Innokas experts have developed a risk bias document to support the tracking of various biases throughout the development journey of AI-powered medical solutions.

This bias documentation serves as a versatile tool designed to monitor potential bias risks in your AI medical solution across different stages of product development. To ensure its effectiveness, it should be used consistently throughout the entire development process.

Below are examples of several development phases, each with relevant bias risks to be aware of.

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1. Data collection plan

In the very beginning, when you plan data collection for the solution, you should look out for coverage bias and Simpson’s Paradox. Coverage bias occurs when the population in datasets does not match the target population. This can lead to AI models that do not generalize well across different populations, potentially exacerbating healthcare disparities. Another risk during this phase is Simpson’s Paradox, where trends may reverse when groups of data are combined. This paradox can mislead researchers and clinicians by presenting contradictory trends depending on how data is aggregated.

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2. Model design

When you move on to designing the model, consider model expressiveness, which refers to the capacity of the model to represent functions accurately. A model with insufficient expressiveness may fail to capture the complexities of medical data, leading to poor performance and inaccurate predictions.

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3. User interface and usability

Finally, when designing the user interface and testing for usability, account for automation bias as it can lead to an over-reliance on automated systems. This bias can cause users to trust AI recommendations even when they are incorrect, potentially resulting in medical misdiagnoses and inappropriate treatments.

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Addressing bias at each stage of AI medical solution development is fundamental for creating fair, accurate, and reliable systems. By being vigilant about potential biases during data collection, model design, and user interface testing, you can significantly mitigate risks and enhance the overall effectiveness and trustworthiness of your AI solutions.

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Use this checklist for directions on what you need to look out for during the development journey.

^{Download the checklist here}

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