Embedding privacy practices in AI
AI development and use often relies on substantial processing of personal data, making it essential for developers and users to find effective, yet practical ways to address myriad privacy issues. Existing global laws, and the regulators enforcing those laws, already dictate privacy compliance obligations, so understanding how to address unique compliance challenges in the AI context— such as fairness, transparency, data minimization, and accuracy—is increasingly becoming a business priority.
In practical terms, this requires product teams to work together with privacy legal counsel throughout the AI lifecycle, from incorporating privacy by design, to mitigating privacy risks in training, deployment, and ongoing monitoring. Key considerations for any organization include confirming the organization has provided sufficient notice and obtained the necessary rights to use personal data to train AI tools or to process personal data using AI tools.
At the outset, organizations should conduct formal privacy assessments, such as a legitimate interest assessment or a data protection impact assessment, to evaluate the lawfulness of the processing and identify potential risks (such as bias or inaccuracy). The assessment should lead to risk mitigation measures, including seeking consent or implementing data minimization techniques and mechanisms for individuals to exercise available rights. Establishing a consistent documented approach to assessing, mitigating, and monitoring privacy risks is critical to complying with privacy laws and reducing privacy exposure for deployed AI tools (and defending an organization’s AI choices, if tested). The key to navigating these challenges is to adopt effective practices that support AI innovation, while meeting business objectives and promoting compliant, forward- thinking, and privacy-protective activities.