For AI systems to be trustworthy, they often need to be responsive to a multiplicity of criteria that are of value to interested parties. Approaches which enhance AI trustworthiness can reduce negative AI risks. This Framework articulates the following characteristics of trustworthy AI and offers guidance for addressing them.
- Valid and Reliable
- Safe
- Secure and Resilient
- Accountable and Transparent
- Explainable and Interpretable
- Privacy-Enhanced
- Fair -- with Harmful Bias Managed
Creating trustworthy AI requires balancing each of these characteristics based on the AI system's context of use. While all characteristics are socio-technical system attributes, accountability and transparency also relate to the processes and activities internal to an AI system and its external setting. Neglecting these characteristics can increase the probability and magnitude of negative consequences.
Trustworthiness characteristics are inextricably tied to social and organizational behavior, the datasets used by AI systems, selection of AI models and algorithms and the decisions made by those who build them, and the interactions with the humans who provide insight from and oversight of such systems. Human judgment should be employed when deciding on the specific metrics related to AI trustworthiness characteristics and the precise threshold values for those metrics.
Addressing AI trustworthiness characteristics individually will not ensure AI system trustworthiness; tradeoffs are usually involved, rarely do all characteristics apply in every setting, and some will be more or less important in any given situation. Ultimately, trustworthiness is a social concept that ranges across a spectrum and is only as strong as its weakest characteristics.
When managing AI risks, organizations can face difficult decisions in balancing these characteristics. For example:
- Tradeoffs may emerge between optimizing for interpretability and achieving privacy
- Organizations might face a tradeoff between predictive accuracy and interpretability
- Under certain conditions such as data sparsity, privacy-enhancing techniques can result in a loss in accuracy, affecting decisions about fairness and other values
Dealing with tradeoffs requires taking into account the decision-making context. These analyses can highlight the existence and extent of tradeoffs between different measures, but they do not answer questions about how to navigate the tradeoff. Those depend on the values at play in the relevant context and should be resolved in a manner that is both transparent and appropriately justifiable.
Highly secure but unfair systems, accurate but opaque and uninterpretable systems, and inaccurate but secure, privacy-enhanced, and transparent systems are all undesirable. A comprehensive approach to risk management calls for balancing tradeoffs among the trustworthiness characteristics. It is the joint responsibility of all AI actors to determine whether AI technology is an appropriate or necessary tool for a given context or purpose, and how to use it responsibly.