Scaled Bias Amplification

• Scaled Bias Amplification is a family of normalized constructs that quantifies how bias increases relative to a domain-specific baseline.
• In causal inference, fairness metrics, and architectural designs, SBA measures vary from scalar ratios to intervention schemes based on residualization and normalization.
• The approach emphasizes that amplification values are only interpretable relative to their chosen baselines, necessitating careful methodological considerations.

Searching arXiv for papers and exact IDs related to “Scaled Bias Amplification” and closely related bias-amplification formulations. Searching arXiv for papers and exact IDs related to “Scaled Bias Amplification” and closely related bias-amplification formulations. Scaled Bias Amplification (SBA) denotes a family of normalized constructs for measuring, comparing, or deliberately controlling bias amplification. Across the cited literature, the term does not denote a single canonical statistic. In causal inference, SBA is defined as the ratio of adjusted to unadjusted unmeasured-confounding bias; in robust learning it appears either as a tunable amplification scheme or as a disparity ratio such as $\mathrm{ADD}=\mathrm{ODD}/\mathrm{EDD}$; in multi-attribute fairness evaluation it is a weighted test-time discrepancy between predicted and actual group proportions within attribute strata; and in structured systems it is operationalized through layer-wise or scale-dependent amplification factors. A distant but formally precise usage also appears in nonlocal-box theory, where a noisy computation recursively scales an initial bias until it becomes constant-sized (Stokes et al., 2020, Li et al., 2023, Subramonian et al., 2024, Dwivedi et al., 3 Sep 2025, Li et al., 10 Apr 2026, Yang et al., 20 Jun 2026, Mori, 2016).

1. Conceptual scope and recurring structure

A common feature of SBA formulations is normalization against a baseline. The baseline is domain-specific: unadjusted causal bias, inherent group-difficulty disparity, dataset-conditioned group proportions, first-layer polarization, or the reliability of a noisy computational primitive. This normalization converts raw bias growth into a comparable quantity, typically answering not whether bias exists, but whether a procedure, architecture, or adjustment decision increases bias relative to a reference level.

The literature separates naturally into two forms. The first is metric-style SBA, in which a scalar score summarizes relative inflation. Examples include the causal ratio

$$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$

and the group-risk ratio

$$\mathrm{SBA}=\mathrm{ADD}=\frac{\mathrm{ODD}}{\mathrm{EDD}}.$$

The second is intervention-style SBA, in which amplification is itself the mechanism of interest. BAM introduces a scaling coefficient $\lambda$ that intentionally amplifies shortcut reliance before corrective reweighting, while multi-agent and recommender-system papers study how bias scales with connectivity, depth, or model size rather than defining a single universal score (Li et al., 2023, Subramonian et al., 2024, Li et al., 10 Apr 2026, Yang et al., 20 Jun 2026).

This heterogeneity has methodological consequences. SBA is not interchangeable across domains, and numerical values are only interpretable relative to the baseline built into the specific formulation. A score of $\mathrm{SBA}>1$ may indicate amplified confounding bias, amplified group-risk disparity, amplified polarization, or amplified popularity concentration, depending on the paper.

2. Causal-inference SBA as normalized confounding inflation

In the causal-inference formulation synthesized from “Causal Simulation Experiments: Lessons from Bias Amplification,” SBA is the ratio of absolute adjusted bias to absolute unadjusted bias under unmeasured confounding. With treatment $X$, outcome $Y$, unmeasured confounder $U$, and adjuster $Z$,

$$\mathrm{Bias}_{\text{unadj}}=\theta\frac{\operatorname{Cov}(X,U)}{\operatorname{Var}(X)}, \qquad \mathrm{Bias}_{\text{adj}}=\theta\frac{\operatorname{Cov}(X,U\mid Z)}{\operatorname{Var}(X\mid Z)},$$

so

$$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$0

The Frisch–Waugh–Lovell view gives the geometric interpretation: conditioning on $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$1 residualizes $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$2, and amplification occurs when projection removes a large component of treatment variation without proportionally removing treatment–confounder alignment. In the instrument-like case $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$3, the covariance term is unchanged while the denominator shrinks, yielding

$$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$4

The same paper extends this residualization logic beyond linear-Gaussian models to broader DAGs, nonlinear additive settings, and binary treatment via a latent-Gaussian construction in which $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$5 is treated as a $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$6 variable and residualized in the linear-probability sense (Stokes et al., 2020).

