A well-defined framework can help organizations align with privacy regulations by implementing strategies like data minimization and anonymization, which reduce the amount of personal information processed and stored. Data minimization, for instance, involves collecting only the necessary data for specific tasks, while anonymization ensures that personally identifiable information is stripped from datasets. When combined with transparency about data usage, these practices allow organizations to switzerland rcs data manage personal data responsibly, reducing the risk of privacy violations.
As AI becomes more autonomous, determining liability in cases where it causes harm has become more complex. AI’s decision-making process is often opaque, making it challenging to assign responsibility when errors or malfunctions occur. Questions arise about whether liability falls on the developer, the organization deploying the AI, or a third-party provider.
In some cases, product liability laws might apply, holding manufacturers accountable for the harm caused by defective AI systems. However, these laws may not adequately address cases where AI acts independently, and this lack of clarity has led some experts to call for updated regulations specifically designed to address AI-related harm. A robust data governance framework can mitigate liability risks by ensuring thorough testing, regular audits, and adherence to ethical AI guidelines, which then reduce the likelihood of harm and improve accountability within organizations.