In Business World AI has the potential to be an extremely useful technology that can transform many industries and the lives of people that interact with them.
This type of technology, although beneficial, has not been rolled out on a completely widespread level yet. There are some industries – like finance and business management – where AI is used extensively, especially in areas like process automation and cognitive engagement, but there are many industries that have yet to experience the power of effective AI.
To be successful, AI should be complemented by an AI explainability framework that is designed to make the implementation period smoother and nurture feelings of trust around new technologies in the workplace.
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How does AI explainability work?
AI explainability is, in general terms, the strategies and processes you put into place to make it easier for everyone to understand and use AI in the workplace.
The framework you use will vary greatly depending on the type of business you run and the specific requirements of your staff and customers.
In most frameworks, businesses will choose to focus their attention on who they need to explain new AI models to, how detailed these explanations need to be, and how much of the model needs to be explained. For example, developers and programmers will need to know a lot more information compared to someone who may not be interacting with AI on a daily basis.
Although explaining how AI works, in theory, may sound like a quick and simple task, it’s actually a lot more important than many businesses realise. This is why creating and managing an effective AI explainability framework is so important.
Why and when can explainability be useful?
Explainable AI (XAI) is highly important for many reasons; for keeping businesses to remain compliant, to speed up the adoption process, and to create a culture of trust within the workforce.
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XAI can bridge the gap between teams
The needs of different teams can sometimes cause issues when adopting AI. For example, high-up executives will have a very different approach to managing AI compared to the developers who will be interacting with the technology on a daily basis.
Through an AI explainability plan, you’ll be able to draw up a strategy that includes explaining AI in a way that’s relevant to different groups. You can tell them everything they need to do, how it relates to their role and the opportunities that could present themselves too.
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XAI makes your technology more accessible
In some sectors where AI is not already well-established, like health care and transportation, XAI can help you with the building blocks of getting team buy-in to your new ways of working.
XAI will allow you to build confidence in the models you’re implementing while making it much easier for everyone to understand – regardless of how much interaction they’ve had with AI in the past.
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XAI makes your models more accurate
Not only does XAI make technology easier for your employees, but it will also enhance the accuracy and reliability of the models you are using.
Through enhancing the dataset, XAI will inform you of what parts of the input are generating specific answers. By looking at this information, you’ll be able to see if there are any biases or information which is affecting the end results.
Explainable AI use cases
Explainable AI can be used in a variety of different sectors and organisations to improve operations. Here are a few use cases that illustrate how beneficial XAI can be in a healthcare setting.
To verify and improve models
When you’re working in a healthcare setting, clinical tasks can be complex and have little room for error.
By creating a model that incorporates and elevates a range of predefined properties, AI can be used to detect and correct problems far more quicker than a human would be able to.
Without the usual biases that humans may have or the human error that is evident in intensive decision-making situations, AI can help clinicians come to a more accurate result, faster.
To manage communication and collaboration
Explanations and definitions of things in a healthcare context are extremely important, especially when it comes to having one shared meaning to use throughout a decision-making process.
It is important for employees in healthcare to be able to justify their decisions so they can better communicate and collaborate with their colleagues – to the benefit of the organisation and the patients.
To identify new opportunities and insights
Workers in healthcare can also turn to artificial intelligence to help them identify new opportunities or uncover information and insights that they didn’t already know.
AI can be used for both research and educational purposes which can further knowledge in the industry and be shared with different teams and firms. For example, looking at results from clinical trials to help develop new drugs.
