Meet Ian, an IT specialist at an e-commerce platform company. He often found himself in situations where he needed to justify a model’s predictions to stakeholders but lacked the tools to clearly explain the reasoning behind them. The predictions, though accurate, seemed like a black box.
Diane is a data analytics expert at the same firm. She had the most advanced AI models at her disposal, predicting future sales trends, identifying customer behaviors, and even forecasting stock prices. Yet she hit a wall each time she tried to explain how these models arrived at their conclusions. She was clearly “lost in translation”!
The woes of Ian and Diane resonate with many of us. How much better would your decisions be if you could just understand and easily communicate what’s driving these predictions? Without understanding how they’re making decisions, it’s hard to trust the machine learning models we deploy. There’s a need to demystify these predictions.
In This Post
Explainable AI charts to the rescue
The explainable AI charts introduced in our fall 2023 release allow us to see the reasoning behind a machine learning model’s predictions. Not only does this make the prediction process more transparent, but it also empowers users to make more informed decisions based on those predictions.
Let’s see how this feature can make your work life (and your decisions) exponentially better.
Why do we need explainable AI charts?
The AI black box conundrum
The main objective is to demystify machine learning predictions by offering a visual representation of the factors that influence each prediction. It allows users to get a holistic view right from Vizpads without shifting between models and charts.
AI can often be seen as a black box, where inputs go in and outputs come out without a clear understanding of the decision-making process. These explainability charts bridge that gap by visually presenting the influencing factors behind each prediction.
Bring in actionable insights
By understanding the underlying reasons for each prediction, you can make decisions that are data-informed. Now, when your IT ops team asks about risk mitigation or when you’re thinking of making a strategic change, explainable AI charts will be your trusted ally. They help you build trust in a model’s outputs and support better decision-making.
Additionally, by understanding the key influencers in predictions, teams can strategize better. If they know certain variables heavily sway a model’s prediction, they can focus their efforts on optimizing those areas.
Tell me how it works
In the Vizpad module, alongside the familiar options of bar charts and KPI charts, there’s a new option: “Explainable AI chart.” There, you can select a machine learning model that was run previously, along with a relevant Business View.
The user interface clearly segments the insights into two primary sections. One section has a table that showcases all the columns from the Business View, giving users the flexibility to select specific rows of interest. This ensures that users have full control over which data points they want to explore. The main section offers a detailed visual breakdown of the factors influencing the predicted value for the chosen row.
Making AI transparent (and user-friendly!)
Here, color-coded bars intuitively display the positive and negative influences, making it instantly clear which factors play pivotal roles in the predictions. This juxtaposition of raw data and visual insights ensures that users get not only a holistic view of their data, but also the underlying reasons behind each prediction, thereby promoting trust, transparency, and informed decision-making.
This is all good. But how will it help Ian and Diane?
Back to Ian and Diane. With the AI explainability chart at his disposal, Ian can now delve deep into the reasoning behind every prediction. If a stakeholder questions the prediction of a particular product’s sales for the next quarter, Ian can walk them through the factors the model considered, explaining the significance of each one. No more black-box predictions; everything is laid out clearly.
For Diane, the explainable AI charts turned AI’s predictions into intuitive visuals. Diane can now see the weightage of each factor, understand its influence, and explain it to anyone, no matter their technical background. Instead of just presenting the predictions, she can now understand the “why” behind them:
“Sales will spike in December because of X, influenced by Y, and amplified by Z.”
From black boxes to transparent insights
The AI explainability chart isn’t just a fancy new feature—it’s a tool of empowerment. It demystifies the world of machine learning predictions, making them accessible and easily understood. For Ian, Diane, and countless others, this means more confidence in their models, better strategic decisions, and a smooth experience communicating these predictions to others. The future of machine learning is not just smarter models, but models we can understand.
For more, check out a virtual walkthrough of this feature (and more) in our Tellius fall 2023 release webinar.
