Both the Partnership on AI's Framework and UNESCO's Recommendation offer valuable guidance for responsible AI development. The Partnership on AI's framework is more geared towards practical implementation within organizations, while UNESCO's Recommendation provides a broader, policy-oriented approach for member states. The choice between them depends on the user's specific needs and context.
| Attribute | Partnership on AI's Framework for Responsible AI Development | UNESCO's Recommendation on the Ethics of AI Implementation |
|---|---|---|
| Scope of ethical guidelines | Addresses important questions concerning our future with AI, advancing positive outcomes for people and society. Covers Inclusive Research and Design, AI and Media Integrity, Fairness, Transparency, and Accountability, and Public Policy. | Addresses ethical issues related to AI within UNESCO's mandate, offering a framework of values, principles, and actions for responsible AI development and deployment. Aims to guide societies in dealing with the impacts of AI technologies on humans, societies, and the environment. |
| Specificity of recommendations | Develops resources like 'Guidance for Safe Foundation Model Deployment' and guidelines for collaboration between AI practitioners and stakeholders, particularly from marginalized communities. | Provides policy action areas to translate core values into action, with guidance on data governance, gender equality, and AI applications in various sectors. Offers tools like the Readiness Assessment Methodology (RAM) and Ethical Impact Assessment (EIA) to support implementation. |
| Industry applicability | Designed to guide enterprise organizations in responsible AI adoption across sectors, including media, industry, academia, and civil society. | Provides ethical guidance to all AI actors, including the public and private sectors. |
| Coverage of human rights | Emphasizes human rights frameworks for protecting data enrichment workers and ensuring responsible AI data supply chains, prioritizing the rights of the most vulnerable. | Emphasizes the protection of human rights and dignity as the cornerstone of AI ethics. Addresses potential impacts on various rights and freedoms, including freedom of expression, privacy, and non-discrimination. |
| Emphasis on transparency and explainability | Stresses transparency and disclosure in generative AI, recommending developers be transparent about the technology's capabilities, limitations, and potential risks. | Highlights transparency and explainability as essential for ensuring respect for human rights and ethical principles. Recognizes that transparency is necessary for liability regimes and challenging decisions based on AI outcomes. |
| Focus on fairness and non-discrimination | Aims to ensure AI systems are equitable and minimize discrimination, especially for marginalized communities, avoiding biases that could lead to discriminatory outcomes. | Promotes social justice and safeguards fairness and non-discrimination in compliance with international law. Emphasizes inclusive access to AI benefits, considering the needs of diverse groups. |
| Guidance on accountability mechanisms | Emphasizes the need for accountability in AI development and deployment, promoting traceability in relation to datasets, processes, and decisions made during the AI system lifecycle. | Calls for appropriate oversight, impact assessment, audit, and due diligence mechanisms to ensure accountability for AI systems. Emphasizes the need to attribute ethical and legal responsibility for AI systems to physical persons or legal entities. |
| Inclusion of stakeholder engagement | Recognizes the urgent need for an inclusive approach to AI development, actively involving people most affected by the technology. Develops guidelines to foster collaboration between AI practitioners and stakeholders from marginalized communities. | Emphasizes the importance of engaging all stakeholders, including businesses, in the implementation process. Promotes multi-stakeholder dialogue and consensus-building on ethical issues related to AI systems. |
| Practical implementation support | Provides resources such as guidelines, frameworks, and tools to support the practical implementation of responsible AI practices, including a 10-step guide for AI adoption in newsrooms. | UNESCO has developed practical methodologies like the Readiness Assessment Methodology (RAM) and Ethical Impact Assessment (EIA) to assist member states in implementing the Recommendation. |
| Adaptability to different AI applications | Designed to be adaptable to different AI applications and industries. | The framework is designed for adaptability across cultural, legal, and socioeconomic contexts worldwide. |
| Consideration of environmental impact | Recognizes AI's potential climate risks and promotes the responsible development of AI in all contexts, including climate action. | Emphasizes the need to assess the direct and indirect environmental impact of AI systems throughout their life cycle. Encourages compliance with environmental laws, policies, and practices. |
| Monitoring and evaluation frameworks | Recommends monitoring and evaluating the effectiveness of ethical AI practices. | Directs member states to monitor and evaluate policies, programs, and mechanisms related to AI ethics. Suggests using a combination of quantitative and qualitative approaches, with broad stakeholder participation. |
| Price | Not available | Not available |
| Ratings | Not available | overall: Not available, performance: Not available |