Bias-Aware Manipulation Inference (BAMI)

• Bias-Aware Manipulation Inference is a framework that identifies systematic bias introduced by hidden manipulation mechanisms rather than natural performance drops.
• It employs quantitative measures—such as Aggregated Transform Sensitivity and saliency stratification—to distinguish between symmetric robustness loss and directional inferential shifts.
• BAMI spans multiple domains, from text-to-image systems and explanation pipelines to fairness audits and embodied agents, offering strategies for both detection and mitigation.

Bias-Aware Manipulation Inference (BAMI) denotes a unifying lens for analyzing whether model outputs, detector scores, explanations, or downstream actions are being systematically pushed by bias-bearing or adversarial manipulation channels rather than by the intended task signal. In the literature summarized here, that lens appears in text-to-image systems, image forensics, fairness auditing, explanation pipelines, embodied manipulation, financial LLM inference, and selection-biased statistical analysis; the recurring concern is that a system can remain apparently natural, semantically aligned, or even audit-compliant while a hidden mechanism induces a directional shift in outputs, confidence, or action choice (Huang et al., 2 Apr 2025, Ricker et al., 29 May 2026, Yao et al., 15 Jun 2026). In adjacent statistical work, “bias-aware inference” already means inference that explicitly accounts for possible bias rather than treating it as negligible, which suggests a broader inferential foundation for BAMI (Noack et al., 2019, Armstrong et al., 2020).

1. Conceptual scope and distinction from robustness

A central conceptual contribution comes from the distinction between bias and lack of robustness. In BIAS-ID, detector output is written as $C(x)\in[0,1]$, the score shift under a transformation $T_l$ is

$\Delta_{T_l}(x)=C(x_{T_l})-C(x),$

and class-conditional mean shifts are used to decide whether a nuisance transformation merely weakens evidence symmetrically or instead induces a directional class preference (Ricker et al., 29 May 2026). In that framework, a detector is unbiased with respect to a transformation if the mean shifts are near zero or cancel symmetrically, whereas it is biased when the shifts do not vanish and do not cancel. This makes BAMI more specific than generic robustness analysis: the key question is not only whether performance drops, but whether a nuisance or hidden intervention systematically pushes predictions toward one hypothesis.

The same broadening appears in manipulation detection. “Exploring Saliency Bias in Manipulation Detection” argues that manipulated-region visual saliency and semantic saliency affect both human observers and forensic models, so detector performance is not explained only by pixel-level forensic traces; it also depends on whether the edit occurs in attention-grabbing or semantically central content (Krinsky et al., 2024). In RoboView-Bias, an analogous distinction is made for embodied agents: if grasping success changes under task-equivalent changes in object color, camera viewpoint, or distance, the system exhibits visual bias rather than merely random fragility (Liu et al., 26 Sep 2025). Taken together, these works suggest that BAMI is concerned with directional sensitivity under semantically invariant or stealth-preserving interventions, not simply with aggregate error under perturbation.

A plausible implication is that BAMI should be defined operationally by the presence of a manipulable nuisance factor that preserves enough task semantics to look legitimate while still inducing a systematic inferential skew. That view is explicit in several domains: transformation bias in detectors, saliency bias in forensic benchmarks, and hidden inference-path manipulation in generative or advisory systems.

2. Manipulation channels and threat models

The literature identifies several concrete manipulation channels. In text-to-image diffusion models, the manipulated variable can be the conditional text embedding supplied after the text encoder; in fairness explanation pipelines it can be the background distribution used by SHAP; in fairness audits it can be the empirical distribution of the submitted sample; in autoregressive LLM systems it can be the sampling layer itself; and in embodied agents it can be a supposedly nuisance visual factor such as viewpoint or color that materially changes action success (Huang et al., 2 Apr 2025, Laberge et al., 2022, Lafargue et al., 28 Jul 2025, Yao et al., 15 Jun 2026, Liu et al., 26 Sep 2025).

Domain	Manipulated variable	Representative work
Text-to-image generation	Post-encoder text embedding or embedding direction	(Huang et al., 2 Apr 2025, Roh et al., 2024, Vice et al., 2024)
Explanation / fairness audit	SHAP background sampling	(Laberge et al., 2022)
Fairness compliance audit	Empirical audit-sample distribution	(Lafargue et al., 28 Jul 2025)
Financial LLM inference	Sampling layer during decoding	(Yao et al., 15 Jun 2026)
Embodied manipulation	Camera viewpoint, color, distance	(Liu et al., 26 Sep 2025)

In the implicit bias injection attack against text-to-image diffusion models, the attacker inserts a module immediately after the text encoder, computes a general bias direction

$$v^{diff}=\frac{1}{N}\sum_{i=1}^{N}(v_i^{bias}-v_i^{neu}),$$

and then produces a prompt-conditioned perturbation

$$\tilde v^{diff}=MLP_\theta(Avg(v^{user}))\odot v^{diff}, \qquad \tilde v^{bias}=v^{user}+\tilde v^{diff}.$$

