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Social Aspects · Accountability, Transparency, and Interpretability

Yilin Zhang, Cai Xu, You Wu, Ziyu Guan, Wei Zhao

Real-world deployments inevitably encounter distribution shifts, rendering the confidence estimates of deep neural networks highly unreliable, posing severe risks in safety-critical applications. Existing methods improve calibration via training-time regularization or post-hoc adjustment, but often rely on access to (or simulation of) target domains, limiting practicality. We propose Frequency-aware Gradient Rectification (FGR), a target-agnostic training framework for robust calibration. From a frequency perspective, FGR applies low-pass filtering to a subset of training images to diminish spurious high-frequency cues and encourage the learning of domain-invariant features. However, the associated information loss can degrade In-Distribution (ID) calibration. To resolve this trade-off, FGR treats ID calibration as a hard constraint and rectifies conflicting parameter updates via geometric projection. This ensures a first-order non-increase in the ID calibration objective without introducing additional weighting hyperparameters. Extensive experiments on synthetic, real-world, and semantic shift datasets demonstrate that FGR significantly improves calibration under diverse shifts while preserving ID performance, and it remains compatible with post-hoc calibration methods.

Social Aspects · Accountability, Transparency, and Interpretability

Silpa Vadakkeeveetil Sreelatha, Dan Wang, Serge Belongie, Muhammad Awais, Anjan Dutta

Text-to-image diffusion models achieve impressive generation quality but inherit and amplify training-data biases, skewing coverage of semantic attributes. Prior work addresses this in two ways. Closed-set approaches mitigate biases in predefined fairness categories (e.g., gender, race), assuming socially salient minority attributes are known a priori. Open-set approaches frame the task as bias identification, highlighting majority attributes that dominate outputs. Both overlook a complementary task: uncovering rare or minority features underrepresented in the data distribution (social, cultural, or stylistic) yet still encoded in model representations. We introduce RAIGen, the first framework, to our knowledge, for unsupervised rare-attribute discovery in diffusion models. RAIGen leverages Matryoshka Sparse Autoencoders and a novel minority metric combining neuron activation frequency with semantic distinctiveness to identify interpretable neurons whose top-activating images reveal underrepresented attributes. Experiments show RAIGen discovers attributes beyond fixed fairness categories in Stable Diffusion, scales to larger models such as SDXL, supports systematic auditing across architectures, and enables targeted amplification of rare attributes during generation.

Social Aspects · Accountability, Transparency, and Interpretability

Keying Kuang, Iain Carmichael, Elizabeth Purdom

Saliency maps are widely used to interpret image classification models and build trust in their predictions; however, their reliability remains a central concern, as randomized networks can produce saliency maps that closely resemble those of trained models. We identify a previously underappreciated architectural contributor to this phenomenon: a *center-focused saliency bias* induced by common convolutional design choices. Through controlled ablations, we show that this bias arises from architectural components such as zero padding and receptive field growth, and persists even in randomly initialized convolutional neural networks (CNNs) and under randomized inputs. In contrast, this behavior is largely absent in non-convolutional architectures such as Vision Transformers (ViTs) and multilayer perceptrons (MLPs). To investigate the interaction between architectural priors and learning, we introduce a corner-shift benchmark and a Center-Shift Index that quantify how saliency redistributes under object relocation. We show that training can partially shift saliency toward object regions, while randomized models remain dominated by architectural priors, helping explain the previously observed similarity between trained and random saliency maps and clarify how architectural priors can confound standard saliency evaluations.

Social Aspects · Accountability, Transparency, and Interpretability

Zirui Li, Xuefeng Bai, Kehai Chen, Yizhi Li, Jian Yang, Chenghua Lin, Min zhang

Latent or continuous chain-of-thought methods replace explicit textual rationales with a number of internal latent steps, but these intermediate computations are difficult to evaluate beyond correlation-based probes. In this paper, we view latent chain-of-thought as a manipulable causal process in representation space by modeling latent steps as variables in a structural causal model (SCM) and analyzing their effects through step-wise $\mathrm{do}$-interventions. We study two representative paradigms (i.e., Coconut and CODI) on both mathematical and general reasoning tasks to investigate three key questions: (1) which steps are causally necessary for correctness and when answers become decidable early; (2) how influence propagates across steps and relates to explicit CoT; (3) how to characterize and interpret the influence patterns revealed by (2). Across settings, we find that latent-step budgets should be treated as distinct functionalities rather than homogeneous extra depth, We further show that training/decoding should account for a gap between early output bias and late representational commitment. These results motivate mode-conditional and stability-aware analyses as more reliable tools for interpreting and eventually improving latent reasoning systems.

