A Cognitive Framework for Learning Debiased and Interpretable Representations via Debiasing Global Workspace

Authors: jinyung  hong
eun som jeon
changhoon  kim
keun hee park
utkarsh  nath
yezhou  yang
pavan  turaga
theodore p pavlic
Venue: NeurIPS_cc_2024_Workshop_UniReps
Published: 2024-09-07 22:28:32

Abstract: When trained on biased datasets, Deep Neural Networks (DNNs) often make predictions based on attributes derived from features spuriously correlated with the target labels. This is especially problematic if these irrelevant features are easier for the model to learn than the truly relevant ones. Many existing approaches, called debiasing methods, have been proposed to address this issue, but they often require predefined bias labels and entail significantly increased computational complexity by incorporating extra auxiliary models. Instead, we provide an orthogonal perspective from the existing approaches, inspired by cognitive science, specifically Global Workspace Theory (GWT). Our method, Debiasing Global Workspace (DGW), is a novel debiasing framework that consists of specialized modules and a shared workspace, allowing for increased modularity and improved debiasing performance. Additionally, DGW enhances the transparency of decision-making processes by visualizing which features of the inputs the model focuses on during training and inference through attention masks.  We begin by proposing an instantiation of GWT for the debiasing method. We then outline the implementation of each component within DGW. At the end, we validate our method across various biased datasets, proving its effectiveness in mitigating biases and improving model performance.

Main paper: 
 introduction: 
 Deep Neural Networks (DNNs) have achieved remarkable advancements across various domains, such as image classification (He et al., 2019; Xie et al., 2020) , generation (Wang and Gupta, 2016; Kataoka et al., 2016) , and segmentation (Luo et al., 2017; Zheng et al., 2014) . However, DNNs often show limited generalization capability to out-of-distribution (OOD) data and are susceptible to biases present in their training datasets (Torralba and Efros, 2011) . These biases occur when irrelevant features, like background color, correlate with target labels, causing the models to rely on these features for making predictions (Geirhos et al., 2020) . This reliance on biased features leads to poor performance when the model encounters new data that does not share the same biases.Biased datasets possess many bias-aligned samples, where irrelevant features correlate with the labels, and a small number of bias-conflicting samples, where these features do not align with the labels. Models trained on such data indeed tend to focus on the bias-aligned samples, leading to poor generalization (Hendrycks et al., 2021b,a) .Various debiasing methods have been proposed to prevent a network from relying on spurious correlations when trained on a biased dataset. Some methods assume that biased features are "easier" to learn than robust ones, leading to the use of auxiliary models that exploit these biased features to guide the main model's training (Nam et al., 2020; Sanh et al., 2020) . Strategies such as reweighting samples (Liu et al., 2021; Nam et al., 2020) and data augmentation (Kim et al., 2021; Lee et al., 2021) are common but often struggle with insufficiently diverse samples. Other approaches involve identifying specific biases before training (Hong and Yang, 2021; Kim et al., 2019; Li and Vasconcelos, 2019; Sagawa et al., 2019) , allowing the model to ignore or correct these biases. Although effective, this requires accurate bias identification and extensive manual labeling (Bahng et al., 2020; Tartaglione et al., 2021) .In this work, we depart from the above perspectives and focus on a novel and completely different approach to implement a debiasing framework. In modern ML and AI, it has been argued that it is better to build an intelligent system from many interacting specialized modules rather than a single "monolithic" entity to deal with a broad spectrum of conditions and tasks (Goyal and Bengio, 2022; Minsky, 1988; Robbins, 2017) . Toward this end, we focused on a cognitive science framework proposed to underlie perception, executive function, and consciousness: Global Workspace Theory (GWT). GWT is a crucial element of modern cognitive science that models human consciousness arising from integrating and broadcasting information across specialized, unconscious processes in the brain (Baars, 1993 (Baars, , 2005)) . Many recent studies proposing a deep-learning implementation of GWT (Bengio, 2017; Goyal et al., 2021; VanRullen and Kanai, 2021) have demonstrated their effectiveness in allowing a model to have general-purpose functionality, increased modularity, improved performance, and interpretable representation learning. This perspective is expected to be well suited for application in implementing debiasing methods.Therefore, we propose the Debiasing Global Workspace (DGW), a novel instantiation of GWT for debiasing to eliminate the negative effect of the misleading correlations. Our debiasing approach involves specialized modules (acting as the specialists in GWT) and an attention-based information bottleneck (acting as the global workspace in GWT). This allows the model to achieve straightforward, functional modularity and effective debiasing performance while providing interpretable representation by visualizing which attributes are essential for accurate predictions and which are irrelevant and likely to cause errors.The rest of this paper is organized as follows. We begin in Section 2 with a review of related work and relevant background literature. Then, in Section 3, we propose a conceptual modification of the GWT to implement a debiasing method. This involves defining specialized modules and the shared global workspace (Section 3.1). Then, we provide a step-by-step framework for defining the essential deep-learning components of our debiasing model within an AI system (Section 3.2). In Section 4, we empirically test our method on biased datasets, including Colored MNIST, Corrupted CIFAR10, and Biased FFHQ, and demonstrate that DGW effectively separates and understands intrinsic and biased features through both performance metrics and visualizations. Finally, we conclude with a discussion of future work and limitations of our approach in Section 5. 
