Building Fair AI in Healthcare: Turning Data Into Equitable Decisions
This article was adapted from The Analytics Edge In Healthcare written by Dimitris Bertsimas, Ph.D., Agni Orfanoudaki, Ph.D., Holly Wiberg, Ph.D.
Artificial intelligence (AI) and machine learning (ML) are transforming healthcare. Predictive models can flag high-risk patients, optimize scheduling, and even guide organ allocation. But in medicine, the goal isn’t just to predict outcomes – it’s to make better, more ethical decisions that improve lives.
One of the most pressing challenges in this space is fairness. If an algorithm performs worse for certain patient groups, or if it systematically disadvantages people based on race, gender, or socioeconomic status, the consequences can be severe. At H2O, we help healthcare leaders with our AI platform that is not only powerful but also equitable, ensuring the technology supports fair access to care.
Why Fairness in AI Matters
Data-driven models learn from historical data. Healthcare data often reflects long-standing inequities. If underserved patients are underrepresented or have limited access to care, models may misinterpret their risk or deprioritize them for interventions. For example:
A readmission risk model trained on data from large urban hospitals may underperform in rural populations.
A transplant allocation algorithm could unintentionally favor patients who historically had better access to care.
Such biases are rarely intentional, but without careful design, they can be amplified by advanced analytics.
Our Framework for Fair, Data-Driven Decisions
Our AI platform balances predictive accuracy, operational efficiency, and fairness. Our approach integrates two powerful disciplines:
Fair Machine Learning – building models that account for differences across protected groups (such as race or gender) and applying fairness definitions that fit the clinical context.
Optimization for Equitable Resource Allocation – using mathematical optimization to make fair decisions once predictions are available.
This two-step strategy lets healthcare organizations predict wisely and act justly.
1. Building Fair Predictive Models
Bias can enter the AI pipeline at many points:
Data Collection: Underserved groups may be underrepresented or have missing data. We use sampling and reweighting techniques to make models more representative.
Feature Selection: Variables can inadvertently encode bias (e.g., socioeconomic proxies). We carefully audit and adjust inputs.
Model Training: We incorporate fairness constraints or reweight loss functions to reduce disparities across groups.
No single fairness definition fits every use case. Sometimes we want equal prediction accuracy across groups; other times we want equal opportunity (e.g., equal chance of identifying patients who would benefit from intervention). We help clinical and operational teams choose the right fairness goals for their mission.
2. Making Fair Decisions with Optimization
Even a fair model can lead to unfair outcomes if the decision-making layer isn’t designed thoughtfully. Optimization is a branch of applied mathematics that we use to turn predictions into fair, resource-efficient actions.
For example, in organ transplantation, there’s a fixed supply of donor kidneys and thousands of patients in need. Traditional allocation rules aim to maximize life years gained but may inadvertently disadvantage certain groups. We use optimization models that balance:
Efficiency: Maximize total quality-adjusted life years (QALY) gained.
Equity: Ensure fair access across race, age, or other protected attributes.
This lets policymakers quantify trade-offs. For example, understanding how slightly reducing efficiency can dramatically improve fairness.
Case Study: A Fairer Kidney Allocation Policy
End-stage renal disease affects over 500,000 Americans, and nearly 100,000 are waiting for a kidney transplant at any given time. Organs are scarce, so allocation policy is critical and ethically complex.
Using historical patient and donor data, Dynamic Ideas, the parent company of H2O, designed a new kidney allocation policy by:
Predicting Benefit: Estimating life years gained for each patient-organ match using ML models.
Optimizing Allocation: Solving a mathematical optimization problem to balance total life years gained with fairness constraints.
Creating a Scoring System: Translating the optimization results into a transparent, points-based system for policy use.
Result: The proposed policy improved life years gained by 8% compared to existing standards – while maintaining similar fairness across key demographic groups.
Importantly, this approach is flexible. Policymakers can adjust fairness definitions or weight different objectives to fit clinical and ethical priorities.
Beyond Kidneys: A Blueprint for Ethical AI in Healthcare
While the case study focused on kidney transplantation, the methodology applies broadly:
Prioritizing preventive interventions (e.g., early cancer screenings).
Equitably distributing scarce resources such as ICU beds, vaccines, or specialty care slots.
Other organ allocations (heart, liver, lung).
By combining ML predictions with optimization-driven decision design, healthcare leaders can achieve data-informed fairness at scale.
What This Means for Healthcare Leaders
C-Suite Executives: Understand the strategic trade-offs between efficiency and equity to shape responsible AI strategy.
Hospital Operators: Design workflows that integrate fair predictions into allocation and scheduling decisions.
Clinicians: Gain trust in algorithms that support – not undermine – equitable care delivery.
Fair AI isn’t just a regulatory checkbox; it’s a competitive advantage and an ethical imperative. Organizations that get this right will lead in trust, quality, and long-term patient outcomes.
Our Expertise
At H2O, we bridge cutting-edge data science with healthcare’s ethical and operational realities. Fair machine learning and optimization helps organizations turn complex data into actionable, equitable outcomes. Whether you’re modernizing clinical decision-making, improving resource allocation, or ensuring compliance with fairness standards, we can help.