The attack is “plug-and-play,” requires no retraining of the diffusion model, and is intended to preserve prompt alignment while shifting affective, cultural, religious, or gender framing (Huang et al., 2 Apr 2025). A closely related embedding-level attack is FameBias, which replaces a trigger-word embedding by

$$\mathbf{e_r}=\alpha\cdot\mathbf{e}_{w_p}+\beta\cdot\mathbf{e}_{w_t},$$

thereby making benign prompts containing a chosen trigger noun repeatedly depict a specific public figure (Roh et al., 2024). “Manipulating and Mitigating Generative Model Biases without Retraining” makes the same family of channels explicit in vector-algebraic form by shifting prompt embeddings along centroid directions between classes or demographic attributes (Vice et al., 2024).

Outside generation, the manipulated object can be the reference set or audit sample rather than the model input. “Fool SHAP with Stealthily Biased Sampling” shows that a model owner can reduce the attribution of a sensitive feature by manipulating the sampling of background points used to approximate SHAP expectations, while leaving the model itself unchanged and preserving simple output-space plausibility checks (Laberge et al., 2022). “Exposing the Illusion of Fairness” studies a related problem for regulatory fairness audits: an auditee can alter the empirical distribution of the submitted sample so that Disparate Impact appears compliant, using entropic or optimal-transport projections that remain close to the original data distribution (Lafargue et al., 28 Jul 2025). In financial advisory LLMs, the paper on invisible manipulation channels moves the attack one layer deeper: the attacker compromises autoregressive token sampling, boosts a target token set only when tokens are already plausible, and thereby biases opinions while preserving watermark validity and evading black-box detectors (Yao et al., 15 Jun 2026).

3. Quantitative criteria and observable signatures

Several papers provide quantitative observables that naturally function as BAMI diagnostics. BIAS-ID introduces Aggregated Transform Sensitivity (ATS). For a transform family $T$ with tested levels $L$,

$$\text{ATS}_T(X_R,X_F)=\frac{1}{|L|}\sum_{l\in L}\left|\overline{\Delta}_{T_l}(X_R)+\overline{\Delta}_{T_l}(X_F)\right|,$$

which summarizes whether a nuisance transformation induces a net classward drift rather than symmetric evidence degradation (Ricker et al., 29 May 2026). This is a direct template for manipulation inference: compare original and intervened scores, average by class, and quantify whether the intervention systematically favors one hypothesis.

For manipulation detection, saliency stratification produces another family of observables. The saliency-bias study bins examples into five manipulation-saliency groups and evaluates detector AuROC within each bin, while semantic change is tracked with top1 overlap, top5 overlap, top5 IoU, and top5 probability change computed from CLIP-based concept predictions (Krinsky et al., 2024). The key empirical pattern is that detection performance rises with saliency and that more visually salient manipulations tend to produce larger semantic change. In BAMI terms, this yields a “high detectability / high saliency / high semantic shift” signature that can reveal benchmark or model shortcut dependence.

For embodied agents, RoboView-Bias formalizes visual bias through a context-averaged coefficient of variation,

$$CV_{SR}(V_i)=\mathbb{E}_{c\in C_{\text{Gen}(V_i)}}[CV(V_i\mid c)],$$

and interaction effects through

$T_l$0

These metrics quantify not only single-factor bias but also asymmetric coupling, such as viewpoint strongly amplifying color-related bias (Liu et al., 26 Sep 2025). A plausible implication is that BAMI should treat some factors as bias amplifiers, not merely nuisance covariates.

At the sample level, AIM defines a credibility-weighted bias score

$T_l$1

with $T_l$2 measuring within-group credibility and $T_l$3 obtained from a comparability graph plus random walk with restart (Liu et al., 2024). This turns BAMI into an attribution problem over specific training samples rather than only over aggregate rates.

In hidden-channel LLM attacks, the relevant observable is distributional distinguishability. If $T_l$4 is the manipulated token distribution and $T_l$5 the native one, the paper gives a KL-based upper bound and a sample-complexity relation

$T_l$6

showing that output-only detection becomes impractical when the induced divergence is sufficiently small (Yao et al., 15 Jun 2026). This is a particularly sharp BAMI result: even strong directional bias can be hard to infer if it is injected sparsely enough at the inference layer.

4. Inference workflows across data, models, and audit pipelines

A recurring BAMI workflow begins with a controlled intervention, then compares system behavior before and after the intervention, and finally asks whether the observed shift is better explained by intended task signal or by a bias-bearing mechanism. In BIAS-ID this workflow is explicit: apply a transformation to the input image, run the detector’s original preprocessing after the intervention, compute image-wise score shifts, average by class, and aggregate across transformation levels into ATS (Ricker et al., 29 May 2026). The same pattern generalizes to manipulation inference more broadly.

