Both the Meta AI Ethics Guidelines and the EU AI Act Compliance Program aim to ensure the ethical development and deployment of AI. The EU AI Act provides a more structured, legally enforceable framework with a risk-based approach, while Meta's guidelines offer a broader, more flexible approach applicable to various AI stakeholders. The choice between them depends on the specific context and requirements, with the EU AI Act being mandatory for organizations operating within the EU market.
| Attribute | Meta AI Ethics Guidelines | EU AI Act Compliance Program |
|---|---|---|
| Scope of Ethical Guidelines | Fairness and inclusion, robustness and safety, privacy and security, and transparency and control, as well as mechanisms for governance and accountability. These considerations apply to all AI tools, and their risks should be evaluated through these lenses according to how they will be used. The guidelines are addressed to all AI stakeholders designing, developing, deploying, implementing, using or being affected by AI. | Integrates essential ethical principles to promote sustainable AI, emphasizing respect for human autonomy, harm prevention, fairness, transparency, and accountability. |
| Compliance Framework | Content moderation, balancing safety with expression, considering cultural nuances, and ensuring transparency in enforcement decisions. Structured around five core elements: fairness and inclusion, privacy and security, transparency and control, accountability and governance, and responsible innovation. | Uses a risk-based approach, classifying AI systems into four levels: unacceptable, high, limited, and minimal. Establishes obligations for providers, deployers, importers, distributors, and manufacturers of AI systems with a link to the EU market. |
| Risk Assessment Methodology | Internal risk assessment process to identify potential risks for specific use cases, focusing on how end-users and others could be affected. This can involve surveys, interviews, or market analysis. Impact assessment methodology, ethical evaluation procedures, and implement long-term impact consideration. | Employs a tiered, risk-based approach to regulate AI, aligning rules and obligations with the potential harm posed by AI systems. Assesses the severity of risks to safety, rights, and societal impact. |
| Transparency and Explainability Measures | Transparency to end-users regarding potential risks and limitations of AI systems. Informing users when they are interacting with an AI-powered chatbot. Disclosure approaches for AI use, user communications, documentation standards, explanation mechanisms, and user feedback channels. | Emphasizes transparency and explainability to ensure AI systems are trustworthy and comprehensible. Involves disclosing details about data sources, algorithms, and decision-making processes. |
| Accountability Mechanisms | Clear accountability for AI systems and develops escalation processes for ethical concerns. Documentation requirements and reporting mechanisms for AI oversight. | Ensures accountability through strict enforcement mechanisms, including penalties for non-compliance. National Competent Authorities (NCAs) can impose penalties. |
| Data Privacy and Security Protocols | Robust data protection policies and security protocols, including data encryption, access control mechanisms, and key management practices. Embrace privacy-by-design principles. | Supports data governance by establishing principles like data minimization, purpose limitation, and data quality. Requires AI developers to implement appropriate data governance measures and establish strict security processes. |
| Human Oversight and Control | Maintains human oversight and control over AI systems. | Requires high-risk AI systems to be designed to allow effective human oversight during their use to prevent or minimize risks to health, safety, or fundamental rights. |
| Bias Detection and Mitigation Techniques | Bias testing protocols for AI systems and develops representative data approaches. Mitigation strategies for identified issues and ongoing monitoring for fairness. | Emphasizes the importance of using high-quality, accurate, and unbiased data for training reliable AI models. Provides legal grounds for processing sensitive data solely for bias detection and correction in high-risk AI systems, under specific conditions. |
| Stakeholder Engagement Process | Engages stakeholders to broaden their perspective and create a more inclusive approach to policymaking. Helps them better understand how their policies impact people and organizations. | Emphasizes the importance of stakeholder engagement in addressing technical elements, such as measuring the accuracy and robustness of AI systems. Advocates for multi-stakeholder governance. |
| Independent Auditing and Certification | Undergoes regular compliance audits to ensure adherence to industry standards and regulations. | Certain high-risk AI systems may require external audits and certifications from independent bodies to ensure compliance. ISO 42001 provides an audit-ready AI governance structure. |
| Reporting and Whistleblowing Channels | Provides a variety of channels for people to report concerns, including HR, Employment Law, and an anonymous Whistleblower Hotline. | Providers and deployers must report serious incidents to the relevant authorities promptly. The AI Act establishes the right for individuals to lodge complaints with market surveillance authorities. |
| Training and Awareness Programs | Fosters training and education so that all stakeholders are aware of and trained in Trustworthy AI. | Requires providers and deployers of AI systems to ensure that anyone dealing with the operation and use of AI systems has a sufficient level of AI literacy. ISO 42001 outlines structured education initiatives to ensure personnel meet EU AI Act training requirements. |
| Price | Not available | Not available |
| Ratings | Not available | Not available |