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Social Aspects · Accountability, Transparency and Interpretability

Xinyue Xu, Yi Qin, Lu Mi, Hao Wang, Xiaomeng Li

Existing methods, such as concept bottleneck models (CBMs), have been successful in providing concept-based interpretations for black-box deep learning models. They typically work by predicting concepts given the input and then predicting the final class label given the predicted concepts. However, (1) they often fail to capture the high-order, nonlinear interaction between concepts, e.g., correcting a predicted concept (e.g., “yellow breast”) does not help correct highly correlated concepts (e.g., “yellow belly”), leading to suboptimal final accuracy; (2) they cannot naturally quantify the complex conditional dependencies between different concepts and class labels (e.g., for an image with the class label “Kentucky Warbler” and a concept “black bill”, what is the probability that the model correctly predicts another concept “black crown”), therefore failing to provide deeper insight into how a black-box model works. In response to these limitations, we propose Energy-based Concept Bottleneck Models (ECBMs). Our ECBMs use a set of neural networks to define the joint energy of candidate (input, concept, class) tuples. With such a unified interface, prediction, concept correction, and conditional dependency quantification are then represented as conditional probabilities, which are generated by composing different energy functions. Our ECBMs address both limitations of existing CBMs, providing higher accuracy and richer concept interpretations. Empirical results show that our approach outperforms the state-of-the-art on real-world datasets.

Social Aspects · Accountability, Transparency and Interpretability

Yair Gat, Nitay Calderon, Amir Feder, Alexander Chapanin, Amit Sharma, Roi Reichart

Causal explanations of the predictions of NLP systems are essential to ensure safety and establish trust. Yet, existing methods often fall short of explaining model predictions effectively or efficiently and are often model-specific. In this paper, we address model-agnostic explanations, proposing two approaches for counterfactual (CF) approximation. The first approach is CF generation, where a large language model (LLM) is prompted to change a specific text concept while keeping confounding concepts unchanged. While this approach is demonstrated to be very effective, applying LLM at inference-time is costly. We hence present a second approach based on matching, and propose a method that is guided by an LLM at training-time and learns a dedicated embedding space. This space is faithful to a given causal graph and effectively serves to identify matches that approximate CFs. After showing theoretically that approximating CFs is required in order to construct faithful explanations, we benchmark our approaches and explain several models, including LLMs with billions of parameters. Our empirical results demonstrate the excellent performance of CF generation models as model-agnostic explainers. Moreover, our matching approach, which requires far less test-time resources, also provides effective explanations, surpassing many baselines. We also find that Top-K techniques universally improve every tested method. Finally, we showcase the potential of LLMs in constructing new benchmarks for model explanation and subsequently validate our conclusions. Our work illuminates new pathways for efficient and accurate approaches to interpreting NLP systems.

Social Aspects · Accountability, Transparency and Interpretability

Guihong Li, Hsiang Hsu, Chun-Fu Chen, Radu Marculescu

Machine unlearning has emerged as a new paradigm to deliberately forget data samples from a given model in order to adhere to stringent regulations.However, existing machine unlearning methods have been primarily focused on classification models, leaving the landscape of unlearning for generative models relatively unexplored.This paper serves as a bridge, addressing the gap by providing a unifying framework of machine unlearning for image-to-image generative models.Within this framework, we propose a computationally-efficient algorithm, underpinned by rigorous theoretical analysis, that demonstrates negligible performance degradation on the retain samples, while effectively removing the information from the forget samples. Empirical studies on two large-scale datasets, ImageNet-1K and Places-365, further show that our algorithm does not rely on the availability of the retain samples, which further complies with data retention policy.To our best knowledge, this work is the first that represents systemic, theoretical, empirical explorations of machine unlearning specifically tailored for image-to-image generative models.