Social Aspects · Accountability, Transparency, and Interpretability

Min Jae Song, Kameron Shahabi

We introduce ideal attribution mechanisms, a formal abstraction for reasoning about attribution decisions over strings. At the core of this abstraction lies the ledger, an append-only log of the prompt-response interaction history between a model and its user. Each mechanism produces deterministic decisions based on the ledger and an explicit selection criterion, making it well-suited to serve as a ground truth for attribution. We frame the design goal of watermarking schemes as faithful representation of ideal attribution mechanisms. This novel perspective brings conceptual clarity, replacing piecemeal probabilistic statements with a unified language for stating the guarantees of each scheme. It also enables precise reasoning about desiderata for future watermarking schemes, even when no current construction achieves them, as the ideal functionalities are specified first. In this way, the framework provides a roadmap that clarifies which guarantees are attainable in an idealized setting and worth pursuing in practice.

Social Aspects · Accountability, Transparency, and Interpretability

Robin Hesse, Simone Schaub-Meyer, Janina Hesse, Bernt Schiele, Stefan Roth

Explainable artificial intelligence (XAI) aims to provide human-interpretable insights into the behavior of deep neural networks (DNNs), typically by estimating a simplified causal structure of the model. In existing work, this causal structure often includes relationships where the presence of a concept is associated with a strong activation of a neuron. For example, attribution methods primarily identify input pixels that contribute most to a prediction, and feature visualization methods reveal inputs that cause high activation of a target neuron – the former implicitly assuming that the relevant information resides in the input, and the latter that neurons encode the presence of concepts. However, a largely overlooked type of causal relationship is that of encoded absences, where the absence of a concept increases neural activation. In this work, we show that such missing but relevant concepts are common and that mainstream XAI methods struggle to reveal them when applied in their standard form. To address this, we propose two simple extensions to attribution and feature visualization techniques that uncover encoded absences. Across experiments, we show how mainstream XAI methods can be used to reveal and explain encoded absences, how ImageNet models exploit them, and that debiasing can be improved when considering them.

Social Aspects · Accountability, Transparency, and Interpretability

Yuexin Li, Wenjie Qu, Linyu Wu, Yulin Chen, Yufei He, Tri Cao, Bryan Hooi, Jiaheng Zhang

Existing sentence-level watermarking methods enhance robustness to paraphrasing by anchoring watermarks in sentence semantics. However, their prefix-based designs remain vulnerable to structural perturbations, such as sentence splitting and merging, which commonly arise under strong paraphrasers like DIPPER and GPT-3.5. To mitigate this issue, we propose AliMark, a framework that reformulates sentence-level watermarking as a bit sequence encoding and alignment problem between a potentially watermarked text and a secret bit sequence. Notably, our approach adopts a two-stage detection strategy: we generate multiple restructured text variants and adaptively align their extracted bit sequences with the secret bit sequence to minimize alignment cost. This multi-candidate alignment design naturally improves robustness to sentence merges and splits. Extensive experiments demonstrate that AliMark substantially outperforms state-of-the-art baselines under diverse paraphrasing attacks.

Social Aspects · Accountability, Transparency, and Interpretability

Dawood Wasif, Terrence Moore, Chang-Tien Lu, Jin-Hee Cho

Federated learning enables on-device training without centralizing data, yet existing systems still struggle to provide explanations that are both locally faithful and globally consistent under strict privacy and bandwidth constraints. Prior approaches either keep explanations siloed across clients, transmit heavy or sensitive artifacts, or replace expressive task models with interpretable surrogates that sacrifice accuracy. We propose xFedAlign, a model-agnostic framework that decouples task optimization in parameter space from explanation coordination in a compact group space. Each client distills a lightweight surrogate to produce private, per-class top-k attribution artifacts, which are robustly aggregated by the server into a Global Explanation Prior that softly aligns client explanations without constraining task learning. Across image, text, and tabular benchmarks with IID and non-IID partitions, xFedAlign matches FedAvg accuracy while consistently reducing explanation drift and improving deletion and insertion AUC relative to Local-XAI, FedAttr-Agg, and Fed-XAI, with only a few kilobytes of additional communication per round. Privacy and robustness evaluations further demonstrate reduced membership inference advantage and increased resistance to attribution poisoning, enabling consistent and trustworthy explanations in federated learning.

