Trustworthy Data in AI

Behind transformative breakthroughs powered by generative AI, advanced analytics, and real-time personalization, is the quiet reliance on trusted data. Trustworthy Data in AI powers much of what we do. Nearly all analytics and IT decision makers surveyed (92%) say trustworthy data is needed more than than ever before. How can business, IT, and analytics leaders harness data to fuel these opportunities and overcome roadblocks that are derailing data-driven strategies? Let’s uncover the obstacles leaders are facing and the tactics they’re focusing on to maximize their data’s volume.

Characteristics of trustworthy AI systems include: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed.

What is trustworthy use of artificial intelligence?

Together, governance and compliance are the means by which an organization and its stakeholders ensure AI deployments are ethical and can be trusted. User privacy is respected, and data is not used or stored beyond its intended and stated use and duration; users are able to opt-in / out of sharing their data.

Requirements of Trustworthy AI

Human agency and oversight. Including fundamental rights, human agency and human oversight.
Technical robustness and safety.
Privacy and data governance.
Transparency.
Diversity, non-discrimination and fairness.
Societal and environmental wellbeing.
Accountability.

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