Interesting take…
https://journals.irapa.org/index.php/JESTT/article/view/1013
Artificial Intelligence in Autonomous Weapon Systems: Legal Accountability and Ethical Challenges
Autonomous Weapon Systems (AWS) are reshaping modern warfare, offering enhanced operational efficiency but raising significant legal, ethical, and regulatory concerns. Their capacity to engage targets without human intervention creates an accountability gap, challenging the application of International Humanitarian Law (IHL). The current legal frameworks are incompetent to define meaningful human control. That complicate the attribution of responsibility when AWS violate human rights. Ethical challenges, including the dehumanization of warfare, algorithmic biases, and indiscriminate targeting, jeopardize civilian protection. Moreover, the proliferation of AWS amplifies global security risks, particularly with their potential misuse by non-state actors. This paper critically examines these challenges, evaluating current legal frameworks, ethical considerations, and regulatory inconsistencies. It proposes war torts, corporate accountability, transparency measures, and binding international treaties to address governance gaps. Supports international cooperation and oversight mechanisms is essential to ensure AWS comply with IHL and human rights law. This research contributes to the global discourse on autonomous warfare, offering practical policy recommendations for ethical and legal governance.
Automating the law of AI?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5166908
Legal Challenges in Protecting Personal Information in Big Data Environments
The rapid expansion of artificial intelligence (AI) and high-speed big data processing has raised significant legal challenges in safeguarding personal information. Traditional data protection frameworks struggle to address issues such as mass data collection, cross-border data transfers, and evolving cyber threats, particularly in AI-powered, high-speed data environments. This research examines key legal concerns, including compliance with privacy regulations, ethical considerations in AI-enhanced data processing, and enforcement limitations in large-scale data ecosystems. The study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to systematically evaluate legal frameworks, case studies, and technological solutions for data protection. By applying PRISMA, the research ensures a structured approach to selecting, screening, and analyzing studies on data privacy regulations and their effectiveness. Additionally, AI-driven big data analytics present new challenges in balancing regulatory compliance with real-time, high-speed data processing demands. The study investigates how well-established legal frameworks—such as the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR)—address AI-enhanced risks of data breaches, unauthorized access, and personal information misuse. A structured data collection process was implemented using established databases such as Google Scholar, IEEE Xplore, PubMed, Westlaw, and LexisNexis. Quantitative analysis techniques, including descriptive statistics, chi-square tests, regression analysis, and meta-analysis, were applied to examine compliance rates, reported data breaches, monetary penalties, and response times to data incidents. The statistical analysis reveals significant inconsistencies in data privacy enforcement, as compliance rates vary widely (mean: 72.5%, SD: 12.3), and financial penalties under GDPR and CCPA range significantly (median: $1.1M, max: $5.2M). Furthermore, chi-square tests indicate a significant relationship between fines and compliance rates (p < 0.05), highlighting the impact of regulatory penalties on corporate adherence to data protection laws. As AI-powered high-speed data systems continue to evolve, there is an increasing need for adaptive legal frameworks that can address privacy risks while enabling technological innovation. This study emphasizes the necessity of AI-driven compliance mechanisms, automated regulatory monitoring, and real-time enforcement strategies to safeguard personal information in the era of high-speed big data processing.
Thinking real thoughts about artificial people.
Do androids dream of electric sheep? This Michigan educator’s classes ponder the humanity of A.I.
Matthew Katz knows you might be worried about “The Terminator.”
The Central Michigan University philosophy professor, though, also wants you to consider whether an android — a Terminator or something with less sinister intent — could one day “worry” about you.