related work: 
 There have been a variety of different debiasing methods for DNNs, and there also have been several connection points between GWT and neural networks. We review these approaches here.Debiasing with predefined forms of bias or specific bias labels. This method involves identifying specific biases before training (Hong and Yang, 2021; Kim et al., 2019; Li and Vasconcelos, 2019; Sagawa et al., 2019) . The model then learns to ignore or correct these biases. Although effective, it depends on accurately identifying biases beforehand, which can be challenging. Another approach (Bahng et al., 2020; Tartaglione et al., 2021) uses bias labels to tag data, allowing the model to differentiate between biased and unbiased data during training. This improves learning but requires extensive manual labeling.Debiasing using the easy-to-learn heuristic. Biases are "easier" for models to learn (Nam et al., 2020) than intrinsic features. Techniques like dynamic training schemes, re-weighting samples, and data augmentation (Geirhos et al., 2018; Lee et al., 2021; Minderer et al., 2020; Li and Vasconcelos, 2019; Lim et al., 2023) help models focus on unbiased features. However, these methods struggle with insufficient diverse samples. Complex models can learn invariant features or correct representations but are difficult to design and train (Tu et al., 2022; Zhao et al., 2020; Agarwal et al., 2020; Bahng et al., 2020; Geirhos et al., 2018; Goel et al., 2020; Kim et al., 2019; Li et al., 2020; Minderer et al., 2020; Tartaglione et al., 2021; Wang et al., 2020) .Others. SelecMix (Hwang et al., 2022) creates new training samples by mixing pairs with similar labels but different biases, or different labels but similar biases, using an auxiliary contrastive model. Although effective, this adds significant training complexity. χ 2 model (Zhang et al., 2023) learns debiased representations by identifying Intermediate Attribute Samples (IAS) and using a χ-structured metric learning objective. However, its reliance on training dynamics to identify IASs makes it different from our approach and out of the scope of our study.In neuroscience and cognitive science, there is an ongoing effort to develop theories of consciousness (ToCs) to identify the neural correlates of consciousness, as reviewed by Seth and Bayne (2022) . One such theory is the Global Workspace Theory (GWT) (Baars, 1993; Dehaene and Changeux, 2011; Mashour et al., 2020) , which is inspired by the 'blackboard' architecture used in artificial intelligence. In this architecture, a centralized blackboard resource facilitates information sharing among specialized processors.Recent studies have aimed to bridge the gap between neuroscience and deep learning, focusing on practical solutions for implementing a GWT using current deep learning components while considering the equivalent brain mechanisms (Goyal and Bengio, 2022; Minsky, 1988; Robbins, 2017; Goyal et al., 2021; Hong et al., 2024) . Bengio (2017) emphasized learning high-level concepts by selecting key elements through attention, forming a low-dimensional conscious state similar to language, which aids in better representation learning. Mashour et al. (2020) details GWT's implementation in neuroscience, suggesting that consciousness arises from extensive information sharing across brain regions via a central network of neurons.Inspired by GWT, our Debiasing Global Workspace (DGW) framework manages intrinsic and biased attributes in neural networks. DGW integrates information from intrinsic and bias specialists, ensuring disentangled representations are considered in decision making. Unlike prior works focusing on monolithic architecture or general-purpose learning, our approach uniquely applies these theories to debiasing neural networks. 