Social Aspects · Accountability, Transparency and Interpretability

Mengxi Ya, Yiming Li, Tao Dai, Bin Wang, Yong Jiang, Shu-Tao Xia

Saliency-based representation visualization (SRV) ($e.g.$, Grad-CAM) is one of the most classical and widely adopted explainable artificial intelligence (XAI) methods for its simplicity and efficiency. It can be used to interpret deep neural networks by locating saliency areas contributing the most to their predictions. However, it is difficult to automatically measure and evaluate the performance of SRV methods due to the lack of ground-truth salience areas of samples. In this paper, we revisit the backdoor-based SRV evaluation, which is currently the only feasible method to alleviate the previous problem. We first reveal its \emph{implementation limitations} and \emph{unreliable nature} due to the trigger generalization of existing backdoor watermarks. Given these findings, we propose a generalization-limited backdoor watermark (GLBW), based on which we design a more faithful XAI evaluation. Specifically, we formulate the training of watermarked DNNs as a min-max problem, where we find the `worst' potential trigger (with the highest attack effectiveness and differences from the ground-truth trigger) via inner maximization and minimize its effects and the loss over benign and poisoned samples via outer minimization in each iteration. In particular, we design an adaptive optimization method to find desired potential triggers in each inner maximization. Extensive experiments on benchmark datasets are conducted, verifying the effectiveness of our generalization-limited watermark. Our codes are available at \url{https://github.com/yamengxi/GLBW}.

Social Aspects · Accountability, Transparency and Interpretability

Krzysztof Kacprzyk, Tennison Liu, Mihaela van der Schaar

Transparent machine learning (ML) models are essential for ensuring interpretability and trustworthiness in decision-making systems, particularly in high-stakes domains such as healthcare, finance, and criminal justice. While transparent machine learning models have been proposed for classification and regression, time series forecasting presents some unique challenges for ensuring transparency. In particular, currently used bottom-up approaches that focus on the values of the time series at specific time points (usually regularly spaced) do not provide a holistic understanding of the entire time series. This limits the applicability of ML in many critical areas. To open up these domains for ML, we propose a top-down framework of bi-level transparency, which involves understanding the higher-level trends and the lower-level properties of the predicted time series. Applying this framework, we develop TIMEVIEW, a transparent ML model for time series forecasting based on static features, complemented with an interactive visualization tool. Through a series of experiments, we demonstrate the efficacy and interpretability of our approach, paving the way for more transparent and reliable applications of ML in various domains.

Social Aspects · Accountability, Transparency and Interpretability

Zichuan Liu, Yingying ZHANG, Tianchun Wang, Zefan Wang, Dongsheng Luo, Mengnan Du, Min Wu, Yi Wang, Chunlin Chen, Lunting Fan 等

Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns.Although previous saliency-based methods addressed the challenges,their perturbation may not alleviate the distribution shift issue, which is inevitable especially in heterogeneous samples.We present ContraLSP, a locally sparse model that introduces counterfactual samples to build uninformative perturbations but keeps distribution using contrastive learning.Furthermore, we incorporate sample-specific sparse gates to generate more binary-skewed and smooth masks, which easily integrate temporal trends and select the salient features parsimoniously.Empirical studies on both synthetic and real-world datasets show that ContraLSP outperforms state-of-the-art models, demonstrating a substantial improvement in explanation quality for time series data.The source code is available at \url{https://github.com/zichuan-liu/ContraLSP}.

Social Aspects · Accountability, Transparency and Interpretability

Xiaxia Wang, David Jaime Tena Cucala, Bernardo Grau, Ian Horrocks

There is increasing interest in methods for extracting interpretable rules from ML models trained to solve a wide range of tasks over knowledge graphs (KGs), such as KG completion, node classification, question answering and recommendation. Many such approaches, however, lack formal guarantees establishing the precise relationship between the model and the extracted rules, and this lack of assurance becomes especially problematic when the extracted rules are applied in safety-critical contexts or to ensure compliance with legal requirements. Recent research has examined whether the rules derived from the influential Neural-LP model exhibit soundness (or completeness), which means that the results obtained by applying the model to any dataset always contain (or are contained in) the results obtained by applying the rules to the same dataset. In this paper, we extend this analysis to the context of DRUM, an approach that has demonstrated superior practical performance. After observing that the rules currently extracted from a DRUM model can be unsound and/or incomplete, we propose a novel algorithm where the output rules, expressed in an extension of Datalog, ensure both soundness and completeness. This algorithm, however, can be inefficient in practice and hence we propose additional constraints to DRUM models facilitating rule extraction, albeit at the expense of reduced expressive power.

