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responsible machine learning toolkit

Equality AI's toolkit has both open source and hosted solutions to support developers and healthcare systems that wish to monitor and govern the impact of their machine learning models.


Data Quality
AI Fairness
AI System

Equality AI's MLOPS Studio is a workspace for all developers with responsible AI technology baked into the MLOps and AI life cycle.

Research Areas and Topics

Delivery of healthcare

Research has shown that socio-economic status, gender and ethnicity have implicit and explicit effects on how healthcare is delivered, e.g. studies have shown that clinicians are less likely to believe black women when they complain about pain, which translates to less care given to them.

Resource allocation

The use of social variables to predict which inpatients might be discharged earliest might disproportionately allocate resources to patients from richer, predominantly white neighborhoods and away from African Americans in poorer neighborhoods.

Disease Outcomes Prediction

Using historical records of patients to identify at-risk patients might disadvantage minority groups. For example, if too few African American patients were included in the training data to construct the model, the model might be inaccurate for them, which might result in a prediction system under-detects patients in that group.

Clinical trial

The underrepresentation of participants from racial and ethnic minorities and other diverse groups in clinical research is a major concern because people of different ages, races, and ethnicities may react differently to certain medical products.

Patient Segmentation

Patient segmentation analysis that uses big data can help divide a patient population into distinct groups, which can then be targeted with care models and intervention programs tailored to their needs. The use of demographic and socio-economic variables in segmentation analysis may lead to bias against individuals and minority groups.

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