Bias Checklist

A guide to identifying and mitigating bias in AI systems within healthcare.

Understanding and Addressing Bias

Bias is a pervasive issue that can affect various aspects of our lives, including the development and deployment of artificial intelligence systems. In the context of AI, bias is not a single concept but a multi-faceted problem that can manifest in data, algorithms, and human decision-making. Recognizing and mitigating different categories of bias is crucial for building fair, equitable, and trustworthy AI.

Social Bias

Social bias occurs when AI models perpetuate and amplify existing societal prejudices, stereotypes, and systemic inequalities. This often originates from the data used to train the models, which reflects historical and societal imbalances.

  • Selection & Representation Bias: This happens when the training data is not representative of the real-world population. For example, a diagnostic AI trained primarily on data from one demographic may perform poorly for underrepresented groups, leading to health disparities [2].
  • Stereotyping: The model may learn to associate certain attributes or outcomes with specific groups based on prevalent stereotypes in the data, leading to unfair recommendations or predictions.

To help address social bias, explore The Upstate Bias Checklist and its accompanying survey:

The Upstate Bias ChecklistREDCap Survey Link

Cognitive Bias

Cognitive biases are systematic patterns of deviation from norm or rationality in judgment, which can be introduced by the humans who design, train, and use AI systems. These mental shortcuts can inadvertently lead to skewed or unfair AI outcomes. For a comprehensive list of over 50 cognitive biases that can affect clinical judgment, see this guide [4].

  • Confirmation Bias: The tendency for developers or users to favor information that confirms their pre-existing beliefs. This can influence how data is selected, how models are interpreted, and which results are considered "correct."
  • Automation Bias: The over-reliance on automated systems, which can lead clinicians to accept incorrect AI suggestions without sufficient critical review. Mitigating this requires targeted interventions, such as cognitive forcing tools [1].

Algorithmic Bias

Algorithmic bias arises from the technical process of building the AI model itself. These are not necessarily reflections of social or cognitive prejudices but are errors introduced by the choice of data, features, or model architecture [3].

  • Measurement Bias: Arises from errors in how data is collected or measured. If a proxy variable is used to represent a real-world outcome (e.g., using healthcare cost as a proxy for health needs), it can introduce bias if the proxy is not equally representative for all groups.
  • Evaluation Bias: Occurs when the evaluation metrics used to assess a model's performance do not represent the desired outcome for all subgroups. A model might have high overall accuracy but perform very poorly for a minority population.

Why Addressing Bias is Critical

Addressing bias in AI is not just a technical challenge but also an ethical and societal imperative. Biased AI systems can perpetuate and amplify existing societal inequalities, leading to unfair outcomes in areas like hiring, loan applications, criminal justice, and healthcare. Building unbiased or less biased AI is essential for promoting fairness, accountability, and transparency.

Tools and Resources

Several tools and resources exist to help identify and mitigate bias in AI systems. These resources provide frameworks, checklists, and methodologies to guide developers and practitioners in building more responsible AI.

  • [1] O’Sullivan ED, Schofield SJ. A cognitive forcing tool to mitigate cognitive bias - a randomised control trial. BMC Med Educ. 2019;19(1):12. View Article
  • [2] Algorithmic Bias in Health Care. Accessed July 4, 2025. View Resource
  • [3] Algorithmic Bias Initiative. The University of Chicago Booth School of Business. Accessed March 2, 2023. View Initiative
  • [4] Croskerry, P. Cognitive Biases. St. John Regional Hospital. View PDF