method: 
 We propose the Debiasing Global Workspace (DGW), an instantiation of GWT for debiasing. DGW learns the composition of attributes in a dataset and provides interpretable explanations for the model's decisions. We introduce the conceptual framework of GWT for debiasing first (Section 3.1), its implementation in a deep learning framework next (Section 3.2), and the training objectives last (Section 3.3).Figure 1 depicts a conceptual overview of our proposed DGW framework. The conceptual flow of the DGW proceeds through a sequence of steps that we describe in detail here.Step 0. To learn disentangled representations of intrinsic and biased attributes, we introduce two specialists: intrinsic ϕ i and biased ϕ b . In the original GWT, the specialists connect to the global workspace before any stimulus appears, coupling their latent spaces bidirectionally with the workspace. We modify this setup to control the connections to backpropagate different information to two specialists separately (black and red connections between specialists and DGW in Step 0 in Fig. 1 ). Specifically, the intrinsic and bias specialists function identically in the forward pass. However, during the backpropagation stage, only the intrinsic attribute encoder updates its parameters and learns, while the bias attribute encoder remains frozen and does not undergo parameter updates when the task of the model is to find intrinsic attributes.Step 1. The DGW acts as an independent and intermediate shared latent space trained to perform unsupervised neural translation between the C latent spaces from the specialized modules. The translation system is optimized to ensure that successive translation and back translation (e.g., a cycle from A to B, then back to A) return the original input (Goyal et al., 2021; VanRullen and Kanai, 2021) . We implement specific operations to mimic the translation system by leveraging the residual operations (He et al., 2016) and a variant of mixup (Verma et al., 2019) . Posner (1994) argues that attention determines what information is consciously perceived and what is discarded in brains. In GWT, attention selects the information that enters the workspace. When a specific module is connected to the workspace through attention, its latent space activation vector is copied into the DGW. This internal copy serves as a bidirectional connection interface between the corresponding module and the DGW.When a new stimulus, such as the digit "zero," appears, its latent activity transfers to the corresponding internal copy inside the workspace, initiating a broadcast to all other domains. This shared latent space (S i latent in Fig. 1 ) uses translations and back translations from all modules to compute and train via error backpropagation. We introduce a recurrent, top-down pathway, and it can sometimes be considered as a key to account for the global ignition property observed in the brain when an input reaches consciousness, and the corresponding module is mobilized into the conscious global workspace (VanRullen and Kanai, 2021).Step 2. The incoming information is then immediately broadcast and translated (via the shared latent space) into the latent space of all other modules. In GWT, this translation process is automatic. However, we modify this to manually enforce learning of intrinsic and biased attribute representation with different loss functions. Specifically, we enforce the classifier ϕ i to learn intrinsic attributes through error backpropagation from specific training objectives (Step 2-1 in Fig. 1 ). Step 2-2 simultaneously enforces the connection to the classifier ψ b and limits backpropagation to the intrinsic specialist ϕ i to learn the bias attribute representations.Here, we present our deep-learning-based implementation of the DGW. It combines and organizes existing components for effective debiasing frameworks in a way that is consistent with the cognitivescience-inspired DGW framework described above.Two specialized modules and the shared workspace. DGW uses two independent specialists, the intrinsic attribute encoder ϕ i and the bias attribute encoder ϕ b . From these, we derive concatenated features E = [ϕ i (x); ϕ b (x)] ∈ R L×D . To connect specialists and the shared workspace, we define e ∈ {E i , E b }, whereE i = [ϕ i (x); sg(ϕ b (x))] and E b is vice versa, with sg(•) as the stopgradient operator. We introduce the Global Latent Attention (GLA) module, which acts as a shared workspace that encourages the synchronization among the input feature vector E via a latent feature representation S latent .Latent-slot binding specific to each input. The GLA module uses a set number of latent embeddings or latent slots C. These latent slots represent the learnable embedding vectors in the DGW, and perform competitive attention (Vaswani et al., 2017) on the input features e. We defines latent ∈ {S i latent , S b latent } ∈ R C×Dwhere C i is number of slots for intrinsic features and C b for bias features, with C = C i + C b . The attention mechanism is such that:EQUATIONwhere, k, q are linear projection matrices, and the softmax function normalizes the slots, creating competition among them. The slots are refined iteratively using the following:EQUATIONwhere, slatent is the latent slot representation after n iterations, GRU (Cho et al., 2014 ) is a recurrent neural network, and v is another liner projection matrix. The initial slots s (0) latent are initialized with learnable queries following (Jia et al., 2022) .The above computations can be considered to implement a shared global workspace (Goyal et al., 2021; Hong et al., 2024) as they enable different parts of the model to compete for attention, integrating and broadcasting information similar to GWT.Broadcast updated information to specialists. Specialists update their states using information from the shared workspace. The inverted cross-attention mechanism allows specialists to query and