Social Aspects · Accountability, Transparency and Interpretability

Borui Zhang, Wenzhao Zheng, Jie Zhou, Jiwen Lu

Rigorousness and clarity are both essential for interpretations of DNNs to engender human trust. Path methods are commonly employed to generate rigorous attributions that satisfy three axioms. However, the meaning of attributions remains ambiguous due to distinct path choices. To address the ambiguity, we introduce Concentration Principle, which centrally allocates high attributions to indispensable features, thereby endowing aesthetic and sparsity. We then present SAMP, a model-agnostic interpreter, which efficiently searches the near-optimal path from a pre-defined set of manipulation paths. Moreover, we propose the infinitesimal constraint (IC) and momentum strategy (MS) to improve the rigorousness and optimality. Visualizations show that SAMP can precisely reveal DNNs by pinpointing salient image pixels.We also perform quantitative experiments and observe that our method significantly outperforms the counterparts.

Social Aspects · Accountability, Transparency and Interpretability

Qihan Ren, Jiayang Gao, Wen Shen, Quanshi Zhang

This study aims to prove the emergence of symbolic concepts (or more precisely, sparse primitive inference patterns) in well-trained deep neural networks (DNNs). Specifically, we prove the following three conditions for the emergence. (i) The high-order derivatives of the network output with respect to the input variables are all zero. (ii) The DNN can be used on occluded samples, and when the input sample is less occluded, the DNN will yield higher confidence. (iii) The confidence of the DNN does not significantly degrade on occluded samples. These conditions are quite common, and we prove that under these conditions, the DNN will only encode a relatively small number of sparse interactions between input variables. Moreover, we can consider such interactions as symbolic primitive inference patterns encoded by a DNN, because we show that inference scores of the DNN on an exponentially large number of randomly masked samples can always be well mimicked by numerical effects of just a few interactions.

Social Aspects · Accountability, Transparency and Interpretability

Xinyu Yang, Victor Weixin Liang, James Y Zou

Advances in machine learning are closely tied to the creation of datasets. While data documentation is widely recognized as essential to the reliability, reproducibility, and transparency of ML, we lack a systematic empirical understanding of current dataset documentation practices. To shed light on this question, here we take Hugging Face - one of the largest platforms for sharing and collaborating on ML models and datasets - as a prominent case study. By analyzing all 7,433 dataset documentation on Hugging Face, our investigation provides an overview of the Hugging Face dataset ecosystem and insights into dataset documentation practices, yielding 5 main findings: (1) The dataset card completion rate shows marked heterogeneity correlated with dataset popularity: While 86.0\% of the top 100 downloaded dataset cards fill out all sections suggested by Hugging Face community, only 7.9\% of dataset cards with no downloads complete all these sections. (2) A granular examination of each section within the dataset card reveals that the practitioners seem to prioritize Dataset Description and Dataset Structure sections, accounting for 36.2\% and 33.6\% of the total card length, respectively, for the most downloaded datasets. In contrast, the Considerations for Using the Data section receives the lowest proportion of content, accounting for just 2.1\% of the text. (3) By analyzing the subsections within each section and utilizing topic modeling to identify key topics, we uncover what is discussed in each section, and underscore significant themes encompassing both technical and social impacts, as well as limitations within the Considerations for Using the Data section. (4) Our findings also highlight the need for improved accessibility and reproducibility of datasets in the Usage sections. (5) In addition, our human annotation evaluation emphasizes the pivotal role of comprehensive dataset content in shaping individuals' perceptions of a dataset card's overall quality. Overall, our study offers a unique perspective on analyzing dataset documentation through large-scale data science analysis and underlines the need for more thorough dataset documentation in machine learning research.

