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WedVault#5: Meta vs. ChatGPT Showdown, Ethical AI Guides
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Launching INTELLECT-1: Open-Source Model
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The Future of Cybertrucks.
AI Funding
AI Jobs
A Prompt to Develop Ethical AI Compliance and Risk Mitigation Strategies
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Writer RAG tool: build production-ready RAG apps in minutes
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Integrated into Writer’s full-stack platform, it eliminates the need for complex vendor RAG setups, making it quick to build scalable, highly accurate AI workflows just by passing a graph ID of your data as a parameter to your RAG tool.
AI Resources
Launching INTELLECT-1: Open-Source Model
Releasing INTELLECT-1: We’re open-sourcing the first decentralized trained 10B model:
- INTELLECT-1 base model & intermediate checkpoints
- Pre-training dataset
- Post-trained instruct models by @arcee_ai
- PRIME training framework
- Technical paper with all details— Prime Intellect (@PrimeIntellect)
9:18 PM • Nov 29, 2024
Instant file upload with API
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AI Cheat Sheets
The Future of Cybertrucks
AI Funding💰Raidium raises nearly $17M in funding round. Cradle builds out its protein-design AI platform with $73M in new funding. Blue Bear Capital lands $160M to back AI founders in climate, energy, and industry. | AI Jobs💼Creative Workflow Architect, Sales at Runway Commercial Sales Manager at Glean |
A Prompt to Develop Ethical AI Compliance and Risk Mitigation Strategies
You are an AI ethics and compliance expert. I am looking to develop a framework for my business, [business name], to ensure that our AI systems align with ethical standards and minimize risks associated with AI decision-making. This includes addressing concerns such as bias, transparency, accountability, and privacy while ensuring compliance with relevant regulations.
Please provide a step-by-step guide that includes:
1. Ethical AI Principles and Standards: Explain how to define and adopt core ethical principles for AI development and usage, such as fairness, transparency, and accountability. Include a review of relevant industry standards and frameworks (e.g., IEEE, EU AI Act, or OECD guidelines).
2. Bias Identification and Mitigation: Detail strategies for identifying and mitigating biases in AI models and datasets. Provide actionable steps for ensuring diverse and representative data, conducting fairness audits, and testing AI outputs for unintended discrimination.
3. Privacy and Data Security: Outline best practices for safeguarding customer data and ensuring AI systems comply with data privacy regulations (e.g., GDPR, CCPA). Include methods for implementing secure data handling protocols, anonymization techniques, and consent management systems.
4. Risk Assessment and Monitoring: Describe how to assess potential risks associated with AI decision-making, including ethical risks, operational risks, and compliance risks. Provide steps for setting up monitoring systems to track AI performance and flag potential issues in real-time.
5. Governance and Accountability Structures: Recommend how to establish clear governance frameworks for AI ethics, including appointing AI ethics committees, defining accountability roles, and creating transparent reporting mechanisms for AI operations.
6. Training and Awareness: Provide guidance on training employees and stakeholders about ethical AI practices and compliance requirements. Include tips for fostering a culture of responsibility and ethical awareness across the organization.
7. Continuous Improvement: Suggest methods for periodically reviewing and updating AI systems to align with evolving ethical standards and regulatory changes. Include guidance on gathering stakeholder feedback and incorporating lessons learned into future AI development.
The final output should be structured with clear headings for each section and actionable steps that I can follow to develop a robust ethical AI compliance and risk mitigation strategy. Let me know what specific details or context about my business you need to tailor this guide effectively.
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