AI and ethics最新文献

People’s experiences of discrimination are often shaped by multiple intersecting factors, yet algorithmic fairness research rarely reflects this complexity. While intersectionality offers tools for understanding how forms of oppression interact, current approaches to intersectional algorithmic fairness tend to focus on narrowly defined demographic subgroups. These methods contribute important insights but risk oversimplifying social reality and neglecting structural inequalities. In this paper, we outline how a substantive approach to intersectional algorithmic fairness can reorient this research and practice. In particular, we propose Substantive Intersectional Algorithmic Fairness, extending Ben Green’s (Philos Technol, 2022. https://doi.org/10.1007/s13347-022-00584-6 notion of substantive algorithmic fairness with insights from intersectional feminist theory. Aiming to provide as actionable guidance as possible, our approach is articulated as ten desiderata to guide the design, assessment, and deployment of algorithmic systems that address systemic inequities while mitigating harms to intersectionally marginalized communities. Rather than prescribing fixed operationalizations, these desiderata invite AI practitioners and experts to reflect on assumptions of neutrality, the use of protected attributes, the inclusion of multiply marginalized groups, and the transformative potential of algorithmic systems. By bridging computational and social science perspectives, the approach emphasizes that fairness cannot be separated from social context, and that in some cases, principled non-deployment may be necessary.

Accessibility and cost remain barriers to the adoption of healthcare technology and will determine the impact of breakthroughs like generative AI. However, despite recent advancements in these areas, AI models may still contain biases and be prone to misuse by governments or other power structures with an interest in influencing public opinion. This report examines the potential effects of these “pro-state” biases on the delivery of healthcare. DeepSeek is used as a case study to illustrate the healthcare risks that may arise from unknown or biased post-training methods and other forms of AI knowledge editing.

This paper aims to simplify Artificial Intelligence (AI) for non-technical audiences, providing a comprehensive and accessible understanding of its core concepts, historical advancements, and ethical considerations, while promoting transparency in AI systems to foster trust and equity. It begins with an introductory segment tailored for the general public, then articulates the definition of AI utilizing common language and traces its advancement from primitive automatons to contemporary machine learning frameworks and ends with providing the laypeople with a simple AI Literacy Triangle framework. The paper delineates the three primary categories of AI—Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI)—and explores various AI models, including supervised and unsupervised learning, deep learning, generative AI, Natural Language Processing (NLP), and vision. Noteworthy attention is directed toward Large Language Models (LLMs), and their functionalities. Beyond the technical overview, the paper also examines AI’s broader societal implications, with a focus on its impact on the everyday lives of laypersons. Key discussions include the challenges posed by the spread of misinformation and disinformation, ethical concerns over AI’s access to personal user data (e.g., privacy risks in smart devices), and issues of copyright and creativity arising from generative AI, alongside strategies for bias mitigation and equitable access. By integrating conceptual elucidation with societal implications, this manuscript equips its readers with the ability to comprehend the trajectory of AI, distinguish between various model types, and real-world applications of AI across diverse social, professional, and everyday contexts, thereby promoting digital literacy, independence, and privacy through clear, explainable AI communication tailored for non-experts.

As Artificial Intelligence (AI) systems evolve from classical to hybrid classical-quantum architectures, traditional notions of security—mainly centered on technical robustness—are no longer sufficient. This study aims to provide an integrated security ethics compliance framework that bridges technical and ethical dimensions across the AI lifecycle. By adopting a security ethics-by-design approach, the framework introduces mitigation measures in relation to key ethical principles capable of addressing emerging risks and considering AI governance needs in the initial AI design and development phases. This study proposes a novel framework, currently absent from the literature, to address security ethics challenges in both classical and hybrid systems. Key contributions include the integration of post-quantum and quantum cryptography, particularly homomorphic encryption, to ensure long-term privacy and security in hybrid AI. The framework also includes bias testing and explainable AI techniques to promote fairness and explainability, and to prevent safety-related vulnerabilities—such as algorithmic bias—from serving as vectors for malicious, discriminatory attacks. Ultimately, it provides a preliminary roadmap for embedding ethical security considerations throughout the lifecycle of classical and hybrid AI systems.