In text-to-image systems, several papers recommend comparing matched prompts across clean and suspected systems, across paraphrases, or across deployment branches. The implicit bias injection paper argues that a BAMI system can look for a prompt-conditioned but directionally consistent shift in output valence, unusually high cross-domain consistency, and an unusually high “large affect shift / small semantic drift” ratio (Huang et al., 2 Apr 2025). FameBias adds more explicit inference signals: repeated trigger-target coupling, identity concentration under semantically unrelated prompts, prompt-embedding inconsistency, token-sensitive discontinuities under paraphrase, and abnormal recurrence of a celebrity face when the prompt does not mention a public figure (Roh et al., 2024). These are natural BAMI observables because they connect a hidden conditioning-pathway intervention to stable, signed output asymmetries.

For explanation pipelines, the workflow is reference-distribution auditing. Global Shapley values are defined as

$T_l$7

so changing the background distribution $T_l$8 changes the explanation even if the model is untouched (Laberge et al., 2022). The relevant BAMI inference question is therefore not only whether a sensitive feature has low attribution, but whether that attribution is stable across independently sampled honest backgrounds. Low sensitive-feature SHAP attribution that depends heavily on the supplied background set is evidence for a manipulated explanation channel rather than genuine model impartiality.

For fairness audits, the workflow becomes distributional hypothesis testing. “Exposing the Illusion of Fairness” formulates the attacker’s problem as

$T_l$9

with $\Delta_{T_l}(x)=C(x_{T_l})-C(x),$0 chosen as KL divergence or Wasserstein distance, and then compares the submitted audit sample against the reference distribution using multiple discrepancy tests (Lafargue et al., 28 Jul 2025). This yields a canonical BAMI task: infer whether a seemingly compliant sample is an honest draw or the result of a fairness-constrained projection of the original empirical distribution.

A more general inferential version appears in the selection-bias paper. There the selected-data likelihood

$\Delta_{T_l}(x)=C(x_{T_l})-C(x),$1

is built directly into the simulator, and amortized neural posterior estimation is trained on the selected outputs rather than on the latent full data (Arruda et al., 20 Apr 2026). This suggests a broader BAMI principle: if the manipulation can be represented as a simulator-level observation operator, it can be treated as part of the inference model rather than as an external nuisance.

Training-time BAMI appears in BAM, AIM, and Bias Mimicking. BAM deliberately amplifies shortcut learning using per-sample auxiliary variables, then infers minority or hard samples from the resulting error set and reweights them in a second stage (Li et al., 2023). AIM attributes unfairness to specific samples and supplies witness examples through a credibility-weighted local bias score (Liu et al., 2024). Bias Mimicking directly manipulates the empirical joint distribution by enforcing matched $\Delta_{T_l}(x)=C(x_{T_l})-C(x),$2 across classes, so that $\Delta_{T_l}(x)=C(x_{T_l})-C(x),$3 and $\Delta_{T_l}(x)=C(x_{T_l})-C(x),$4 become statistically independent in the sampled training data (Qraitem et al., 2022). These are all BAMI-style procedures because they infer harmful bias structure from controlled manipulations of training dynamics or sampling distributions.

5. Mitigation and defense strategies

The mitigation literature is heterogeneous, but several recurring patterns are clear. The first is bias-aware evaluation and dataset alignment. In BIAS-ID, semantically aligned training plus content-based augmentation is exemplified by B-Free, which emerges as the most consistently least biased detector across JPEG, WebP, resizing, rotation, and grayscale interventions, although the paper stresses that no detector is completely unbiased (Ricker et al., 29 May 2026). The saliency-bias study reaches a related conclusion from a different angle: aggregate AUROC on saliency-skewed datasets is misleading, so evaluation should be stratified by manipulation saliency and semantic relevance rather than reported only in aggregate (Krinsky et al., 2024).

A second pattern is targeted data intervention. AIM proposes AIM-Delete, which removes the top-$\Delta_{T_l}(x)=C(x_{T_l})-C(x),$5 highest-bias samples, and AIM-Aug, which augments around low-bias samples through neighborhood mixup; both are intended to mitigate group and individual unfairness with minimal or zero predictive utility loss (Liu et al., 2024). Bias Mimicking provides a simpler sampler-level intervention by constructing class-conditioned resampled datasets in which $\Delta_{T_l}(x)=C(x_{T_l})-C(x),$6 is mimicked across classes, thereby removing the label–bias dependence that drives shortcut learning (Qraitem et al., 2022). BAM takes yet another route: it deliberately amplifies bias in a first stage, uses the failure set as a proxy for minority or hard samples, and then continues training on a reweighted dataset (Li et al., 2023). These methods differ in mechanism, but all treat bias as something inferable at the sample or subgroup level and then correctable through sparse data or weighting changes.