Social Aspects · Accountability, Transparency and Interpretability

Maxime Wabartha, Joelle Pineau

Learning inherently interpretable policies is a central challenge in the path to developing autonomous agents that humans can trust. Linear policies can justify their decisions while interacting in a dynamic environment, but their reduced expressivity prevents them from solving hard tasks. Instead, we argue for the use of piecewise-linear policies. We carefully study to what extent they can retain the interpretable properties of linear policies while reaching competitive performance with neural baselines. In particular, we propose the HyperCombinator (HC), a piecewise-linear neural architecture expressing a policy with a controllably small number of sub-policies. Each sub-policy is linear with respect to interpretable features, shedding light on the decision process of the agent without requiring an additional explanation model. We evaluate HC policies in control and navigation experiments, visualize the improved interpretability of the agent and highlight its trade-off with performance. Moreover, we validate that the restricted model class that the HyperCombinator belongs to is compatible with the algorithmic constraints of various reinforcement learning algorithms.

Social Aspects · Accountability, Transparency and Interpretability

Aditya Chattopadhyay, Kwan Ho Ryan Chan, Rene Vidal

Variational Information Pursuit (V-IP) is an interpretable-by-design framework that makes predictions by sequentially selecting a short chain of user-defined, interpretable queries about the data that are most informative for the task. The prediction is based solely on the obtained query answers, which also serve as a faithful explanation for the prediction. Applying the framework to any task requires (i) specification of a query set, and (ii) densely annotated data with query answers to train classifiers to answer queries at test time. This limits V-IP's application to small-scale tasks where manual data annotation is feasible. In this work, we focus on image classification tasks and propose to relieve this bottleneck by leveraging pretrained language and vision models. Specifically, following recent work, we propose to use GPT, a Large Language Model, to propose semantic concepts as queries for a given classification task. To answer these queries, we propose a light-weight Concept Question-Answering network (Concept-QA) which learns to answer binary queries about semantic concepts in images. We design pseudo-labels to train our Concept-QA model using GPT and CLIP (a Vision-Language Model). Empirically, we find our Concept-QA model to be competitive with state-of-the-art VQA models in terms of answering accuracy but with an order of magnitude fewer parameters. This allows for seamless integration of Concept-QA into the V-IP framework as a fast-answering mechanism. We name this method Concept-QA+V-IP. Finally, we show on several datasets that Concept-QA+V-IP produces shorter, interpretable query chains which are more accurate than V-IP trained with CLIP-based answering systems. Code available at https://github.com/adityac94/conceptqa_vip.

Social Aspects · Accountability, Transparency and Interpretability

Mert Yuksekgonul, Varun Chandrasekaran, Erik Jones, Suriya Gunasekar, Ranjita Naik, Hamid Palangi, Ece Kamar, Besmira Nushi

We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text. We propose modeling factual queries as constraint satisfaction problems and use this framework to investigate how the LLM interacts internally with factual constraints. We find a strong positive relationship between the LLM's attention to constraint tokens and the factual accuracy of generations. We curate a suite of 10 datasets containing over 40,000 prompts to study the task of predicting factual errors with the Llama-2 family across all scales (7B, 13B, 70B). We propose SAT Probe, a method probing attention patterns, that can predict factual errors and fine-grained constraint satisfaction, and allow early error identification. The approach and findings take another step towards using the mechanistic understanding of LLMs to enhance their reliability.

Social Aspects · Accountability, Transparency and Interpretability

Shengyao Lu, Keith G Mills, Jiao He, Bang Liu, Di Niu

Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability. Most existing methods for explaining GNNs typically rely on training auxiliary models, resulting in the explanations remain black-boxed. This paper introduces Graph Output Attribution (GOAt), a novel method to attribute graph outputs to input graph features, creating GNN explanations that are faithful, discriminative, as well as stable across similar samples. By expanding the GNN as a sum of scalar products involving node features, edge features and activation patterns, we propose an efficient analytical method to compute contribution of each node or edge feature to each scalar product and aggregate the contributions from all scalar products in the expansion form to derive the importance of each node and edge. Through extensive experiments on synthetic and real-world data, we show that our method not only outperforms various state-of-the-art GNN explainers in terms of the commonly used fidelity metric, but also exhibits stronger discriminability, and stability by a remarkable margin.