The causal interpretation is sharply structural. Instrumental-variable-as-covariate adjustment weakly increases bias due to $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$7, with equality only when there is no unmeasured confounding. Proxy adjustment can amplify bias when $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$8 predicts $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$9 but does not fully bridge confounding. Collider adjustment can amplify bias by opening an association between $$\mathrm{SBA}=\mathrm{ADD}=\frac{\mathrm{ODD}}{\mathrm{EDD}}.$$0 and $$\mathrm{SBA}=\mathrm{ADD}=\frac{\mathrm{ODD}}{\mathrm{EDD}}.$$1. The general condition is

$$\mathrm{SBA}=\mathrm{ADD}=\frac{\mathrm{ODD}}{\mathrm{EDD}}.$$2

Attenuation, $$\mathrm{SBA}=\mathrm{ADD}=\frac{\mathrm{ODD}}{\mathrm{EDD}}.$$3, requires conditioning on $$\mathrm{SBA}=\mathrm{ADD}=\frac{\mathrm{ODD}}{\mathrm{EDD}}.$$4 to reduce $$\mathrm{SBA}=\mathrm{ADD}=\frac{\mathrm{ODD}}{\mathrm{EDD}}.$$5 sufficiently more than it reduces $$\mathrm{SBA}=\mathrm{ADD}=\frac{\mathrm{ODD}}{\mathrm{EDD}}.$$6; the exposition describes this as uncommon for variables independent of $$\mathrm{SBA}=\mathrm{ADD}=\frac{\mathrm{ODD}}{\mathrm{EDD}}.$$7 or instrument-like.

The same work turns SBA into a simulation and sensitivity-analysis device. Recommended practice is to vary one structural edge while holding all other edges and marginal variances fixed, renormalizing error terms so that counterfactual simulation arms remain comparable. For $$\mathrm{SBA}=\mathrm{ADD}=\frac{\mathrm{ODD}}{\mathrm{EDD}}.$$8 with $$\mathrm{SBA}=\mathrm{ADD}=\frac{\mathrm{ODD}}{\mathrm{EDD}}.$$9, the parameterization enforces

$\lambda$0

The paper recommends sufficient $\lambda$1 such as $\lambda$2, and explicit reporting of bias, MSE, SBA, and $\lambda$3. In a clinical binary-treatment example constructed from an e-Health intervention RCT, the true covariate-adjusted ACE was $\lambda$4, the naive estimator averaged $\lambda$5 with absolute bias $\lambda$6, and the adjusted estimator averaged $\lambda$7 with absolute bias $\lambda$8, giving an observed $\lambda$9. The paper’s central practical warning is that adjusting for strong treatment predictors with weak outcome relevance can worsen confounding bias rather than reduce it.

3. Amplification as training mechanism and as group-risk ratio

In “Bias Amplification Enhances Minority Group Performance,” the paper does not define a separate SBA metric, but BAM is explicitly a scaled bias-amplification scheme. Stage 1 introduces a learnable auxiliary variable $\mathrm{SBA}>1$0 per example and optimizes

$\mathrm{SBA}>1$1

where $\mathrm{SBA}>1$2 controls amplification intensity. Stage 2 removes $\mathrm{SBA}>1$3, collects the error set

$\mathrm{SBA}>1$4

and continues training with

$\mathrm{SBA}>1$5

Larger $\mathrm{SBA}>1$6 and longer Stage 1 training $\mathrm{SBA}>1$7 strengthen amplification; $\mathrm{SBA}>1$8 controls reweighting strength. The paper also proposes the validation criterion

$\mathrm{SBA}>1$9

and proves that if accuracies of any two groups differ by at most $X$0, then $X$1. On spurious-correlation benchmarks, BAM achieved competitive worst-group accuracy with only a small validation set or without group annotations, and the paper interprets Stage 1 as a mechanism for making minority or hard examples stand out through misclassification (Li et al., 2023).

