 30-day rolling windows, and statistical testing, including two-sample t-tests and variance ratio tests. The findings indicate that both DOGE and SOL exhibit significant price variability, with SOL showing higher average prices and greater standard deviation compared to DOGE. For instance, the mean closing price for DOGE was $0.0875 with a standard deviation of $0.0941, while SOL had a mean closing price of $54.6754 and a standard deviation of $59.3020. Historical volatility trends reveal distinct patterns: DOGE’s volatility is primarily influenced by social media trends and speculative trading, whereas SOL’s volatility is driven more by technological advancements and market developments. The two-sample t-test results show no significant difference in the mean volatilities of DOGE and SOL (t-statistic: -0.8674, p-value: 0.3858), but the variance ratio test highlights that SOL’s volatility is significantly more variable than DOGE’s, with a variance ratio of 10.7028. These results suggest that while the average risk levels of DOGE and SOL are similar, their volatility behaviors differ significantly. For investors, understanding these distinct volatility characteristics is crucial for making informed decisions regarding asset allocation and risk management. The study's insights also provide valuable guidance for financial analysts and portfolio managers, emphasizing the importance of considering both average volatility and its variability when assessing the risk profiles of cryptocurrencies. Future research should explore the impact of external factors such as regulatory changes and macroeconomic events on cryptocurrency volatility and expand the analysis to include other digital assets and longer time periods. Incorporating high-frequency trading data and advanced econometric models could further enhance the accuracy of volatility predictions, offering deeper insights into the behavior of digital currencies under various market conditions

In the dynamic and rapidly evolving landscape of blockchain technology, traditional fraud detection methods, which often rely on labeled data, face limitations due to the diverse and adaptive nature of fraud. This study introduces a novel framework that employs the K-Means clustering algorithm, a technique celebrated for its unsupervised learning capabilities, to detect anomalous transaction patterns indicative of potential fraud, such as unusually high transaction volumes or rapid transfers between wallets. By circumventing the need for pre-labeled examples of fraudulent activity, our approach significantly enhances adaptability and applicability across various blockchain contexts. We apply this framework to a comprehensive dataset encompassing multiple cryptocurrencies, including Bitcoin, Ethereum, Doge Coins, and Tether, analyzing attributes such as closing prices, volatility, and market volume. The results demonstrate the framework’s effectiveness in isolating outliers and identifying transactions that bear hallmarks of suspicious activity, thereby contributing a powerful tool for proactive fraud detection. This research not only paves the way for future advancements in blockchain security but also reinforces the trustworthiness and integrity of blockchain systems by providing a robust mechanism for identifying and mitigating fraudulent activities without the constraints of traditional, supervised methods.

Cloud computing has emerged as a transformative technology in financial services, promising operational efficiency, scalability, and innovation. However, significant concerns related to security, Trust, and other challenges hinder the adoption of cloud services. This paper comprehensively analyzes these challenges, highlighting the interplay between security measures, product-related factors, and their impact on trust and cloud adoption within financial institutions. The study employs a predictive correlational quantitative research design to explore the security and trust factors influencing the adoption of cloud services. The methodology extends traditional correlation analysis by employing predictive correlation and multiple regression analysis within a Partial Least Squares Structural Equation Modeling (PLS-SEM) framework to maximize the explained variance of the dependent variable, Trust. The findings reveal that while security measures such as robust data protection, regulatory compliance, and cybersecurity protocols are crucial for mitigating risks and addressing concerns related to data breaches, privacy, and unauthorized access, they are insufficient to drive widespread adoption. Product-related factors, including operational efficiency, scalability, cost savings, and the ability to innovate and develop new products and services, are pivotal in shaping the perceived value and Trust in cloud computing. Additionally, the study identifies challenges such as multi-tenancy risks, the semantic gap in data analysis, loss of control over data, the complexity of regulatory compliance, and skill gaps in the workforce as significant barriers to cloud adoption. The study emphasizes the importance of a holistic approach that combines robust security measures with a compelling product offering tailored to the specific needs of financial institutions.