Understanding AI's Impact on Privacy: Guidance

Artificial Intelligence (AI) holds the potential to revolutionize our lives, making us more efficient, productive, healthier, and innovative. This groundbreaking technology is already being harnessed across both private and public sectors to leverage data for improved forecasting, better products and services, cost reduction, and relieving workers from routine administrative tasks.

However, like any emerging technology, AI comes with its own set of risks. The widespread and unregulated use of AI raises significant concerns about its impact on human rights and personal privacy, particularly with generative AI (GenAI). GenAI uses deep-learning algorithms and powerful foundational models that train on massive amounts of unlabeled data to produce AI-generated outputs.

This paper delves into the privacy implications of widespread AI adoption, aiming to uncover what this means for businesses and outline key steps organizations can take to use AI responsibly. By understanding the privacy implications of AI and proactively mitigating risks, companies can leverage AI's power while safeguarding individual privacy.

1. Understand Your Regulatory Environment and Adopt an AI Privacy Strategy

Legislators, policymakers, and regulators emphasize aligning AI systems with recognized standards. It is crucial to identify applicable regulatory frameworks for your business, determine which ones to comply with, and plan your AI deployment accordingly. Establish a baseline for AI usage that satisfies various regimes and streamline your AI development or AI-related activities accordingly.

2. Incorporate Privacy by Design into Your AI Projects

Address privacy impacts and compliance issues at the ideation stage and throughout the AI lifecycle through systematic privacy impact assessments (PIA) or data protection impact assessments (DPIA). Privacy by Design, as outlined in the ISO 31700 Privacy by Design Standard and Carbon GRC Privacy by Design Assessment Framework, can help integrate privacy into AI systems.

Even if your system uses anonymized or non-personal data, privacy risks like re-identification from training data sets can emerge. A thorough assessment should include security and privacy threat modeling across the AI lifecycle and stakeholder consultations. Consider broader privacy issues such as data justice and indigenous data sovereignty.

3. Assess AI Privacy Risks

Evaluate privacy risks associated with developing in-house AI solutions or using public models trained on public data. Ensure these models adhere to AI and ethical standards, regulations, best practices, and codes of conduct (e.g., NIST, ISO, regulatory guidance). This applies whether you are developing or acquiring and integrating an AI system.

If you are a client, request documentation from the developer to support their PIA and related AI privacy risk assessments and conduct your own assessments. In jurisdictions like the UK and the EU, a PIA/DPIA is a legal requirement and should incorporate AI considerations, focusing on the necessity and proportionality of data collection and consent.

4. Audit Your AI System

As an AI system developer or third-party vendor, ensure your clients and regulators that you have built trustworthy AI. One way to do this is through audits against recognized standards, regulatory frameworks, and best practices, including an algorithmic impact assessment.

For instance, test the AI system using scripts that simulate real-world scenarios to gather user feedback, ensuring its effectiveness, reliability, fairness, and overall acceptance before deployment. Explain what data was used, how it was applied, and how end users can contest or challenge AI-based automated decision-making to prevent biased outcomes.

5. Respect Rights and Choices Through Explainability and Transparency

Be prepared to address questions and manage preferences of individuals impacted by your AI systems. Organizations using AI for automated decision-making should be able to explain in plain language how AI affects their end users.

Explainability involves articulating why an AI system reached a particular decision, recommendation, or prediction. Develop documented workflows to identify and explain data usage, its application to end users, and how users can contest or challenge AI-driven decisions.

By following these guidelines from Carbon GRC, organizations can responsibly adopt AI, balancing innovation with the imperative to protect individual privacy.