“An Effective Theory of Bias Amplification” turns SBA into an explicitly normalized disparity metric. For two groups, group risk is

$X$2

with

$X$3

and

$X$4

The paper identifies $X$5 as the relevant scaled amplification quantity. In ridge regression and random-projection models, each group’s risk decomposes as $X$6, with explicit formulas in terms of regularization $X$7, aspect ratios $X$8 and $X$9, group covariances $Y$0, mismatch $Y$1, and label noise $Y$2. The paper reports that amplification can occur even without group imbalance or spurious features, that overparameterized regimes $Y$3 tend to increase amplification as $Y$4, that underparameterized regimes $Y$5 tend toward deamplification, and that near $Y$6 there can be an optimal $Y$7 minimizing amplification (Subramonian et al., 2024).

“A Systematic Study of Bias Amplification” provides a closely related normalization in controlled image classification. For class $Y$8,

$Y$9

$U$0

$U$1

For two groups, the paper’s class-specific directional BiasAmp equals $U$2. Empirically, bias amplification was correlated with model accuracy, model capacity, model overconfidence, and amount of training data; varied greatly during training; and appeared primarily when it was easier to recognize group membership than class membership. Capacity sweeps exhibited a V-shaped relation, with amplification high at both low and very high capacity, and early training often produced large transient spikes (Hall et al., 2022).

4. Test-time SBA under subgroup imbalance, distribution shift, and balanced datasets

In “Multi Attribute Bias Mitigation via Representation Learning,” SBA is a test-time fairness metric designed to separate model-induced bias amplification from train–test distribution shift. For test set $U$3, with target groups $U$4 and attributes or multi-attribute conjunctions $U$5, the paper defines

$U$6

Within-attribute proportions are

$U$7

with gap

$U$8

weight

$U$9

and final score

$Z$0

This construction is test-only: it references actual and predicted group distributions on the evaluation set and therefore avoids conflating model behavior with train–test shift. The paper gives the decomposition

$Z$1

and identifies the first term as the SBA gap. With $Z$2, SBA is bounded in $Z$3. Empirically, on FB-CMNIST the paper reports ERM SBA growing $Z$4 as bias ratio increased, while GMBM remained $Z$5; on CelebA, SBA was $Z$6 for ERM, $Z$7 for BAdd, and $Z$8 for GMBM; on COCO, SBA was $Z$9 for ERM, $$\mathrm{Bias}_{\text{unadj}}=\theta\frac{\operatorname{Cov}(X,U)}{\operatorname{Var}(X)}, \qquad \mathrm{Bias}_{\text{adj}}=\theta\frac{\operatorname{Cov}(X,U\mid Z)}{\operatorname{Var}(X\mid Z)},$$0 for BAdd, and $$\mathrm{Bias}_{\text{unadj}}=\theta\frac{\operatorname{Cov}(X,U)}{\operatorname{Var}(X)}, \qquad \mathrm{Bias}_{\text{adj}}=\theta\frac{\operatorname{Cov}(X,U\mid Z)}{\operatorname{Var}(X\mid Z)},$$1 for GMBM (Dwivedi et al., 3 Sep 2025).

