人工智能医疗影像诊断侵权损害赔偿法律问题

人工智能医疗影像诊断侵权损害是指临床医生根据人工智能医疗影像辅助诊断结论所实施的医疗行为对患者造成的损害。人工智能与医疗影像技术的融合应用,大大提高了疾病诊断的效率与质量,其诊断错误所引发的侵权损害赔偿问题也不容忽视。人工智能医疗影像诊断模式多样、责任主体多元、损害原因各异、责任份额不同,其引起的侵权责任错综复杂。人工智能医疗影像诊断侵权应定性为多数人侵权中的分别侵权行为。现阶段人工智能医疗影像诊断侵权损害赔偿的判定,应以人工智能医疗影像诊断设备的民事法律关系客体定位为逻辑起点,根据人工智能医疗影像诊断模式、侵权场景、错误发生原因等因素来判定责任主体,综合原因力大小、过错程度等因素来确定赔偿份额,并从“利益平衡”视角对侵权损害赔偿的责任范围进行适当限制,以促进人工智能技术在医疗影像行业的广泛应用和大健康产业的长足发展。     

人工智能时代精准人才管理探析

人才是组织获得长远发展的第一资源。随着人工智能时代的到来,如何运用人工智能技术对人才进行精准化地选、用、育、留、储,是组织探索解决人才管理瓶颈的关键。研究从人工智能时代精准人才管理的挑战与机遇入手,指出基于"合不合"理论以构建智能化精准人才管理系统,并探讨了"AI人才管理专家系统"的具体运用情况,以期为各类组织进行精准人才管理提供帮助。     

弱人工智能时代的劳动价值论与劳动制度

随着弱人工智能时代的到来,物质生产与非物质生产全面自动化。这个正在生成的社会现实意味着经典劳动价值论“一切价值均来自人类劳动”的命题必须被重新思考。笔者以为,弱人工智能应被视为介于人与物之间的特殊劳动者。这样才能解释弱人工智能时代的价值创造问题。从这个角度出发,将弱人工智能视为物或工具就是一种未经反思的意识形态,其建构方向是资本原则与弱人工智能结合的永续生产模式。因此,只有强调并确立弱人工智能的劳动者地位,才能有效破除资本原则对社会发展模式的控制,将弱人工智能强大的生产能力服务于人类自由发展的目的。     

Responsibility Determination: Free Will and Legal Responsibility in the Age of Artificial Intelligence

Free will is the foundation of determination of responsibility. Genetic enginnering represented by technologies of gene editing, artificial medical devices and AI have fundamentally challenged the concept of free will and so have significantly influenced determination of legal responsibility. These challenges are fundamental, not instrumental, and can be divided into two aspects in legal philosophy. First, the direct challenge, that is, the emerging technology represented by genetic engineering and artificial narrow intelligence (ANI) has challenged the concept of free will. Second the would-be ultimate challenge, that is, presented by an artificial general intelligence (AGI) agent that is considered to reach humanlevel free will, can be a legal subject, thus taking full legal responsibility. The direct challenge constitutes a new “forgiveness” condition for taking responsibility. The would-be ultimate challenge deserves significant attention, because the concept of free will is not only about human responsibility, but also about human dignity.     

The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration

ABSTRACT

As government and public administration lag behind the rapid development of AI in their efforts to provide adequate governance, they need respective concepts to keep pace with this dynamic progress. The literature provides few answers to the question of how government and public administration should respond to the great challenges associated with AI and use regulation to prevent harm. This study analyzes AI challenges and former AI regulation approaches. Based on this analysis and regulation theory, an integrated AI governance framework is developed that compiles key aspects of AI governance and provides a guide for the regulatory process of AI and its application. The article concludes with theoretical implications and recommendations for public officers.     

