The Ethics Pillar of Trusted AI

There is no one-size-fits-all approach to AI ethics. Find out how to make sure an AI system reflects your organizational values.

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Ethics Is Integral to Trusted AI

AI systems and the data they use can span national borders and diverse cultures. AI becomes embedded in processes with multiple stakeholders and complex social ramifications. High-minded principles will only get you so far. Ethical goals that align with your organizational values, use case, and priorities must be defined. In the dimensions of Trust below, we address privacy, bias and fairness, interpretability and transparency, as well as how to evaluate the impact of your AI system.

Trust Dimensions within Ethics

The four dimensions of AI Trust that support ethics:

Privacy

Though closely related to security, privacy presents unique risks in machine learning beyond the traditional jurisdiction of InfoSec. Find out how AI intersects with privacy and personal data.

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Bias & Fairness

Algorithmic bias has been the subject of much debate in the AI world. Learn what algorithmic bias is, how to identify it, and how to mitigate it to make your AI systems more fair and equitable.

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Explainability

Explainability is a critical element of trustworthiness in AI. Find out about challenges to explaining model behavior and the importance of interpretability and transparency.

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Impact

It’s vital to take the context of a use case into account. Find out how to assess impact across stakeholders and build AI that reflects your values.

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AI Ethics

In this white paper, we tell you how to avoid common blockers to ensure that the AI you build is trustworthy and remains true to your business rules and core values.

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White Paper

AI Ethics

AI holds tremendous promise for many industries. It can improve accuracy, speed, scalability, personalization, consistency, and clarity in so many business functions. But many businesses still hesitate to move forward with AI projects. Why?