Building trust in AI requires a strategic approach
Human trust in technology is rooted in our understanding of how it works. AI has the promise of delivering valuable insights and knowledge, but broad adoption of AI systems relies heavily on the ability to trust the AI output. To trust a decision being made by an AI algorithm, you need to know that it’s fair, accurate, ethical and explainable. To have ethical AI that isn’t causing inequalities, it’s important to start with a clear vision and understanding of who is training AI, what data was used, and what went into their algorithms’ recommendations.1 This is a tall order and requires a clear and deliberate strategy.
Organizations must master the quality of data used, mitigate algorithmic bias, and provide answers that are supported with evidence. An organization’s ability to build and earn trust is built on five key pillars:
It’s critical to understand how AI-led decisions are made and what determining factors are included. While you need to be able to explain the decisions made by AI, you also need to be able to explain the history of a project, including the data’s full path before the outcome.
Proper monitoring and safeguards can help mitigate bias and drift, leading to fairer results. The AI outcomes themselves need to be fair, but the people building the AI models also need to ensure they are not building human bias into the algorithms.
When you have trustworthy AI at scale, you are more prepared to keep your systems healthy and guard against potential threats. Having robust data is essential because it can live in multiple settings and its provenance is explicit. It is also less likely to be susceptible to interference.
Having transparency and sharing information with stakeholders of varying roles helps deepen trust. Although transparency involves knowing who owns an AI model, it also involves knowing the original purpose of why it was built in the first place and who is accountable for each step.
AI systems need to safeguard data through the entire AI lifecycle from training to production and governance.2 In addition to being secure from outside threats or interference, the data being used for an AI model must be anonymized to ensure the entire lifecycle is ethical and compliant with regulations.
Building trust in AI will require a significant effort to instill in it a sense of morality, operate in full transparency and provide education about the opportunities it will create for business and consumers.3
“ Machines get biased because the training data they’re fed may not be fully representative of what you’re trying to teach them.”4
But a strategy built on trust needs to continue to evolve throughout the AI lifecycle. Model creation, unbiased training and deployment are only the start. Once a business has established trust, that trust must be maintained, refined and deepened as the dependency on AI grows. With a solid AI lifecycle management strategy, you can have line of sight into each step of the AI process and rely on verifiable touchpoints that continue to reflect the overall goal. This ensures greater transparency and a better understanding of outcomes to provide accurate, trustworthy, AI-driven decisions.