Group 1 - The report emphasizes the importance of establishing a technology ethics governance system that aligns with China's national conditions and international trends, promoting a culture of "technology for good" [4][8][9] - It highlights the need for agile governance to strengthen early warning, tracking, and analysis of ethical risks in technology, allowing for timely adjustments to governance methods and ethical standards [6][8] - The report calls for a multi-stakeholder approach to governance, advocating for open cooperation and collaboration to form consensus and synergistic effects [8][9] Group 2 - The report outlines key principles of technology ethics, including enhancing human well-being, respecting the right to life, and ensuring fairness and justice in scientific activities [10][11][12] - It stresses the importance of risk control, advocating for objective assessment and cautious handling of uncertainties and risks associated with technology applications [12][13] - The report emphasizes the need for transparency and openness in technology innovation activities, ensuring reasonable participation of stakeholders and the public [13] Group 3 - The report proposes improvements to the government’s technology ethics management system, establishing a National Technology Ethics Committee as the highest advisory and regulatory body [14][15] - It outlines the responsibilities of innovation entities, such as educational institutions and research organizations, to establish ethical review committees and manage ethical risks in their activities [15][16] - The report encourages the role of scientific and technological communities in promoting ethical self-discipline and raising public awareness of technology ethics [16] Group 4 - The report calls for refining technology ethics norms and standards, particularly in critical fields like life sciences and artificial intelligence, to guide compliant scientific activities [19] - It emphasizes the need for a robust regulatory framework for technology ethics, including clear responsibilities and improved processes for ethical review and risk management [19][20] - The report advocates for the establishment of a legal framework to enhance accountability for violations of technology ethics [20][24] Group 5 - The report highlights the importance of technology ethics education, making it a compulsory part of undergraduate and graduate programs to instill ethical awareness in future scientists [25][26] - It promotes the standardization of ethics training for technology personnel, ensuring they adhere to ethical requirements in their research and innovation activities [26][27] - The report encourages public engagement in technology ethics discussions, advocating for effective communication between scientists and the public to address ethical challenges [28]

Mild rise of polysilicon prices amid supply cut – The average market price of rod-type polysilicon rose 2-3% from Rmb36.5-40.6/kg to Rmb37.2-41.7/kg in January, while that of granular silicon also edged up 3% from Rmb38/kg to Rmb39/kg in the month, per price data from the China Silicon Industry Association. According to the Association, PRC monthly polysilicon output dropped 43.4% yoy and 6.6% mom to 970k MT in January. Most polysilicon suppliers are actively fulfilling the commitments of industry self-regu ...

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Core Viewpoints - The draft national standard aims to enhance the security of generative AI services by addressing cybersecurity issues, with a primary focus on preventing AI systems from generating content deemed offensive by the Communist Party, such as pornography, bullying, hate speech, defamation, copyright infringement, and criticism of the Party's monopoly on power [1][12] - The standard provides comprehensive security requirements for generative AI services, covering training data security, model security, and security measures, and is applicable to service providers conducting security assessments and relevant regulatory authorities [38][39] Scope and Overview - The document outlines the basic security requirements for generative AI services, including training data security, model security, and security measures, and provides security assessment requirements [38] - It aims to help service providers establish a cybersecurity baseline for generative AI services and improve service security levels by addressing key issues such as cybersecurity, data security, and personal information protection throughout the service lifecycle [46] Training Data Security Requirements - Data source security: Service providers must conduct security assessments of data sources before collection and verify data after collection, rejecting sources with over 5% illegal or unhealthy information [48][49] - Data content security: Training data must be filtered for illegal and unhealthy information before use, and intellectual property rights must be managed to avoid infringement risks [62][63] - Data annotation security: Annotators must undergo internal security training, and annotation rules must be detailed to ensure data accuracy and safety [68][71] Model Security Requirements - Model training: The safety of generated content should be a primary evaluation metric during training, and regular security audits of development frameworks and code are required [75][76] - Model output: Technical measures should be implemented to improve the accuracy and reliability of generated content, and models should refuse to answer questions that induce illegal or unhealthy information [78][79] - Model monitoring: Continuous monitoring of model inputs is necessary to prevent malicious attacks, and a standardized monitoring and evaluation system should be established [81] Security Measures Requirements - Service applicability: The necessity, applicability, and safety of generative AI services in various fields must be fully demonstrated, with additional security measures for critical scenarios such as medical and financial services [87] - Service transparency: Information about service applicability, scenarios, and purposes should be disclosed prominently, and user input collection for training purposes should be optional and easy to disable [88][91] - Public and user complaints: Service providers must provide channels for public and user complaints and establish rules and timelines for handling them [93] Appendices - Appendix A lists major security risks related to training data and generated content, including violations of socialist core values, discriminatory content, commercial violations, and infringement of legal rights [99][100][102][104] - Appendix B provides key points for security evaluation, including the construction of keyword libraries, test question banks for generated content, and classification models for filtering and evaluating security risks [108][109][114]

Issue Brief Key Concepts in AI Safety Reliable Uncertainty Quantification in Machine Learning Authors Tim G. J. Rudner Helen Toner ...