This research studies the occurrence of data breaches in healthcare provider settings regarding patient data. Using visual analytics and data visualization tools, we study the distribution of healthcare breaches by state. We review the main causes and types of breaches, as well as their impact on both providers and patients. The research shows a range of data breach victims. Network servers are the most popular location for common breaches, such as hacking and information technology (IT) incidents, unauthorized access, theft, loss, and improper disposal. We offer proactive recommendations to prepare for a breach. These include, but are not limited to, regulatory compliance, implementing policies and procedures, and monitoring network servers. Unfortunately, the results indicate that the probability of data breaches will continue to rise.

While data breaches are a frequent and universal phenomenon, the characteristics and dimensions of data breaches are unexplored. In this novel exploratory research, we apply machine learning (ML) and text analytics to a comprehensive collection of data breach litigation cases to extract insights from the narratives contained within these cases. Our analysis shows stakeholders (e.g., litigants) are concerned about major topics related to identity theft, hacker, negligence, FCRA (Fair Credit Reporting Act), cybersecurity, insurance, phone device, TCPA (Telephone Consumer Protection Act), credit card, merchant, privacy, and others. The topics fall into four major clusters: “phone scams”, “cybersecurity”, “identity theft”, and “business data breach”. By utilizing ML, text analytics, and descriptive data visualizations, our study serves as a foundational piece for comprehensively analyzing large textual datasets. The findings hold significant implications for both researchers and practitioners in cybersecurity, especially those grappling with the challenges of data breaches.

As climate change, environmental, social, and governance (ESG), along with sustainability, become increasingly crucial for businesses and society, there is a noticeable scarcity of information and transparency regarding corporate practices. Often, government agency enforcement actions lead to litigation and are ultimately resolved by court decisions. Moreover, in instances when there is perceived inadequacy in government enforcement, citizens frequently turn to the courts for preventive judgments against businesses or agencies. In an effort to shed light on the multifaceted aspects of climate change, we adopted a novel, exploratory approach to analyze climate change-related litigation cases. Utilizing a blend of machine learning-based text analytics, we have extracted key insights from individual case narratives. Our analysis encompassed over four hundred cases from the Westlaw database through various keyword searches. The emergent topics from our case dataset revolved around four critical environmental themes: forest, land, water, and air emissions. Our findings provide insight into the nature and dimensions of climate change and also carry significant policy implications, laying the groundwork for future research in this domain.

The rapid proliferation of artificial intelligence (AI) systems across high-stakes domains—with global AI adoption accelerating since 2023—has created an urgent need to identify which AI challenges and issues are materializing into real-world harms so that policymakers can develop targeted regulations, organizations can implement effective risk management, and accountability mechanisms can address actual rather than speculative problems. Public concern has risen sharply: 52% of Americans now feel more concerned than excited about AI (up from 38% in 2022), and 80% believe government should maintain AI safety rules even if development slows. Yet existing approaches exhibit critical limitations that impede evidence-based governance. Ethics frameworks, while establishing normative principles across 84+ published guidelines, remain aspirational rather than empirical. Survey-based studies capture perceptions from over 48,000 respondents globally but measure expectations rather than documented harms. Incident databases catalog over 1200 AI failures but depend on media coverage, systematically overrepresenting high-profile cases while underrepresenting routine organizational problems. This study addresses this gap by analyzing 347 AI-related U.S. litigation cases using machine learning text analytics, providing empirical evidence of AI problems that have crossed the threshold from abstract concern into documented legal conflict. Employing Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) topic modeling with coherence validation (NMF achieving 0.276 NPMI vs. LDA’s 0.164), the analysis identifies nine distinct AI issue areas with specific case distributions: cybersecurity vulnerabilities and data breaches (116 cases, 33.4%), intellectual property and AI ownership (61 cases, 17.6%), AI misrepresentation and inflated claims (59 cases, 17.0%), criminal justice and algorithmic due process (37 cases, 10.7%), employment automation (33 cases, 9.5%), privacy and surveillance (31 cases, 8.9%), platform accountability (21 cases, 6.1%), algorithmic bias (19 cases, 5.5%), and government AI deployment (6 cases, 1.7%). The findings reveal a systematic mismatch between AI ethics discourse—which emphasizes fairness and transparency—and litigation patterns, where data security (33.4%) and intellectual property (17.6%) dominate while algorithmic bias comprises only 5.5% of cases. Most disputes are addressed through existing legal frameworks (First Amendment, Lanham Act, FOIA, Title VII) rather than AI-specific regulation, underscoring the urgent need for governance mechanisms aligned with empirically documented AI challenges.