Social Aspects · Accountability, Transparency and Interpretability

Weida Li, Yaoliang Yu

The family of probabilistic values, axiomatically-grounded in cooperative game theory, has recently received much attention in data valuation. However, it is often computationally expensive to compute exactly (exponential w.r.t. the number of data to valuate denoted by $n$). The existing generic estimator costs $O(n^2\log n)$ utility evaluations to achieve an $(\epsilon,\delta)$-approximation under the 2-norm, while faster estimators have been developed recently for special cases (e.g., empirically for the Shapley value and theoretically for the Banzhaf value). In this work, starting from the discovered connection between probabilistic values and least square regressions, we propose a Generic Estimator based on Least Squares (GELS) along with its variants that cost $O(n\log n)$ utility evaluations for many probabilistic values, largely extending the scope of this currently best complexity bound. Moreover, we show that each distributional value, proposed by Ghorbani et al. (2020) to alleviate the inconsistency of probabilistic values induced by using distinct databases, can also be cast as optimizing a similar least square regression. This observation leads to a theoretically-grounded framework TrELS (Training Estimators based on Least Squares) that can train estimators towards the specified distributional values without requiring any supervised signals. Particularly, the trained estimators are capable of predicting the corresponding distributional values for unseen data, largely saving the budgets required for running Monte-Carlo methods otherwise. Our experiments verify the faster convergence of GELS, and demonstrate the effectiveness of TrELS in learning distributional values. Our code is available at https://github.com/watml/fastpvalue.

Social Aspects · Accountability, Transparency and Interpretability

Hila Chefer, Oran Lang, Mor Geva, Volodymyr Polosukhin, Assaf Shocher, michal Irani, Inbar Mosseri, Lior Wolf

Text-to-image diffusion models have demonstrated an unparalleled ability to generate high-quality, diverse images from a textual prompt. However, the internal representations learned by these models remain an enigma. In this work, we present Conceptor, a novel method to interpret the internal representation of a textual concept by a diffusion model. This interpretation is obtained by decomposing the concept into a small set of human-interpretable textual elements. Applied over the state-of-the-art Stable Diffusion model, Conceptor reveals non-trivial structures in the representations of concepts. For example, we find surprising visual connections between concepts, that transcend their textual semantics. We additionally discover concepts that rely on mixtures of exemplars, biases, renowned artistic styles, or a simultaneous fusion of multiple meanings of the concept.Through a large battery of experiments, we demonstrate Conceptor's ability to provide meaningful, robust, and faithful decompositions for a wide variety of abstract, concrete, and complex textual concepts, while allowing to naturally connect each decomposition element to its corresponding visual impact on the generated images.

Social Aspects · Accountability, Transparency and Interpretability

Robert Huben, Hoagy Cunningham, Logan Smith, Aidan Ewart, Lee Sharkey

One of the roadblocks to a better understanding of neural networks' internals is \textit{polysemanticity}, where neurons appear to activate in multiple, semantically distinct contexts. Polysemanticity prevents us from identifying concise, human-understandable explanations for what neural networks are doing internally. One hypothesised cause of polysemanticity is \textit{superposition}, where neural networks represent more features than they have neurons by assigning features to an overcomplete set of directions in activation space, rather than to individual neurons. Here, we attempt to identify those directions, using sparse autoencoders to reconstruct the internal activations of a language model. These autoencoders learn sets of sparsely activating features that are more interpretable and monosemantic than directions identified by alternative approaches, where interpretability is measured by automated methods. Moreover, we show that with our learned set of features, we can pinpoint the features that are causally responsible for counterfactual behaviour on the indirect object identification task \citep{wang2022interpretability} to a finer degree than previous decompositions. This work indicates that it is possible to resolve superposition in language models using a scalable, unsupervised method. Our method may serve as a foundation for future mechanistic interpretability work, which we hope will enable greater model transparency and steerability.

