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Businesses can ensure responsible data handling practices that respect individual privacy and promote ethical AI systems by implementing role-based access control and user authentication, protecting sensitive data and algorithms, and auditing data access and usage.
Of course, while there are data privacy risks associated with AI use, it can actually help achieve data privacy compliance. For example, AI can quickly identify sensitive information across a large data ecosystem and ensure that it is data mapped correctly and adequately secured or deleted.
Some examples of privacy invasion by AI include targeted advertising based on personal data, data breaches leading to identity theft, and misuse of personal data by unauthorized entities.
AI technologies are also used for assessing the likelihood of an individual's future criminal behavior, informing decisions related to parole, probation, or release conditions. Machine learning algorithms analyze historical data to identify patterns and risk factors associated with recidivism.
Integrate privacy in AI systems. Collect and retain only the necessary personal data to reduce privacy risks. Implement techniques to protect individuals' identities within the data. Clearly communicate privacy practices and obtain explicit user consent (e.g., through privacy policies or data processing agreements).