“Making Bias Amplification in Balanced Datasets Directional and Interpretable” does not define SBA, but it clarifies a limitation of many co-occurrence-based amplification metrics, including common scaled variants. When protected attribute $$\mathrm{Bias}_{\text{unadj}}=\theta\frac{\operatorname{Cov}(X,U)}{\operatorname{Var}(X)}, \qquad \mathrm{Bias}_{\text{adj}}=\theta\frac{\operatorname{Cov}(X,U\mid Z)}{\operatorname{Var}(X\mid Z)},$$2 is balanced with task $$\mathrm{Bias}_{\text{unadj}}=\theta\frac{\operatorname{Cov}(X,U)}{\operatorname{Var}(X)}, \qquad \mathrm{Bias}_{\text{adj}}=\theta\frac{\operatorname{Cov}(X,U\mid Z)}{\operatorname{Var}(X\mid Z)},$$3, so that

$$\mathrm{Bias}_{\text{unadj}}=\theta\frac{\operatorname{Cov}(X,U)}{\operatorname{Var}(X)}, \qquad \mathrm{Bias}_{\text{adj}}=\theta\frac{\operatorname{Cov}(X,U\mid Z)}{\operatorname{Var}(X\mid Z)},$$4

co-occurrence-based baselines become uninformative. The paper argues that leakage amplification measures predictability change but cannot determine the direction of amplification, and introduces Directional Predictability Amplification (DPA) as a bounded predictability-based alternative: $$\mathrm{Bias}_{\text{unadj}}=\theta\frac{\operatorname{Cov}(X,U)}{\operatorname{Var}(X)}, \qquad \mathrm{Bias}_{\text{adj}}=\theta\frac{\operatorname{Cov}(X,U\mid Z)}{\operatorname{Var}(X\mid Z)},$$5 On balanced COMPAS and balanced COCO variants, the paper reports $$\mathrm{Bias}_{\text{unadj}}=\theta\frac{\operatorname{Cov}(X,U)}{\operatorname{Var}(X)}, \qquad \mathrm{Bias}_{\text{adj}}=\theta\frac{\operatorname{Cov}(X,U\mid Z)}{\operatorname{Var}(X\mid Z)},$$6 while DPA remains positive, showing that balanced datasets do not preclude model-induced directional amplification. This suggests that SBA definitions tied exclusively to dataset co-occurrence can miss amplification that emerges only after training (Tokas et al., 2024).

5. SBA as architectural scaling in multi-agent systems and recommenders

In “Aligned Agents, Biased Swarm: Measuring Bias Amplification in Multi-Agent Systems,” SBA is operationalized through polarization growth in a directed acyclic graph $$\mathrm{Bias}_{\text{unadj}}=\theta\frac{\operatorname{Cov}(X,U)}{\operatorname{Var}(X)}, \qquad \mathrm{Bias}_{\text{adj}}=\theta\frac{\operatorname{Cov}(X,U\mid Z)}{\operatorname{Var}(X\mid Z)},$$7 of agents. Each agent $$\mathrm{Bias}_{\text{unadj}}=\theta\frac{\operatorname{Cov}(X,U)}{\operatorname{Var}(X)}, \qquad \mathrm{Bias}_{\text{adj}}=\theta\frac{\operatorname{Cov}(X,U\mid Z)}{\operatorname{Var}(X\mid Z)},$$8 outputs a state $$\mathrm{Bias}_{\text{unadj}}=\theta\frac{\operatorname{Cov}(X,U)}{\operatorname{Var}(X)}, \qquad \mathrm{Bias}_{\text{adj}}=\theta\frac{\operatorname{Cov}(X,U\mid Z)}{\operatorname{Var}(X\mid Z)},$$9, where $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$00 is a probability distribution over options and $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$01 is a textual rationale. Impartiality is the uniform distribution $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$02, and the paper uses the Gini coefficient $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$03 as the primary scalar bias measure. It defines local gain

