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Building successful AI that’s grounded in trust and transparency

Chapter 01
2 min read

How to avoid bias and drift while ensuring explainability

Artificial intelligence (AI) has expanded its role in our daily lives and is now virtually everywhere — from our workplaces and smart homes to our ADAS-equipped cars and ubiquitous chatbots. AI is also helping to transform businesses and how people work by automating processes, providing better insights through data, and innovating ways of engaging with customers and employees. Businesses across industries are looking to AI to support human decision making, with 84% of executives anticipating increased organizational focus on AI in the near future.1 AI is quickly becoming necessary for all businesses that wish to stay relevant and able to quickly respond to market disruptions and predict future opportunities.

The global AI market size was valued at USD 27.23 billion in 2019 and is projected to reach USD 266.92 billion by 2027.”2

But all AI is not created equal. For AI to have meaningful impact, businesses must answer the critical question: can we trust it? Businesses need to not only trust the AI models themselves, but also the outcomes they produce, because AI that is either poorly trained, not properly monitored, or unable to be explained can do more harm than good.

Transparent box with data shapes
Trust in AI can only be established when it is fair, transparent and explainable.

Trust in AI can only be established when it is fair, transparent and explainable. In fact, 91% of organizations say it is critical to have the ability to explain how their AI made a decision.3 So how can organizations build trust to tap into the full business value of AI?

It all begins with the right strategy.

1 The business vfalue of AI, IBM, November 2020.