AI Compliance in GDPR: Navigating the Deployment Stage - Episode 4
In the world of artificial intelligence (AI), systems operate with varying levels of autonomy, influencing physical and virtual environments based on the input they receive. As we move into the deployment phase of the AI development life cycle, it's essential to consider the European Union's General Data Protection Regulation (GDPR) and the upcoming Artificial Intelligence Act (AI Act).
The AI Act and GDPR work hand-in-hand to strengthen data protection throughout the AI lifecycle. The AI Act requires AI providers to disclose key information about their training datasets, enhancing transparency and enabling stronger enforcement of GDPR data protection rules. Simultaneously, GDPR imposes strict rules on data processing legality, purpose limitation, data minimization, transparency, and safeguarding individuals' rights in automated decision-making contexts.
During the deployment phase, AI systems must comply with GDPR principles such as lawful basis for processing personal data, purpose limitation, data minimization, transparency, data subject rights (access, rectification, deletion), and restrictions on automated decision-making. Organizations must provide meaningful information about data processing and automated decisions, enable human oversight, and allow individuals to challenge outcomes.
Establishing processes to address individuals' requests for information, access to their personal data, portability, object, rectification, erasure, restriction, and object is crucial throughout the entire life cycle of an AI system. Regular evaluation of AI model's predictions is necessary through analyzing key metrics and incorporating user feedback. Continuous monitoring of AI models or systems is crucial for maintaining strong performance and GDPR compliance.
Updates or retraining may be necessary if there is a drop in accuracy or performance. No notification is required where the personal data breach is unlikely to result in a risk to the rights and freedoms of the individuals concerned. If that is not the case, the breach must also be communicated to the individuals concerned without undue delay.
Ensuring the continued security of AI is a key measure to ensure ongoing GDPR compliance. To prevent threats, it is important to understand how the model is being used or misused and align that usage with established assessment frameworks. Businesses are required to implement appropriate technical and organizational measures, such as pseudonymization, at both the determination stage of processing methods and during the processing itself for GDPR compliance.
The AI Act introduces legal responsibilities for AI providers to publish basic information on their datasets (including personal data usage), which supports public-facing accountability and aids data subjects and regulators in enforcing GDPR compliance. For example, published summaries can help individuals exercise rights such as data access or support complaints to Data Protection Authorities (DPAs). The AI Act also fosters institutional cooperation by requiring authorities overseeing AI to share information with DPAs, streamlining oversight and reinforcing a rights-based supervisory approach during deployment and other phases.
In conclusion, the AI Act and GDPR work together to ensure that AI deployment respects data protection principles by making AI systems more accountable and by safeguarding individuals’ rights against potential risks created by AI-driven processing and automated decision-making. The European Union's AI Act and GDPR are crucial for AI businesses due to the role of data in AI, and the AI development life cycle encompasses four distinct phases: planning, design, development, and deployment.
The AI Act, in harmonization with the GDPR, necessitates AI providers to reveal crucial information about their training datasets, thereby enhancing transparency and facilitating stronger enforcement of GDPR data protection rules (cybersecurity law, technology). Amongst various principles that AI systems must comply with during deployment, they should provide meaningful information about data processing and automated decisions, foster human oversight, and allow individuals to contest outcomes, all of which align with GDPR's emphasis on data subjects' rights and transparency (cybersecurity law, technology).
