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 in order to embed the necessary practices in the way AI is developed and used.

At the outset, organizations should conduct formal assessments, such as a data protection impact assessment, to identify potential risks (such as bias or inaccuracy) and take appropriate risk mitigation measures, including data minimization techniques and mechanisms for individuals to exercise available rights.

The key to navigating these challenges is to adopt effective practices that do not impede AI innovation, but rather support its business objectives in a viable and privacy-conscious way.

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