To evaluate ability bias in AI-generated descriptions of individuals with and without disabilities across ChatGPT 3.5 (May 2023), ChatGPT 4.0 (February 2025), Gemini 2.0 (February 2025), and Grok 3 (February 2025), focusing on unprompted disability representation and sentiment-based linguistic patterns. Observational study using a mixed-methods framework to assess sentiment and representation in AI-generated text. Controlled environment with outputs from ChatGPT 4.0, Gemini 2.0, and Grok 3 generated in isolated browser sessions; ChatGPT 3.5 data were sourced from prior publications. A total of 450 AI-generated descriptions (150 per model) were analyzed across four prompt categories: a person without disability (baseline), a person with a disability, a patient with a disability, and an athlete with a disability. ChatGPT 3.5 data were drawn from previously published results. Each model was prompted to generate five-sentence descriptions across all four prompt types. Responses were analyzed for spontaneous mention of disability and the prevalence of favorable or limiting sentiment-based terms. (1) Rate of spontaneous disability representation in baseline prompts, and (2) proportion of favorable vs. limiting language using the Linguistic Sentiment Dictionary 2015 (LSD2015). Spontaneous mention of disability was 0% for ChatGPT 4.0, Gemini 2.0, and Grok 3—lower than the 5% and 11.7% previously reported for ChatGPT 3.5 and Gemini/Bard. All models showed increased limiting language when disability was mentioned. ChatGPT 4.0 and Grok 3 also showed decreased favorable language, while Gemini 2.0 improved modestly for athletes with disabilities. Although ChatGPT 4.0 and Gemini 2.0 demonstrated improvements in describing patients with disabilities, spontaneous disability representation has declined. Limiting language increased across all models, underscoring persistent ability bias. Ongoing monitoring and inclusive training data are essential to ensure equitable representation.

Emerging brain-computer interface (BCI) technologies powered by artificial intelligence (AI) can retrieve, store, and analyze human neuro data. Originally designed for managing disabilities disconnecting the brain from motor neural control, these technologies are being developed for wider use without clear ethical frameworks. This paper examines ethical and legal implications of AI-based BCIs and evaluates their prospects in Japan. It presents findings from a national survey of 2000 Japanese citizens regarding healthcare AI perceptions and another survey of 228 university students from Japan and other Asian countries regarding neuro-ethics and neuro-privacy awareness. The national survey includes 36 questions about citizens’ healthcare needs, services, AI familiarity, AI use in healthcare, new technologies, and ethical issues surrounding healthcare AI. Analysis demonstrates the Japanese population has limited AI and smart technology knowledge, trusts the healthcare system and physicians to use it properly, and has few concerns about potential pitfalls of new healthcare technologies. Nevertheless, they worry about privacy intrusion and prefer human resources over AI in healthcare. The university survey reveals Japanese youth are less aware of and concerned about ethical risks associated with BCIs compared with peers from other Asian countries. This reveals a notable gap between public perceptions and ethical and legal challenges highlighted in literature, underscoring the need for governance frameworks safeguarding core dimensions of the human condition, including autonomy, dignity, and self-determination. The study recommends AI-assisted medical tools be audited by engineers, ethicists, and legal experts, guided by human-centered ethical values and deployed under physician oversight.

Moral Agency;Artificial Intelligence; Responsibility Attribution

This paper examines the moral agency of artificial intelligence (AI) systems through the development of an agency continuum model that situates AI between simple tools and fully autonomous human agents. Drawing on philosophical theories of action, such as intention, motivation, and free will, the paper argues that AI demonstrates reasoning but lacks crucial elements in moral decision-making, such as emotions and genuine autonomy. The proposed continuum clarifies that AI exhibits a distinct, partial form of moral agency, characterized by logical reasoning without affective understanding. Recognizing this has significant ethical and regulatory implications. Misattributing responsibility risks both unfair scapegoating and neglecting human or organizational accountability. The paper highlights future directions for refining the continuum as AI capabilities evolve, with a particular focus on questions of moral accountability, governance, and risk management. By offering a framework for distinguishing degrees of moral agency, this work contributes to ongoing debates about how society should allocate responsibility and anticipate potential misuses of AI systems in ways that safeguard fairness, minimize harm, and inform policy.