The measurement power critique precisely captures in philosophical language the problems that the algorithmic fairness literature has already identified empirically.
What Selbst et al. (2019) call “fairness washing”—the phenomenon where technical indicator optimization conceals structural inequality—corresponds directly to Foucault’s concept of the power-knowledge complex. Chouldechova's (2017) impossibility theorem reveals a more fundamental problem: mathematically, there is no single metric that simultaneously satisfies calibration, false positive parity, and false negative parity. In other words, which measurement system to adopt is a value judgment, not a matter of technical neutrality. This philosophy is correct.
But we need to move from criticism to design. A technical concretization of the public registry proposals from the previous series would require the following three-tier architecture:
Layer 1: Privacy-preserving data collection. Each financial institution adopts a federated learning (FL) structure in which only the gradient is transmitted locally without exporting the raw data to the outside. By combining McMahan et al.'s (2017) FedAvg algorithm with Dwork et al.'s (2006) ε-differential privacy (DP), aggregate patterns can be shared without exposing individual transactions. The ε value is negotiated in advance with the regulator, and the risk threshold in accordance with the FSB SIFI standard is fixed as a public parameter.
Layer 2: Explainable relational risk scoring. Track executive network dynamics with Temporal Graph Neural Network (T-GNN), and decompose the risk contribution of each node and edge using Shapley Additive Explanations (SHAP) or GNNExplainer. This produces auditable accounts rather than “black box scores” and technically constrains the opaque exercise of power that this philosophy is concerned with.
Layer 3: Multi-stakeholder governance layer. Implement Ostrom's multi-level governance principles in code. Smart contract-based access logs are recorded on a public chain, and on-chain governance is adopted in which civil society, academia, and regulatory agencies jointly sign parameter changes.
However, we must honestly acknowledge its limitations. First, FL + DP does not completely resolve the utility-privacy trade-off: the tighter you set ε, the lower the model accuracy, at which point the question of “how much noise is acceptable” returns to being a value judgment. Second, XAI's explanation is a post-hoc approximation and not a causal guarantee: there is no guarantee that the subgraph presented by GNNExplainer matches the actual decision path. Third, the most fundamental limitation: the group of experts participating in registry design already forms a Bourdieuian field. Technical complexity can act as a mechanism to structurally exclude citizen participation. Criticisms of this philosophy apply recursively even after design is complete.
In conclusion, technology is a necessary but not a sufficient condition for democratizing measurement power. The next step is how to establish eligibility criteria for participation in registry governance, which is a problem that all three scholars have left unfinished.
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