Social Aspects · Accountability, Transparency, and Interpretability

Zakk Heile, Hayden McTavish, Varun Babbar, Margo Seltzer, Cynthia Rudin

Standard machine learning pipelines often admit many near-optimal models. These “Rashomon sets” pose a range of challenges and opportunities for uncertainty-aware, robust decision making. They allow users to incorporate domain knowledge and preferences that would otherwise be difficult to specify directly in an objective, and they quantify diversity among valid models for a given training dataset and objective function. However, computation of Rashomon sets, even for simple, interpretable model classes such as sparse decision trees, continues to require immense memory and runtime resources. We present PRAXIS, an algorithm to approximate this Rashomon set with orders of magnitude improvement in runtime and memory usage. We validate that PRAXIS regularly recovers almost all of the full Rashomon set. PRAXIS allows researchers and practitioners to scalably model the Rashomon set for real-world datasets.

Social Aspects · Accountability, Transparency, and Interpretability

Yixiao Wang, Hayden McTavish, Varun Babbar, Margo Seltzer, Cynthia Rudin

Regression trees are among the most interpretable yet expressive model classes in machine learning. Historically, greedy induction has been the dominant approach for constructing well-performing regression trees. While optimal methods based on dynamic programming and branch-and-bound exist, they are computationally prohibitive for general linear regression trees, despite often achieving substantially better performance than greedy approaches. Recent work has shown that specialized lookahead strategies can dramatically improve runtime while maintaining near-optimal performance, primarily in classification settings. In this work, we develop a novel algorithm for near-optimal, sparse, piecewise linear regression trees that combines a lookahead-style search strategy with efficient rank-one Cholesky updates of the Gram matrix. We demonstrate, both theoretically and empirically, that our method achieves a favorable trade-off between computational efficiency, predictive accuracy, and sparsity, and scales significantly better than the current state of the art.

Social Aspects · Accountability, Transparency, and Interpretability

Siddharth Boppana, Annabel Ma, Max Loeffler, Raphaël Sarfati, Eric Bigelow, Atticus Geiger, Jack Merullo, Owen Lewis

Do the chains of thought (CoT) of reasoning Large Language Models (LLMs) reflect their internal computation? In this paper, we provide evidence of \textit{performative} CoT, where a model becomes strongly confident in its final answer, but continues generating excess tokens without revealing its internal belief. Our analysis compares activation probing of the model's final answer and early forced answering to a CoT monitor across two large reasoning models (DeepSeek-R1 671B \& GPT-OSS 120B). We observe difficulty-specific differences for these methods: the gap between the expressed CoT and the model's internal belief is larger for MMLU-Redux questions that are easier and recall-based, and is smaller on more difficult multihop GPQA-Diamond questions. We also study certain inflection points within individual reasoning traces, finding that they correspond to updates in probe confidence. Finally, we leverage our probes to enable confidence-based early exit from CoT that saves up to 80\% of tokens on MMLU and 30\% of tokens on GPQA while maintaining similar accuracy. This work provides nuance to discussions on CoT faithfulness, and establishes attention probing as an efficient method for detecting performative reasoning and for adaptive computation in reasoning LLMs.

Social Aspects · Accountability, Transparency, and Interpretability

Joshua Tan, Nicholas Vincent, Katherine Elkins, Magnus Sahlgren, Joseph Low, David Pham, Sampo Pyysalo, Jenia Jitsev

Open source projects have made incredible progress in producing widely usable machine learning models and systems, but open source alone will face challenges in fully democratizing access to AI. Unlike previous generations of open source software, open source and open weight AI models require substantial resources to activate and maintain—e.g., data and compute for pre-training, post-training, and deployment—which only a few actors can currently provide. This position paper argues that open source AI must be complemented by public AI: infrastructure and institutions that ensure models are accessible, sustainable, and governed in the public interest. To achieve the full promise of AI models as prosocial public goods, we need to build public infrastructure to power and deliver open source software and models.

