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How to Design a Privacy Risk Framework for AI Systems
- The proposed PIA-AIA framework acts as a guide for organizations to manage privacy risks and ensure algorithmic accountability, integrating PIA with AIA for a more holistic view of AI's impact on personal data and human rights.
- The framework incorporates principles from COBIT 2019, focusing on governance and information & technology management practices.
- Privacy frameworks like FIPP, GAPP, Privacy by Design (PbD), and HCER-AI emphasize protecting data, governance, transparency, and ethical AI development.
- PIA assesses privacy impact in projects involving personal data, while AIA evaluates social impact of algorithms, focusing on fairness, transparency, and accountability.
- The PIA-AIA framework reframes privacy and algorithmic assessments as part of a continuous risk management cycle, ensuring ongoing oversight and governance.
- Integration of PIA and AIA helps organizations align privacy and AI efforts with legal requirements and business goals, leading to improved user trust and operational optimization.
- The dynamic and iterative risk management approach within the framework addresses emergent risks and involves stakeholder engagement for transparency and accountability.
- The framework includes phases such as understanding the I&T environment, privacy threshold analysis, context establishment, risk assessment, risk mitigation, communication, consultation, monitoring, and review.
- Risks are classified into ethical, performance, and implementation categories, allowing for clearer accountability, response strategies, and strategic prioritization.
- A use case involving AI for early prediction of type 2 diabetes risk demonstrates the application of the framework in a primary care setting.
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