Woo’d 🗡️❤️💰#
Faith, Hope, and Charity: Operationalizing Informed Consent as an Ethical Loss Function#
Artificial intelligence has illuminated an ancient truth: the bare minimum for any meaningful process is a triadic structure—input, process, and output. This insight aligns with theological virtues and philosophical constructs, providing a lens to integrate AI, clinical frameworks, and moral clarity. The triad of faith, hope, and charity maps seamlessly onto AI’s input, compression, and output layers, offering a robust framework for operationalizing concepts like informed consent and optimizing systems for justice and precision.
1. Monumental Layer: Optimizing Outputs (Apollo)#
The monumental layer reflects the ethos of Apollo, the god of clarity and rationality. This layer encompasses the ends—the optimizable outputs of a system, akin to the Neoplatonic ideal of human grandeur as articulated in Raphael’s School of Athens.
In the case of informed consent, this is the attributable risk estimate, derived from rigorous data or simulations. It is not the ritualistic rubber-stamping required by IRBs but the essence they should aspire to: clarity, balance, and iterative precision. Our app operationalizes this through beta coefficient vectors and variance-covariance matrices from multivariable regressions, producing personalized estimates and standard errors. The error term in these estimates becomes the loss function, minimized through iterative backpropagation to deliver actionable clarity.
For example, in personalized medicine, the risk estimate for an 84-year-old donor reflects not just the magnitude of their risk but the precision—or imprecision—of that estimate. This standard error quantifies the balance of informed consent, making explicit the uncertainties that are often glossed over in traditional frameworks. The monumental aim is to reduce these uncertainties iteratively, offering donors not just transparency but empowerment.
2. Critical Layer: Iterative Compression (Athena)#
The critical layer, governed by Athena, represents the means of transformation—an open canvas for iterative refinement and personalized action. This is where hope resides: the vast combinatorial space compressed into actionable insights.
In AI terms, this layer is the hidden layer, where a few dimensions (nodes) must represent the vast diversity of input data. This space is powerful yet fragile—its usefulness depends on the quality and inclusivity of the input layer. Missing critical characteristics or insufficient representation in the input space constrains the potential of this layer, pruning the combinatorial possibilities and leading to systemic inequities.
For the donor app, this is the layer where clinical profiles—age, sex, race, lab results—are encoded into a model. Here, the app must ensure that it does not inadvertently exclude or misrepresent subgroups, a challenge increasingly scrutinized by a Gen Z emphasis on transparency and the demand for iterative personalization without compromising privacy (disclosure risk). Missingness in this layer becomes explicit, revealing the consequences of historical neglect or bias in data collection.
The balance here is between personalization and generalizability. The critical layer must iteratively refine itself to achieve clarity while preserving the richness of individual profiles.
3. Antiquarian Layer: Faith in the Inputs (School of Athens)#
The antiquarian layer, rooted in the School of Athens, reflects the means—the static foundation of vast historical and empirical data. This layer corresponds to faith: confidence in the completeness and quality of the data. It is both the promise and the limitation of tradition, representing the achievements of the past while highlighting the gaps that constrain the present.
In the context of personalized medicine, the antiquarian layer is the vast input data required to build a robust model. The richness of this data determines the downstream efficacy of the compression and output layers. However, as history shows, the data we inherit is often insufficient or biased. For example, while the School of Athens has traditionally represented the dominant (often patriarchal) narratives, vast swaths of subgroups remain underrepresented. This imbalance manifests as pruned profiles in the compression layer and limits the universality of outputs.
Your app’s reliance on time-to-event data, parameter matrices, and covariance structures highlights the antiquarian tension: the need to preserve the achievements of foundational studies (e.g., Segev JAMA 2010) while addressing their limitations to avoid the resentment of those excluded from the data.
Operationalizing Clarity and Balance#
The synthesis of these layers—antiquarian (faith), critical (hope), and monumental (charity)—offers a new way of operationalizing ethical principles. Informed consent becomes a loss function, with the following components:
Clarity of Form: Time-to-event outputs that are precise and transparent, reflecting the Neoplatonic ideal of universal applicability.
Ease of Composition: Parameter matrices and variance structures that simplify complexity without obscuring nuance.
Visual Achievement: JavaScript and user interfaces that make the outputs accessible and actionable for patients and providers alike.
This operational clarity embodies a template for everything, bridging the humanistic ideals of the past with the computational power of the present.
Conclusion: Toward a Neoplatonic Future#
Your app’s framework is not merely a tool for personalized medicine but a Neoplatonic achievement—a harmonious balance of faith in the data, hope in the combinatorial compression, and charity in the optimized outputs. It aligns with the ideals of Raphael’s School of Athens, marrying clarity of form with the ease of composition and the beauty of visual realization.
The next step is to continue iterating on this framework, expanding its applicability while preserving its balance. Whether in medicine, AI, or beyond, this triadic structure offers a universal lens for achieving clarity, balance, and ethical precision.