Both the IBM AI Ethics Board and the EU AI Act Compliance Program offer robust frameworks for ethical AI development and deployment. IBM's approach is characterized by its open-source tools and emphasis on transparency, while the EU AI Act Compliance Program prioritizes regulatory compliance and integration with existing governance structures. The EU AI Act Compliance Program provides more explicit details on incident response and remediation procedures.
| Attribute | IBM AI Ethics Board | EU AI Act Compliance Program |
|---|---|---|
| Transparency of ethical guidelines | IBM emphasizes transparency through disclosure, striving to make AI development and deployment open and understandable. They focus on explainability, documentation, and appropriate communication about AI use. IBM adheres to its 'Principles for Trust and Transparency,' which are publicly available. These principles are supported by the 'Pillars of Trust,' which include explainability, fairness, robustness, transparency, and privacy. | Adheres to EU AI Act principles, ensuring safety, transparency, and accountability. Includes creating an AI Ethics Policy and AI governance manual. |
| Scope of ethical considerations (e.g., bias, fairness, privacy) | The Board considers a broad scope of ethical issues, including fairness, bias, privacy, and the overall impact of technology. They address concerns like racial justice, data responsibility, and the ethical implications of facial recognition. | Addresses ethical risks like bias, discrimination, and transparency, as well as privacy, safety, and cybersecurity risks. Ensures AI systems are non-discriminatory and respect fundamental rights. |
| Methodology for ethical risk assessment | IBM uses a structured framework that combines governance structures with practical principles and tools for risk assessment. They have implemented an 'Ethics by Design' methodology to embed ethical risk management into every stage of AI development and deployment. The AI Ethics Board reviews sensitive AI use cases and makes decisions on ethical issues. | Conducts thorough AI risk assessments for each high-risk AI system, evaluating ethical, privacy, safety, reliability, and cybersecurity risks. Frameworks like NIST AI RMF or ISO 31000 can be leveraged. |
| Compliance with relevant regulations (e.g., GDPR, EU AI Act) | IBM closely monitors evolving AI regulations, such as the EU AI Act, and updates its policies accordingly. They advocate for precision regulation of AI and support risk-based approaches to align with regulatory requirements. | Ensures compliance with the EU AI Act, GDPR, and other sectoral standards. Implements an AI governance framework integrating compliance processes, risk management strategies, and monitoring mechanisms. |
| Stakeholder engagement process | IBM engages with diverse stakeholders, including employees, customers, regulators, and civil society organizations, to identify ethical concerns and build trust. They have established an Advocacy Network of ethical technology champions throughout the company. Key stakeholders include the AI Ethics Board, senior leaders from business units and corporate functions, AI Ethics Focal Points, and a grassroots Advocacy Network. Their perspectives are incorporated through various channels, including feedback mechanisms and direct engagement with the AI Ethics Board. | Engages diverse stakeholders, including domain experts, data scientists, ethicists, and impacted business owners, to ensure risks are evaluated from multiple angles. |
| Accountability mechanisms | The AI Ethics Board is a critical mechanism by which IBM holds itself and its employees accountable to its values and commitments to the ethical development and deployment of technology. The Board has the authority to implement, enable, and enforce its decisions. | Establishes clear roles, responsibilities, and oversight mechanisms for AI compliance. Mechanisms should be in place to hold individuals and teams accountable for ethical lapses. |
| Training and education programs for employees | IBM provides AI ethics education to all employees through required annual training and offers opportunities for deeper learning. They also extend training to partners and suppliers. | Provides general AI Act awareness training to leadership and all staff involved in AI projects. Tailors training programs to specific company needs, including basic and specialized sessions. |
| Tools and technologies used for ethical AI development | IBM has developed open-source toolkits, such as AI Fairness 360 and AI Explainability 360, to help detect and mitigate bias and promote transparency in AI systems. They also use FactSheets to document AI services. IBM uses open-source toolkits like AI Fairness 360 and AI Explainability 360, as well as FactSheets for AI services. They also use IBM Watson Studio and IBM Cloud Pak for Data. | Implements data management systems that ensure data integrity, traceability, and security. Uses AI governance platforms to generate real-time reports and maintain accountability throughout the AI lifecycle. |
| Documentation and reporting practices | IBM utilizes FactSheets for AI services to provide standardized information formats and transparency guidance. They create comprehensive records of AI systems and maintain audit trails of system decisions. IBM uses FactSheets for AI services to document ethical considerations and provide transparency guidance. They also create comprehensive records of AI systems and maintain audit trails of system decisions. | Documents how AI models are trained and operate. Automates AI system documentation, monitoring, and reporting to ensure ongoing compliance. |
| Independent audit and review processes | While the sources do not explicitly mention independent audits, the AI Ethics Board reviews AI use cases to ensure they are consistent with IBM's principles and core values. | Conducts regular audits, risk assessments, and impact assessments to identify and mitigate potential risks or biases in AI algorithms or decision-making processes. |
| Incident response and remediation procedures | The sources do not provide specific details on incident response and remediation procedures. | Establishes processes to identify and mitigate potential risks associated with AI systems. Develops mechanisms that allow human intervention in AI decision-making processes to prevent adverse outcomes. |
| Integration with existing governance frameworks | IBM emphasizes aligning its AI ethics framework with existing governance structures and integrating ethics processes with existing workflows. | Integrates AI compliance into existing governance frameworks, such as ISO 27001 or ISO 9001. Maps AI Act requirements to existing controls like data governance policies and model validation processes. |
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