Part 1: David Robinson on algorithmic governance in kidney allocation systems
Focus: Democratic oversight of high-stakes medical algorithms and the balance between technical optimization and ethical fairness
Part 2: Dr. Giovanni Parmigiani on race-based adjustments in medical algorithms
Key Issue: The MDRD study controversy and the 2021 transition to race-free kidney function estimation
Medical algorithms require transparent, participatory governance processes that include both technical experts and affected communities to maintain public trust and fairness
Statistical accuracy doesn't automatically ensure ethical fairness - technical optimization can perpetuate or amplify social inequities without deliberate ethical consideration
Using race as a proxy for biological factors like muscle mass opens doors to bias - direct measurement of relevant biological markers provides better outcomes
David Robinson - From Seattle God Committee to Modern Algorithmic Governance
The story begins with Belding Scribner's 1960 invention of the Teflon tube that enabled repeated dialysis. With only four dialysis machines but overwhelming demand, Scribner made a radical choice: medical experts would determine eligibility, but laypeople would make the ethical decisions about who receives treatment.
Description: Belding Scribner invents Teflon tube for repeated dialysis
Impact: Enabled long-term dialysis but created scarcity problem
Description: First lay committee to decide who gets life-saving treatment
Impact: Established precedent for public input in medical rationing
Description: Congress mandates national computer-based allocation system
Impact: Created legal framework for algorithmic organ distribution
Description: Life Years From Transplant system proposed in Dallas
Impact: Sparked debate about age-based allocation and patient advocacy
Making algorithmic decision-making processes visible and understandable
Including affected communities in algorithm design and oversight
Predicting and modeling the distributional effects of algorithmic changes
Ongoing monitoring and evaluation of algorithmic outcomes
Robinson's work exemplifies interdisciplinary scholarship that welcomes diverse perspectives and methodologies. Just as Cornell's founding motto embraces broad access to education, algorithmic governance requires bringing together technical expertise with public input and ethical consideration.
Dr. Giovanni Parmigiani - Statistical Methods and Social Consequences
The Problem: Kidney function assessment requires measuring glomerular filtration rate (GFR), but direct measurement is complex and expensive. The MDRD study developed an equation to estimate GFR from serum creatinine and demographic factors.
The Controversy: Including race as a variable improved statistical accuracy but created systematic differences in care recommendations across racial groups.
Black participants showed higher creatinine levels for same kidney function
Explanation: Race used as proxy for muscle mass differences
Race-based adjustments led to different care recommendations
Explanation: Higher estimated GFR for Black patients = less urgent care
Race as a social construct used for biological prediction
Explanation: Mixed ancestry, self-reporting biases, and cultural variability
New algorithms achieve similar accuracy without race
Explanation: Task force developed race-free GFR estimation methods
Mathematical formulas can obscure the ethical weight of life-and-death decisions
Example: LYFT scores made age-based rationing appear purely technical
System architecture determines which ethical issues get debated
Example: Focus on age allocation overshadowed transplant list access inequities
Public engagement shapes values rather than just measuring them
Example: Clive Graw's advocacy changed how people thought about age fairness
Public participation needs institutional support and data infrastructure
Example: Structured meetings, analysis tools, and public reports enable engagement
Mathematical model relating creatinine levels to kidney function
Binary (yes/no) variables like sex and race
Model accuracy doesn't guarantee social fairness
The MDRD researchers hypothesized that racial differences in creatinine levels reflected differences in average muscle mass, since creatinine is a byproduct of muscle metabolism. This raised the question: if muscle mass is the real biological factor, why not measure it directly?
Student Discussion: Class explored whether replacing race with muscle mass would reduce bias while maintaining predictive accuracy, highlighting the difference between social constructs and measurable biological variables.
How would you feel if your race affected your medical care?
Balance individual care with population-level guidelines
Optimize for accuracy while considering social impact
Create fair systems that maintain public trust
Applying democratic oversight to clinical decision support systems
Extending kidney allocation lessons to other scarce resources
Systematic monitoring of algorithmic outcomes across populations
Evaluating when demographic variables should be included in algorithms
Led by Neil Powell and Cynthia Delgado, a National Kidney Foundation task force developed new GFR estimation methods that achieve similar clinical accuracy without using race. This demonstrates that technical and ethical goals can be aligned through deliberate effort.
Assignment Framework: Students should design studies comparing old and new algorithms, considering data collection, follow-up measures, success metrics, and public explanation strategies across diverse cultural backgrounds.
As Professor Meng emphasized, explaining algorithmic decisions across different cultural backgrounds presents unique challenges. Effective governance requires not just technical transparency but culturally sensitive communication that respects diverse values and worldviews while maintaining scientific integrity.
This session provided a masterclass in the intersection of technical expertise and democratic governance in high-stakes algorithmic systems. David Robinson's account of kidney allocation evolution demonstrated how medical algorithms require both technical sophistication and public legitimacy to function effectively in society.
Dr. Parmigiani's analysis of the MDRD controversy illustrated how well-intentioned statistical methods can perpetuate social inequities when they embed demographic proxies without sufficient consideration of downstream consequences. The 2021 resolution shows that technical accuracy and ethical fairness can be achieved simultaneously through deliberate effort.
The extensive student discussion highlighted the complexity of these issues and the importance of considering multiple perspectives - patients, doctors, data scientists, and policymakers - when designing systems that affect real lives. The session emphasized that effective data science requires both technical competence and social responsibility.