A third pattern is explicit semantic grounding before action or generation. In RoboView-Bias, the Semantic Grounding Layer (SGL) rewrites underspecified instructions into attribute-grounded commands and reduces visual bias by approximately $\Delta_{T_l}(x)=C(x_{T_l})-C(x),$7 on MOKA (Liu et al., 26 Sep 2025). In text-to-image systems, the implicit bias injection paper recommends conditional pathway auditing: compare raw text-encoder outputs with the actual conditioning tensors consumed by the generator, and search for undocumented post-encoder transformations or stored bias directions (Huang et al., 2 Apr 2025). This suggests that mitigation in generative systems often requires control of the conditioning path, not only output filtering.

A fourth pattern is inference-stack hardening. The paper on invisible sampling-layer manipulation reports that software defenses, including cryptographically secure pseudorandom number generators, are ineffective against a compromised sampling layer, whereas QRNG combined with TEE hardware isolation achieves 100% attack blocking and reduces the target rate to the natural baseline (Yao et al., 15 Jun 2026). That result is unusually strong: it implies that some BAMI threats are fundamentally infrastructural and cannot be solved by output auditing alone. A related, but negatively framed, result appears in FameBias: Unified Concept Editing can erase the target figure and drive Bias Success Rate to $\Delta_{T_l}(x)=C(x_{T_l})-C(x),$8, but at the cost of $\Delta_{T_l}(x)=C(x_{T_l})-C(x),$9 Trigger Fidelity Rate and often meaningless outputs, illustrating the steep trade-off between attack removal and utility preservation (Roh et al., 2024).

Finally, proactive protection can itself become part of BAMI. TAFIM adds quasi-imperceptible perturbations to authentic face images so that downstream face-manipulation systems collapse to predefined targets such as solid white, blue, or red images, with compression robustness obtained through differentiable JPEG approximation and multi-method protection obtained through attention-based perturbation fusion (Aneja et al., 2021). Although not a fairness paper, it shows that a defense can create structured, model-specific failure modes that are themselves amenable to manipulation inference.

6. Limitations, controversies, and open problems

The literature repeatedly emphasizes that BAMI is not solved by a single detector, metric, or fairness criterion. BIAS-ID is explicit that its intervention-based methodology only addresses manipulable biases tied to factors that can be changed while preserving label; it does not address content bias, and it does not provide formal significance tests, confidence intervals, or a statistical ranking procedure (Ricker et al., 29 May 2026). “Exposing the Illusion of Fairness” likewise shows that fairness audits can be gamed through minimal-distance sample manipulations, but the strongest detection protocols assume that the supervisory authority can access the original full dataset or a trusted reference distribution (Lafargue et al., 28 Jul 2025). This suggests that BAMI often depends on trusted baselines that may be unavailable in practice.

Another recurring limitation is dependence on explicit group or attribute labels. AIM assumes binary labels and binary sensitive attributes, and its sample-bias score depends on an observed sensitive attribute and a comparability graph (Liu et al., 2024). Bias Mimicking also requires full knowledge of bias-group labels during training and is not designed for overlapping or intersectional sensitive attributes (Qraitem et al., 2022). BAM relaxes this somewhat by permitting no training group labels and, in the hardest setting, no group labels at all, but its group-free mode still relies on the empirical relationship between class imbalance and worst-group accuracy and therefore remains heuristic rather than identified (Li et al., 2023).

A further difficulty is separating inherent bias from injected manipulation. The implicit bias injection paper notes that a base model may already have a positivity tendency, so comparative auditing against a trusted checkpoint or across deployment replicas is needed to distinguish “model is naturally positive” from “model was intentionally made more negative/positive” (Huang et al., 2 Apr 2025). The financial invisible-channel paper sharpens this point: if the KL divergence between manipulated and native output distributions is sufficiently small, then output-only detection can require impractically large sample sizes, even when directional bias has been materially amplified (Yao et al., 15 Jun 2026). A plausible implication is that BAMI must combine output analysis with provenance, pathway, or hardware attestation.

Finally, there is no universal representation of “manipulation.” Some works study selection and censoring, others study post-encoder embedding perturbations, others sampling-layer corruption, others saliency or viewpoint dependence, and others sample-level unfairness in training data. Taken together, these works suggest that BAMI is best understood not as a single algorithmic family but as a research program with a common question: which hidden variable, transformation, or selection operator is systematically steering inference away from the intended signal, and how can that steering be quantified, attributed, and mitigated without mistaking ordinary uncertainty or natural heterogeneity for manipulation?