New Challenges Posed by Robots to China’s Civil Code in the Age of Artificial Intelligence

In the age of artificial intelligence (AI), robots have profoundly impacted our life and work, and have challenged our civil legal system. In the course of AI development, robots need to be designed to protect our personal privacy, data privacy, intellectual property rights, and tort liability identification and determination. In addition, China needs an updated Civil Code in line with the growth of AI. All measures should aim to address AI challenges and also to provide the needed institutional space for the development of AI and other emerging technologies.     

人工智能产品发展风险抗辩后的损害救济分摊机制研究

我国既要加大人工智能产品的研发和应用力度,最大程度激励人工智能产业发展之巨大潜力,又要预判人工智能产品带来的挑战及其风险。因此,需要根据人工智能产品的特点变革我国产品责任制度,以最大限度防范、降低和合理分配人工智能产品发展风险,尤其是需要完善人工智能产品发展风险抗辩后的损害救济分摊机制。对此,我国必须贯彻"共享—分摊"的损害救济理念,确保在国家、社会、生产者、设计者和消费者之间合理分摊损害,以更好维持各方权益的整体平衡,维护社会主义市场经济秩序并最终促进人工智能产业的高质量发展。     

On the Legal Regulation of Algorithms

Increasingly, algorithms challenge legal regulations, and also challenge the right to explanation, personal privacy and freedom, and individual equal protection. As decision-making mechanisms for human-machine interaction, algorithms are not value-neutral and should be legally regulated. Algorithm disclosure, personal data empowerment, and anti-algorithmic discrimination are traditional regulatory methods relating to algorithms, but mechanically using these methods presents difficulties in feasibility and desirability. Algorithm disclosure faces difficulties such as technical infeasibility, meaningless disclosure, user gaming and intellectual property right infringement. And personal data empowerment faces difficulties such as personal difficulty in exercising data rights and excessive personal data empowerment, making it difficult for big data and algorithms to operate effectively. Anti-algorithmic discrimination faces difficulties such as non-machine algorithmic discrimination, impossible status neutrality, and difficult realization of social equality. Taking scenarios of algorithms lightly is the root cause of the traditional algorithm regulation path dilemma. Algorithms may differ in attributes due to specific algorithmic subjects, objects and domains involved. Therefore, algorithm regulation should be developed and employed based on a case-by-case approach to the development of accountable algorithms. Following these development principles, specific rules can be enacted to regulate algorithm disclosure, data empowerment, and anti-algorithmic discrimination.     

Artificial Intelligence and Technology in Teaching Negotiation

Artificial intelligence (AI), machine learning (ML), affective computing, and big‐data techniques are improving the ways that humans negotiate and learn to negotiate. These technologies, long deployed in industry and academic research, are now being adopted for educational use. We describe several systems that help human negotiators evaluate and learn from role‐play simulations as well as applications that help human instructors teach negotiators at the individual, team, and organizational levels. AI can enable the personalization of negotiation instruction, taking into consideration factors such as culture and bias. These tools will enable improvements not only in the teaching of negotiation, but also in teaching humans how to program and collaborate with technology‐based negotiation systems, including avatars and computer‐controlled negotiation agents. These advances will provide theoretical and practical insights, require serious consideration of ethical issues, and revolutionize the way we practice and teach negotiation.     

Artificial Intelligence and the Public Sector—Applications and Challenges

Advances in artificial intelligence (AI) have attracted great attention from researchers and practitioners and have opened up a broad range of beneficial opportunities for AI usage in the public sector. Against this background, there is an emerging need for a holistic understanding of the range and impact of AI-based applications and associated challenges. However, previous research considers AI applications and challenges only in isolation and fragmentarily. Given the lack of a comprehensive overview of AI-based applications and challenges for the public sector, our conceptual approach analyzes and compiles relevant insights from scientific literature to provide an integrative overview of AI applications and related challenges. Our results suggest 10 AI application areas, describing their value creation and functioning as well as specific public use cases. In addition, we identify four major dimensions of AI challenges. We finally discuss our findings, deriving implications for theory and practice and providing suggestions for future research.