No questions answered?

https://philpapers.org/rec/ADETFO-2

The Future of AI Governance

The rapid evolution of artificial intelligence (AI) has generated both unprecedented opportunities and complex governance challenges. This study examines the future of AI governance by analyzing existing regulatory frameworks, identifying governance gaps, and proposing adaptive approaches that balance innovation with ethical responsibility. Using a qualitative, comparative policy analysis, the research focuses on the European Union (EU), United States (US), and China, alongside international organizations such as the OECD and UNESCO. Data were derived from academic publications, regulatory documents, and policy reports published between 2017 and 2023, enabling a thematic evaluation of divergent governance models, ethical principles, accountability mechanisms, stakeholder inclusion, adaptive regulation, and international cooperation. The findings highlight significant divergences in governance: the EU’s risk-based and rights-oriented AI Act, the US’s decentralized and industry-driven model, and China’s centralized, state-controlled approach. While ethical principles such as fairness, transparency, and accountability are widely endorsed, enforcement gaps persist, raising concerns of “ethics washing.” Liability frameworks remain underdeveloped, and stakeholder participation is uneven, with limited inclusivity in authoritarian contexts. Adaptive mechanisms such as regulatory sandboxes and public–private partnerships show promise but require further empirical validation. Moreover, global cooperation is hampered by geopolitical competition, producing fragmented governance regimes. The study concludes that effective AI governance demands a global, multidisciplinary, and inclusive approach that integrates legal clarity, ethical safeguards, and adaptive oversight. Policy implications include the creation of interoperable global standards, enforceable liability mechanisms, binding ethical requirements, stakeholder-inclusive councils, and expanded regulatory sandboxes. By embedding fairness, accountability, and human rights into governance structures, societies can harness AI’s benefits while mitigating its risks and safeguarding democratic values.

AI as “pimp?” Would this work in the drug world? How about other areas...

https://digitalcommons.uri.edu/dignity/vol11/iss2/8/

Leveraging Technology to Measure the Strength of Demand for Commercial and Trafficked Sex

The article examines Transaction Intercept (TI), an artificial‑intelligence platform designed to reduce sex trafficking by disrupting the revenue streams that draw organized crime into the market. Transaction Intercept deploys decoy online ads that appear to offer commercial sexual exploitation or prostitution. Automated "decoy" personas connect to individuals who click on these ads and engage them in text conversations. These exchanges can remain fully automated, ending with deterrence messages, or be transferred to police for potential enforcement action or to trained volunteers who attempt to discourage further attempts to buy sex. Transaction Intercept generates extensive, real‑time data on local demand for commercial sex, including the characteristics of ads that attract buyers, the number of unique individuals who respond, and behavioral patterns by sex. Transaction Intercept generates extensive, real‑time data on local demand, including the characteristics of ads that attract buyers, the number of unique individuals who respond, behavioral patterns among sex buyers, and the outcomes of each interaction. Provided at no cost to law enforcement, the system allows agencies to quickly assess the scale and traits of consumer‑level demand for sex trafficking within their jurisdiction. In its current deployment, Transaction Intercept has facilitated more than 700,000 text exchanges and identified over 25,000 sex buyers across over 75 U.S. cities. The article outlines how this technology can inform public policy, strengthen law‑enforcement practice, and be adapted for use in other national contexts.