Social Aspects · Accountability, Transparency and Interpretability

Zhiyu Zhu, Huaming Chen, Jiayu Zhang, Xinyi Wang, Zhibo Jin, Jason Xue, Flora Salim

Due to the real-world noise and human-added perturbations, attaining the trustworthiness of deep neural networks (DNNs) is a challenging task. Therefore, it becomes essential to offer explanations for the decisions made by these non-linear and complex parameterized models. Attribution methods are promising for this goal, yet its performance can be further improved. In this paper, for the first time, we present that the decision boundary exploration approaches of attribution are consistent with the process for transferable adversarial attacks. Specifically, the transferable adversarial attacks craft general adversarial samples from the source model, which is consistent with the generation of adversarial samples that can cross multiple decision boundaries in attribution. Utilizing this consistency, we introduce a novel attribution method via model parameter exploration. Furthermore, inspired by the capability of frequency exploration to investigate the model parameters, we provide enhanced explainability for DNNs by manipulating the input features based on frequency information to explore the decision boundaries of different models. Large-scale experiments demonstrate that our \textbf{A}ttribution method for \textbf{E}xplanation with model parameter e\textbf{X}ploration (AttEXplore) outperforms other state-of-the-art interpretability methods. Moreover, by employing other transferable attack techniques, AttEXplore can explore potential variations in attribution outcomes. Our code is available at: https://github.com/LMBTough/ATTEXPLORE.

Social Aspects · Accountability, Transparency and Interpretability

Samyak Jain, Robert Kirk, Ekdeep Singh Lubana, Robert Dick, Hidenori Tanaka, Tim Rocktaeschel, Edward Grefenstette, David Krueger

Fine-tuning large pre-trained models has become the de facto strategy for developing both task-specific and general-purpose machine learning systems, including developing models that are safe to deploy. Despite its clear importance, there has been minimal work that explains how fine-tuning alters the underlying capabilities learned by a model during pretraining: does fine-tuning yield entirely novel capabilities or does it just modulate existing ones? We address this question empirically in synthetic, controlled settings where we can use mechanistic interpretability tools (e.g., network pruning and probing) to understand how the model's underlying capabilities are changing. We perform an extensive analysis of the effects of fine-tuning in these settings, and show that: (i) fine-tuning rarely alters the underlying model capabilities; (ii) a minimal transformation, which we call a `wrapper', is typically learned on top of the underlying model capabilities, creating the illusion that they have been modified; and (iii) further fine-tuning on a task where such ``wrapped capabilities'' are relevant leads to sample-efficient revival of the capability, i.e., the model begins reusing these capabilities after only a few gradient steps. This indicates that practitioners can unintentionally remove a model's safety wrapper merely by fine-tuning it on a, e.g., superficially unrelated, downstream task. We additionally perform analysis on language models trained on the TinyStories dataset to support our claims in a more realistic setup.

Yifeng Zhang, Shi Chen, Qi Zhao

Answering visual questions requires the ability to parse visual observations and correlate them with a variety of knowledge. Existing visual question answering (VQA) models either pay little attention to the role of knowledge or do not take into account the granularity of knowledge, e.g., attaching the color of "grassland" to "ground"). They have yet to develop the capability of modeling knowledge of multiple granularity, and are also vulnerable to spurious data biases. To fill the gap, this paper makes progresses from two distinct perspectives: (1) It presents a Hierarchical Concept Graph (HCG) that discriminates and associates multi-granularity concepts with a multi-layered hierarchical structure, aligning visual observations with knowledge across different levels to alleviate data biases. (2) To facilitate a comprehensive understanding of how knowledge contributes throughout the decision-making process, we further propose an interpretable Hierarchical Concept Neural Module Network (HCNMN) It explicitly propagates multi-granularity knowledge across the hierarchical structure and incorporates them with a sequence of reasoning steps, providing a transparent interface to elaborate on the integration of observations and knowledge. Through extensive experiments on multiple challenging datasets (i.e., GQA,VQA,FVQA,OK-VQA) , we demonstrate the effectiveness of our method in answering questions in different scenarios. Our code is available at https://github.com/SuperJohnZhang/HCNMN.