Social Aspects · Accountability, Transparency, and Interpretability

Yael Konforti, Mateo Espinosa Zarlenga, Elaf Almahmoud, Mateja Jamnik

Despite strong average-case performance, deep learning models often exhibit systematic errors on specific population groups, known as *error slices*. Identifying these groups and the root causes of their failures is critical for model debugging and bias mitigation. However, existing error Slice Discovery Methods (SDMs) typically generate explanations disconnected from the model's inference process, thus only approximating the underlying error source and may be inaccurate. We address this limitation by leveraging Concept Bottleneck Models (CBMs), whose predictions are directly dependent on human-understandable semantic concepts. Since downstream task failures in CBMs commonly arise from concept mispredictions, concept representations provide a strong candidate for error slice identification, offering fine-grained explanations directly linked to the error source. Building on this insight, we introduce *CB-SLICE*, a concept-based SDM that groups samples with shared concept prediction failures and identifies the keyword-concepts most responsible for each slice’s failure-mode. Across multiple benchmarks, we show that CB-SLICE outperforms state-of-the-art methods in uncovering well-known biases while providing richer and more faithful explanations of model errors.

Social Aspects · Accountability, Transparency, and Interpretability

David Rundel, Fabian Fumagalli, Maximilian Muschalik, Bernd Bischl, Matthias Feurer

Shapley values are a principled attribution measure widely used in interpretable machine learning, but their exact computation scales exponentially with the number of players, motivating a wide range of approximation methods based on value-function evaluations of sampled coalitions. This raises the question of whether approximation accuracy can be improved by *adaptively* selecting coalitions for evaluation based on previous outcomes. This is particularly relevant in settings where the value function is costly, and the number of evaluations is severely limited, such as retraining-based feature importance, data valuation, and hyperparameter importance. For this purpose, we propose $\texttt{ShaplEIG}$, a Bayesian experimental design approach that approximates the expensive value function via a Gaussian process surrogate and adaptively selects coalitions based on their expected information gain about the Shapley values. Since Shapley values are a linear function of the value function, we show that the expected information gain is available in *closed form* and *efficiently* computable. In extensive experiments across diverse costly applications, our method consistently improves estimation accuracy over state-of-the-art baselines.

Social Aspects · Accountability, Transparency, and Interpretability

Joseph Paillard, Angel REYERO LOBO, Denis-Alexander Engemann, Thirion Bertrand

Feature-importance methods show promise for transforming machine learning (ML) models from predictive engines into tools for scientific discovery. However, expressive models can be unstable due to data sampling and algorithmic stochasticity, leading to inaccurate variable importance estimates, undermining their utility in critical biomedical applications. While ensembling offers a remedy, the choice between explaining a single ensemble model or aggregating individual model explanations is non-trivial due to the non-linearity of importance measures, and remains largely understudied. Our theoretical analysis, developed under assumptions accommodating complex state-of-the-art ML models, reveals that this choice is governed by a trade-off involving the model's excess risk. In contrast to prior literature, we show that ensembling at the model level provides more accurate variable-importance estimates, particularly for expressive models, by reducing this leading error term. We validate these findings on classical benchmarks and a large-scale proteomic study from the UK Biobank.

Social Aspects · Accountability, Transparency, and Interpretability

Mateo Espinosa Zarlenga

Concept-based models (CMs), deep neural networks that ground their predictions on representations aligned with human-understandable concepts (e.g., "round", "stripes", etc.), have been shown to learn representations that *leak* concept-irrelevant information. As the traditional narrative goes, this leakage is undesirable and should be eradicated as it leads to uninterpretable models. In this paper, we posit that this conventional view of leakage in CMs is not only ill-posed, as the evidence of how leakage makes a model less interpretable is often inconclusive, but also bound to lead to impractical CMs under common real-world constraints. Specifically, we argue that *in real-world settings where concept incompleteness is the norm, some leakage is often necessary for constructing accurate and intervenable CMs*. To this end, we propose that there is such a thing as *benign* leakage and show that, by optimizing a reframing of the typical CM training objective, CMs can encourage and exploit this form of leakage without sacrificing accuracy or intervenability.

