Dimensions

DIMENSIONS OF TRUST IN AI

Trustworthy AI results from how people, processes and technologies function together across multiple dimensions of trust. Not every dimension is pertinent for every organization or cognitive tool. Rather, the dimensions of trust are lenses for interrogating AI design, function, and outcomes. With regular activities, decisions and documentation across the AI lifecycle, weighing and addressing the dimensions of trust is what permits effective AI governance and unleashes AI’s greatest potential value. These dimensions are explored in depth in Trustworthy AI, by Beena Ammanath.

Fair and Impartial

As we deploy machines that can perform analysis and even make decisions on our behalf, we require confidence that the machine intelligence is fair and impartial in its outputs. The challenge of course is that AI cannot “think” or “reason.” The onus falls on human stakeholders to probe how an AI functions relative to expectations for fairness and concerns over bias.

Robust and Reliable

When AI outputs are inconsistently accurate, the result is uncertainty, and when models function as intended only within narrow and limited use cases, trustworthiness can suffer in other applications. Data scientists are challenged to build provably robust, consistently accurate AI models in the face of changing real-world data, and by this, two vital compon…

AI Privacy

There is growing awareness worldwide around how AI application can impact the privacy concerns of citizens, organizations and governments. Privacy in AI relates to guarding sensitive information, gaining consent for use of that data, ensuring models do not divulge protected data, using models in a way that respects privacy, and meeting emerging laws and regulations around…

Safe and Secure

Our trust in powerful AI that can change every industry requires systems that can be secured against a variety of threats, many of which are not yet imagined, much less manifest. The path forward requires an awareness of how AI tools may become compromised, the implications from it, and plans and processes to keep security at the forefront of strategy…

Transparent and Explainable AI

A cross-cutting dimension that impacts all other aspects of ethical AI is transparency. It permits accountability, motivates explainability, reveals bias, and encourages fairness. With sufficient transparency, datasets are understood, algorithms can be traced back to their training data, and deployed solutions are understood to be accurate (or not).

Accountable and Responsible

Despite their power and potential, AI tools are still just mathematical models. What happens when an AI makes a decision with a negative impact on an individual or organization? The model itself cannot face any real consequence. Instead, accountability for AI and responsibility for deciding when and how it should be used is a uniquely human…

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