Overview

This service focuses on assessing the fairness and equity of AI models used in healthcare. Through demographic performance breakdowns, explainable AI (XAI) tools, and bias detection methods, SMAILE at Karolinska Institutet supports the ethical development of AI tools. The service identifies potential bias in models and helps adjust training datasets or algorithms to promote fairness across age, gender, ethnicity, and other protected attributes.

We provide expertise and support in: • Bias and fairness audits for clinical AI • Performance disaggregation across demographic groups • Explainable AI and equity-by-design principles

How can the service help you?

Many developers are unaware of hidden biases in training data or model outputs. This service reveals fairness issues early and provides actionable diagnostics with tailored mitigation strategies. It helps build stakeholder trust and prepares for future ethical or regulatory scrutiny.

How will the service be delivered?

Primarily delivered virtually. Clients provide access to anonymized datasets and models. SMAILE experts run audits using stratified metrics and XAI tools. Turnaround time: 2–3 weeks.

Additional Information

Provider Description

SMAILE specializes in responsible AI and ethical technology development. We support equity-focused model design and provide tools and strategies to mitigate risks of algorithmic harm in clinical settings.

Technical Description

Bias analysis is conducted using group-specific performance metrics (e.g., sensitivity, specificity, precision), fairness indicators (e.g., demographic parity, equal opportunity), and XAI tools such as SHAP, LIME, and counterfactual testing. The service aligns with fairness frameworks, including IEEE P7003 and the EU AI Act.

Service Customization

Clients can specify target subgroups for analysis. Optional components include bias mitigation training, fairness KPI monitoring, and strategy workshops.