EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients.
How EY can Help
-
Our Consulting approach to the adoption of AI and intelligent automation is human-centered, pragmatic, outcomes-focused and ethical.
Read more
Having met these conditions for AI confidence, the organization can now action the next layer of checks and balances.
To truly achieve and sustain trust in AI, an organization must understand, govern, fine-tune and protect all of the components embedded within and around the AI system. These components can include data sources, sensors, firmware, software, hardware, user interfaces, networks as well as human operators and users.
This holistic view requires a deeper understanding of the unique risks across the whole AI chain. We have developed a framework to help enterprises explore the risks that go beyond the underlying mathematics and algorithms of AI and extend to the systems in which AI is embedded.
Our unique “systems view” enables the organization to develop five key attributes of a trusted AI ecosystem:
- Transparency: From the outset, end users must know and understand when they are interacting with AI. They must be given appropriate notification and be provided with an opportunity to (a) select their level of interaction and (b) give (or refuse) informed consent for any data captured and used.
- “Explainability”: The concept of explainability is growing in influence and importance in the AI discipline. Simply put, it means the organization should be able to clearly explain the AI system; that is, the system shouldn’t outpace the ability of the humans to explain its training and learning methods, as well as the decision criteria it uses. These criteria should be documented and readily available for human operators to review, challenge and validate throughout the AI system as it continues to “learn.”
- Bias: Inherent biases in AI may be inadvertent, but they can be highly damaging both to AI outcomes and trust in the system. Biases may be rooted in the composition of the development team, or the data and training/learning methods, or elsewhere in the design and implementation process. These biases must be identified and addressed through the entire AI design chain.
- Resiliency: The data used by the AI system components and the algorithms themselves must be secured against the evolving threats of unauthorized access, corruption and attack.
- Performance: The AI’s outcomes should be aligned with stakeholder expectations and perform at a desired level of precision and consistency.
Those organizations that anchor their AI strategy and systems in these guiding principles and key attributes will be better positioned for success in their AI investments. Achieving this state of trusted AI takes not only a shift in mindset toward more purposeful AI design and governance, but also specific tactics designed to build that trust.