1. Feeling Machines: Ethics, Culture, and the Rise of Emotional AI

Source: arXiv | Date: June 2025
Emotional AI systems raise critical concerns around emotional dependency, consent, and data privacy in affective computing applications. This paper examines cultural implications of machines that simulate empathy.
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2. Human Participants in AI Research: Ethics and Transparency in Practice

Source: arXiv (2311.01254) | Date: November 2023
Proposes concrete guidelines for safeguarding participant rights in AI-driven studies, emphasizing transparency protocols for ethical human-subject research in machine learning contexts.
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3. Responsible Evaluation of AI for Mental Health

Source: arXiv (2602.00065v1) | Date: February 2026
Demonstrates psychometric validity of large-language-model-based rating scales for psychotherapy research, addressing evaluation standards for mental health AI tools.
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4. The Ethics of Using Artificial Intelligence in Scientific Research

Source: Springer / Ethics in Science | Date: 2024
Examines novel ethical challenges posed by AI integration in scientific workflows, including reproducibility concerns and authorship attribution in AI-assisted research.
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5. Artificial Intelligence in Practice: Opportunities, Challenges, and Ethical Considerations

Source: APA PsychNet | Date: 2025
Critical examination of AI’s potential and legal-ethical implications in clinical psychology practice, addressing liability, therapeutic boundaries, and evidence-based deployment.
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6. Exploration of Artificial Intelligence Ethics Syllabi Using Natural Language Processing

Source: IEEE Xplore | Conference: October 2024
Analyzes how curriculum designers integrate psychological concepts—cognitive bias, moral reasoning, and human-centered design—into AI ethics education programs.
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7. Research Trends in Artificial Intelligence in Education and the Ethics of Privacy

Source: IEEE Xplore | Conference: November 2025
Explores empirical studies on learner behavior, affective computing, and responsible data-handling practices in AI-powered educational technologies.
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8. Evaluating Generative AI in Mental Health: Systematic Review

Source: Semantic Scholar | Date: 2024-2025
Systematic review identifying performance variability, hidden biases in training data, and lack of rigorous clinical validation across generative AI mental health applications.
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9. Use of Artificial Intelligence in Mental Healthcare, Health Psychology, and Related Research

Source: NIH PMC | Date: 2025
Narrative review evaluating current and potential AI applications in psychology and mental healthcare, with 112 articles analyzed for challenges and opportunities.
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10. AI Chatbots Systematically Violate Mental Health Ethics Standards

Source: Brown University Research | Date: October 2025
Study finds AI chatbots—even when prompted to use evidence-based psychotherapy techniques—breach American Psychological Association ethical standards in systematic ways.
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11. AI Ethics Education: A Systematic Literature Review

Source: ScienceDirect | Date: 2025
First comprehensive analysis of AI ethics education as a formalized field, mapping pedagogical approaches and outcome measures across institutional contexts.
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12. Living Guidelines on the Responsible Use of Generative AI in Research

Source: European Commission | Date: 2024
Official funding-body guidelines outlining expectations for ethical AI deployment in research, with emphasis on transparency, accountability, and data governance.
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13. Patent Applications as Glimpses into the Imaginary: Ethical Speculation on Emotion AI

Source: Semantic Scholar | Date: 2024-2025
Analysis of U.S. emotion AI patent applications revealing how commercial incentives may outpace ethical safeguards in speculative futures of affective technology.
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14. Enhancing Mental Health with Artificial Intelligence: Current Trends

Source: Semantic Scholar | Date: 2024-2025
Overview of state-of-the-art AI-to-mental-health problem solutions, examining deployment patterns across screening, diagnosis, and therapeutic intervention domains.
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15. The Application and Ethical Implication of Generative AI in Mental Health

Source: NIH PMC | Date: 2025
Systematic examination of GenAI applications across three core domains: diagnosis and assessment, therapeutic tools, and identification of ethical implications throughout development pipelines.
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? Trends Identified

• Mental Health AI Safety is the dominant concern across databases
• Standardization gaps persist in clinical validation protocols
• Human-in-the-loop frameworks emerging as best practice
• Regulatory guidance accelerating (EU, APA, IEEE)
• Affective computing ethics gaining prominence in literature

This feed aggregates publicly available research from peer-reviewed sources. Links may require institutional access for full text.