This paper introduces a structured approach to improving decision making in Decentralized Autonomous Organizations (DAO) through the integration of the Question-Option-Criteria (QOC) model and AI agents. We outline a stepwise governance framework that evolves from human led evaluations to fully autonomous, AI-driven processes. By decomposing decisions into weighted, criterion based evaluations, the QOC model enhances transparency, fairness, and explainability in DAO voting. We demonstrate how large language models (LLMs) and stakeholder aligned AI agents can support or automate evaluations, while statistical safeguards help detect manipulation. The proposed framework lays the foundation for scalable and trustworthy governance in the Web3 ecosystem.

So-called `wicked problems', those involving complex multi-dimensional settings, non-verifiable outcomes, heterogeneous impacts and a lack of single objectively correct answers, have plagued humans throughout history. Modern examples include decisions over justice frameworks, solving environmental pollution, planning for pandemic resilience and food security. The use of state-of-the-art artificial intelligence systems (notably Large Language Model-based agents) collaborating with humans on solving such problems is being actively explored. While the abilities of LLMs can be improved by, for example, fine-tuning, hand-crafted system prompts and scaffolding with external tools, LLMs lack endogenous mechanisms to develop expertise through experience in such settings. This work address this gap with Dialectica, a framework where agents engage in structured dialogue on defined topics, augmented by memory, self-reflection, and policy-constrained context editing. Formally, discussion is viewed as an implicit meta-reinforcement learning process. The `dialogue-trained' agents are evaluated post-hoc using judged pairwise comparisons of elicited responses. Across two model architectures (locally run Qwen3:30b and OpenAI's o4-mini) results show that enabling reflection-based context editing during discussion produces agents which dominate their baseline counterparts on Elo scores, normalized Bradley-Terry-Davidson ability, and AlphaRank mass. The predicted signatures of learning are observed qualitatively in statement and reflection logs, where reflections identify weaknesses and reliably shape subsequent statements. Agreement between quantitative and qualitative evidence supports dialogue-driven context evolution as a practical path to targeted expertise amplification in open non-verifiable domains.

PEER-REVIEWED AND ACCEPTED IN IEEE- ISTAS 2025 The rapid evolution of Generative AI (GenAI) has introduced unprecedented opportunities while presenting complex challenges around ethics, accountability, and societal impact. This paper draws on a literature review, established governance frameworks, and industry roundtable discussions to identify core principles for integrating responsible GenAI governance into diverse organizational structures. Our objective is to provide actionable recommendations for a balanced, risk-based governance approach that enables both innovation and oversight. Findings emphasize the need for adaptable risk assessment tools, continuous monitoring practices, and cross-sector collaboration to establish trustworthy GenAI. These insights provide a structured foundation and Responsible GenAI Guide (ResAI) for organizations to align GenAI initiatives with ethical, legal, and operational best practices.

--Artificial intelligence (AI) trends vary significantly across global regions, shaping the trajectory of innovation, regulation, and societal impact. This variation influences how dif - ferent regions approach AI development, balancing technological progress with ethical and regulatory considerations. This study conducts a comparative analysis of AI trends in the United States (US), the European Union (EU), and Asia, focusing on three key dimensions: generative AI, ethical oversight, and industrial applications. The US prioritizes market -driven innovation with minimal regulatory constraints, the EU enforces a precautionary risk -based framework emphasizing ethical safeguards, and Asia employs state -guided AI strategies that balance rapid deployment with regulatory oversight. Although these approaches reflect different economic models and policy priorities, their divergence poses challenges to international collaboration, regulatory harmonization, and the development of global AI standards. To address these challenges, this paper synthesizes regional strengths to propose an adaptive AI governance framework that integrates risk -tiered oversight, innovation accelerators, and strategic alignment mechanisms. By bridging governance gaps, this study offers actionable insights for fostering responsible AI development while ensuring a balance between technological progress, ethical imperatives, and regulatory coherence. Artificial intelligence (AI) has emerged as a transformative force in the 21st century, reshaping industries, governance structures, and societal interactions at an unprecedented pace. From generative AI creating human - like text and images to autonomous systems revolutionizing healthcare, finance, and manufacturing, AI's influence is profound and far - reaching.

The development and deployment of artificial intelligence (AI) systems, with their profound societal impacts, raise critical challenges for governance. Historically, technological innovations have been governed by concentrated expertise with limited public input. However, AI's pervasive influence across domains such as healthcare, employment, and justice necessitates inclusive governance approaches. This article explores the tension between expert-led oversight and democratic participation, analyzing models of participatory and deliberative democracy. Using case studies from France and Brazil, we highlight how inclusive frameworks can bridge the gap between technical complexity and public accountability. Recommendations are provided for integrating these approaches into a balanced governance model tailored to the European Union, emphasizing transparency, diversity, and adaptive regulation to ensure that AI governance reflects societal values while maintaining technical rigor. This analysis underscores the importance of hybrid frameworks that unite expertise and public voice in shaping the future of AI policy.

Artificial intelligence will someday know you better than you know yourself. That day may be sooner than we realize with the amount of data collected on all humans and their environments increasing exponentially. So where are the rules, and what are our rights? Over the past few centuries, data has been collected at high levels: primarily on companies, countries, societies, cultures, religions and other high-level aggregations. With the data age in full swing, we are delving into the frontier of individual data--a level previously unreached in terms of deeply knowing and connecting humans.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Data and judgment complement AI as core elements of decision-making in war and national security in general. The human-like behavior of AI-based technology raises questions regarding the interfaces between science, technology, and society.

Some two weeks ago, Jason M Allen of Pueblo West won first prize in the digital category at the Colorado State Fair for his art work named work "ThéâtreD'opéra Spatial". It was no ordinary art work. Allen used Midjourney, an artificial intelligence (AI) programme, for creating the artwork by converting text into hyper-realistic graphics. The art world remained divided over the ethics of such an AI-generated art, with some purists expressing indignation at the way technology is taking dominance over human artistry and originality. Do purists have a point? Will we see machines overtaking humans in every sphere, including the sublime realm of art?

The COVID-19 crisis forced businesses everywhere to fast track their digital transformation efforts. Faced with the stark choice of becoming a digital-first business, or having no business at all, companies that were previously behind the curve had to implement everything from remote working to entire digital storefronts in a matter of days. According to research by McKinsey, the digital initiatives unleashed in response to the pandemic leapfrogged seven years of progress in a matter of months as companies acted 20 to 25 times faster than they had believed was possible. In the process, this acceleration of digital during the crisis brought about a sea change in executive mindsets with regard to the role of technology in business. Fast forward to today, and corporate leaders are now investing in technology for competitive advantage, refocusing their entire business around cutting-edge technologies, and initiating a business culture where experimentation and innovation is actively encouraged.

Artificial Intelligence (AI) is opening up a new frontier by combining human creativity with technology to drive progress in our society and bring governments closer to their constituents. According to the 2018 United Nations (UN) e-Government Survey all 193 Member States have e-government systems in place, at different maturity levels, to deliver digital services and experiences to citizens. The three most commonly used e-government services are paying utilities (140 countries), submitting income taxes (139 countries), and registering a new business (126 countries). Denmark is heading the top 10 e-government development ranking, followed by Australia, the Republic of Korea, United Kingdom, Sweden, Finland, Singapore, New Zealand, France and Japan. The next phase of e-government will use AI to go beyond digitized and automated services and deliver better experiences to citizens.