How to use responsible AI to manage risk

While AI-driven solutions are quickly becoming a mainstream technology across industries, it has also become clear that deployment requires careful management to prevent unintentional damage.  As is the case with most tools, AI has the potential to expose individuals and enterprises to an array of risks, risks that could have otherwise been mitigated through diligent assessment of potential consequences early on in the process.

This is where “responsible AI” comes in — that is, a governance framework that documents how a specific organization should address the ethical and legal challenges surrounding AI. A key motivation for responsible AI endeavors is resolving uncertainty about who is accountable if something goes wrong.

According to Accenture’s latest Tech Vision report, only 35% of global consumers trust how AI is being implemented. And 77% think companies must be held liable for their misuse of AI.

But the development of ethical, trustworthy AI standards is largely up to the discretion of those who write and deploy a company’s AI algorithmic models. This means that the steps required to regulate AI and ensure transparency vary from business to business.

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