Social Aspects · Accountability, Transparency, and Interpretability

Vacslav Glukhov

Treating the outcome of machine learning as a stable, identifiable artifact is implicit in language, tooling, and governance. This position paper examines whether a trained system admits context-appropriate criteria of identity. We show that neither functional behavior nor internal structure suffices: behavioral equivalence is underdetermined by finite data, while modern architectures admit multiple, structurally distinct realizations of the same function. Consequently, practices that treat learned systems as stable objects presuppose equivalence relations that are rarely made explicit. We do not propose abandoning such practices. Instead, we articulate the minimal conditions under which identity claims grounded in behavior, structure, or training process can be meaningfully interpreted, with implications for reproducibility, traceability, and governance.

Social Aspects · Accountability, Transparency, and Interpretability

Emmy Liu, Varun Prashant Gangal, Chelsea Zou, Michael Yu, Xiaoqi Huang, Alex Chang, Zhuofu Tao, Karanpartap Singh, Sachin Kumar, Steven Feng

Despite numerous attempts at mitigation since the inception of language models, hallucinations remain a persistent problem even in today's frontier LLMs. Why is this? We review existing definitions of hallucination and fold them into a single, unified definition wherein prior definitions are subsumed. This position paper argues that hallucination can be unified by defining it as simply inaccurate (internal) world modeling, in a form where it is observable to the user. For example, stating a fact which contradicts a knowledge base OR producing a summary which contradicts the source. By varying the reference world model and conflict policy, our framework unifies prior definitions. We argue that this unified view is useful because it forces evaluations to clarify their assumed reference "world", distinguishes true hallucinations from planning or reward errors, and provides a common language for comparison across benchmarks and discussion of mitigation strategies. Building on this definition, we outline plans for a family of benchmarks using synthetic, fully specified reference world models to stress-test and improve world modeling components.

Social Aspects · Accountability, Transparency, and Interpretability

Zhuonan Yang, Jacob Xiaochen Li, Francisco Velez, Eric Todd, David Bau, Michael L. Littman, Stephen Bach, Ellie Pavlick

One of the most common complaints about large language models (LLMs) is their prompt sensitivity---i.e., the fact that their ability to perform a task or provide a correct answer to a question can depend unpredictably on the way the question is posed. We investigate this variation by comparing two very different but commonly-used styles of prompting: those using $\textit{instructions}$, which describe the task in natural language, and those using in-context $\textit{examples}$, which provide few-shot demonstration pairs to illustrate the task. We find that, despite large variation in performance as a function of the prompt, all the prompts we study engage the same common underlying mechanisms. Specifically, we identify task-specific heads that are interpretable in vocabulary space--- which we dub $\textit{lexical task heads}$---and show that these heads are shared across prompting styles and are essential to triggering subsequent answer production. We further find that behavioral variation between prompts can be explained by the degree to which these heads are activated, and that failures are at least sometimes due to competing task representations that dilute the signal of the target task. Our results together present an increasingly clear picture of how LLMs' internal representations can explain behavior that otherwise seems idiosyncratic to users and developers.

Social Aspects · Accountability, Transparency, and Interpretability

Hilal Aka, Joe Kwon, Noam Kolt

AI models readily refuse explicitly unlawful requests, but real-world illegality often depends on context. We evaluate frontier models on contextual illegality across four corporate law domains in which routine actions—editing documents, trading stock, requesting payment, approving communications—become unlawful due to triggers such as pending investigations or bankruptcy filings. We study both chat and agentic settings and compare results to a human baseline. The best-performing models achieved near-zero compliance with illegal requests while maintaining high compliance with legal ones, though performance varied sharply by domain. We also identify distinct failure modes such as excessive refusal of legal requests and find improved performance from reasoning models and agentic environments. By utilizing the structure of contextual illegality to create controlled evaluations, our methodology provides empirical grounding for emerging research on law-following AI and extends naturally to additional legal domains.