$$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$04

layer-wise average bias

$$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$05

amplification factor

$$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$06

and cumulative factor

$$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$07

“Relative Gini” normalizes the first agent’s average Gini to $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$08. Across chain, spindle, parallel, fully-connected, and iterated fully-connected topologies, the paper reports systemic amplification even when isolated agents appear neutral. In a four-agent sequential chain, for example, Gini rose from $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$09 to $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$10 for GPT-4o and from $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$11 to $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$12 for Qwen-Max; fully-connected topologies often amplified more strongly than simpler chains; and deeper systems exhibited monotonic or near-monotonic rises in Gini. The paper also identifies “Trigger Vulnerability,” where injecting apparently objective context can initiate demographic heuristics that later agents amplify, especially when agents receive full historical rationales rather than only the immediate predecessor’s output (Li et al., 10 Apr 2026).

In “The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenders,” SBA is not a single scalar formula but a scale-dependent pathology of model growth. The paper defines long-tail fairness through

$$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$13

and attributes popularity bias amplification to spectral collapse in the prediction matrix $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$14, monitored by the dominance ratio $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$15. Under identity activations,

$$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$16

so attention aggregation and FFN projections jointly drive low-rank concentration as depth increases. SPRINT mitigates this by regularizing attention column sums and FFN spectral norms: $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$17 The paper reports that scaling SASRec++ on ML-20M from $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$18 to $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$19 parameters improves accuracy but degrades fairness without mitigation, whereas SPRINT yields more favorable scaling behavior and average improvements of $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$20 in accuracy and $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$21 in long-tail exposure across reported datasets. It further reports an empirical overhead of approximately $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$22 per epoch (Yang et al., 20 Jun 2026).

6. Theoretical extremal usage, misconceptions, and methodological limits

In the nonlocal-box literature, SBA has a distinct formal meaning: recursively amplifying a tiny initial bias through noisy computation until it becomes constant. For independent $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$23 with $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$24, the three-input majority function satisfies

$$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$25

so

$$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$26

The amplification is scaled by the success probability of computing $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$27 with nonlocal boxes. Mori shows that the three-input majority function is the unique optimal amplifier under the considered non-adaptive and specific adaptive protocols, and that one cannot improve Brassard et al.’s trivial-communication threshold

$$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$28

within that framework. The quantum maximum remains

$$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$29

Here SBA is neither fairness metric nor causal sensitivity analysis; it is a recursively scaled bias map in communication-complexity theory (Mori, 2016).

Across the broader literature, several misconceptions recur. Bias amplification is not identical to baseline dataset bias: a balanced dataset can still yield directional amplification after training. It is not always harmful in the same operational sense: BAM uses deliberate Stage 1 amplification to improve worst-group performance in Stage 2, whereas causal SBA treats amplification as a warning against harmful adjustment. It is not guaranteed to decrease with architectural sophistication or model scale: multi-agent connectivity, feedback depth, overparameterization, and transformer depth can all increase amplification. Nor is it always observable from standard performance metrics alone: average accuracy may improve while worst-group disparity, polarization, popularity concentration, or confounding bias worsens (Li et al., 2023, Subramonian et al., 2024, Li et al., 10 Apr 2026, Yang et al., 20 Jun 2026).

The main limitations are likewise domain-specific. Causal SBA depends on untestable assumptions about $$\mathrm{SBA}=\frac{|\mathrm{Bias}_{\text{adj}}|}{|\mathrm{Bias}_{\text{unadj}}|}$$30, rank conditions, and correct residualization structure. Predictability-based amplification metrics depend on attacker models and quality equalization. Test-time subgroup SBA requires reliable attribute annotations and may inherit defects in the test reference distribution. MAS amplification studies currently report averages and relative metrics rather than inferential statistics. Recommender analyses rely on spectral assumptions that are theoretically clean but still approximative for full transformer systems. This suggests that SBA is best understood not as a single universal metric, but as a normalized design pattern for asking how much a procedure amplifies bias relative to an explicit baseline, under assumptions that must be stated rather than implied.