interact with updated latent slots s (n+1) latent , updating their states through:EQUATION)where v is a linear projection matrix. Here, as the meaning of information broadcast, ⊕ can be instantiated with various computational operations, including a residual connection (He et al., 2016) .The other way of operation is a modified version of Manifold Mixup (Verma et al., 2019) , which interpolates feature embeddings to capture higher-level information:ē = Mix α e, A s (n+1) latent , e • v s (n+1) latent , where Mix α (a, b) = α • a + (1 -α)• b, and α ∼ Beta(β, β). The updated feature vector ē is then fed to the classifier ψ i and ψ b . We compare the performance of using residual connections versus our modified Manifold Mixup in Section 4.1.In GWT, the information broadcast through the global workspace is a necessary and sufficient condition for conscious perception (VanRullen and Kanai, 2021) . Intuitively, the attention mask A(s(n+1)latent , e) can be seen as artificial phenomenal consciousness, indicating the immediate subjective experience of sensations and perceptions. These non-negative relevance scores depend on x through the averaged attention weight, allowing us to show interpretable representations for intrinsic and biased attributes in our analysis (Section 4.2).Here, we summarize the objective functions to train our framework. We have two linear classifiers ψ i and ψ b that take the updated concatenated vector ē from the previous module as input to predict the target label y. Our training objectives consist of: i) the relative attribute score learning phase, and ii) the attribute composition phase.Relative attribute score learning phase. In this phase, we define two tasks within the conceptual framework for: identifying intrinsic attributes and identifying biased attributes. Without specific information about bias types, we utilize the relative difficulty score of each data sample, as proposed by Nam et al. (2020) . Specifically, we train ϕ b , S b latent , and ψ b to focus on bias attributes using generalized cross entropy (GCE) (Zhang and Sabuncu, 2018) , while ϕ i , S i latent and ψ i are trained with the cross entropy (CE) loss. Samples with high CE loss from ψ b are considered bias-conflicting compared to those with low CE loss. We define the relevance score function:Score(ē, y) ≜ CE(ψ b (ē), y) CE(ψ i (ē), y) + CE(ψ b (ē), y) .Thus, the objective function is defined using the above relative difficulty score of each data sample:L rel = Score(ē, y) • CE(ψ i (ē), y) + λ rel GCE(ψ b (ē), y),where λ rel is the weight that adjusts between two loss terms. This loss function balances the learning between intrinsic and biased attributes, ensuring effective identification and separation of these attributes during the training phase.Attribute-composition phase. We swap the disentangled latent vectors among the training sets (Lee et al., 2021) . We randomly permute the intrinsic and bias features in each mini-batch, creatingE swap = [ϕ i (x); ϕ b swap (x)]where ϕ b swap (x) denotes the randomly permuted bias attributes. This process produces augmented bias-conflicting latent vectors. As similar as the definition of e, we define e swap ∈ {E i swap , E b swap } and generate ēswap following the same process described in eqs 1, 2 and 3. The objective function for this phase is:L swap = Score(ē, y) • CE(ψ i (ē swap ), y) + λ swap GCE(ψ b (ē swap ), ỹ),where ỹ denotes target labels for permuted bias attributes ϕ b swap (x), and λ swap is the balancing weight between two loss terms. Notice that the relevance score Score(ē, y) is re-used to reduce computational complexity. This loss function implies that by swapping bias features, the model learns to handle a wider variety of bias-conflicting samples, improving its ability to generalize beyond the specific biases present in the training data. Consequently, the model becomes more robust as it learns to focus on intrinsic features while disregarding spurious correlations, resulting in better performance on unbiased data. Furthermore, augmenting the training data in this manner helps the model generalize better to new, unseen data by exposing it to a wider range of possible biases during training.Entropy regularization. We empirically incorporate an additional regularization term on the latent slot attention mask to enhance performance:L ent = H(A(s (n) latent , e)) + H(A(s (n)latent , e swap )), where A(s(n)latent , e) and A(s(n)latent , e swap ) are attention masks from the last iteration of eq. 2. Minimizing entropy H(A) = H(a 1 , . . . , a |A| ) = (1/|A|) i -a i • log(a i ) encourages the attention masks to be consistent over the input features captured by the latent slots. This regularization ensures the model's attention remains focused and interpretable across different input scenarios.Final loss. The total loss function is a combination of the above components:L total = L rel + λ swap • L swap + λ ent • L ent .Here, λ swap and λ ent are weights that adjust the importance of the feature augmentation and entropy regularization, respectively. This comprehensive loss function ensures balanced training that enhances the model's ability to learn and generalize effectively while maintaining interpretability and robustness. 
experiments: 
 Here, we present our experimental results, focusing on performance evaluation on various biased datasets (Section 4.1), interpretable analysis for attribute-centric representation learning (Section 4.2), and additional qualitative and quantitative analyses (Section 4.3).Datasets. Following the previous work (Lee et al., 2021) , we used three well-known benchmark datasets for debiasing methods to evaluate DGW's performance and interpretability:• Colored MNIST (C-MNIST) and Corrupted CIFAR10 (C-CIFAR-10): These synthetic datasets are designed to test model generalization on unbiased test sets by varying the ratio of bias-conflicting samples (0.5%, 1%, 2%, and 5%). • Bias FFQH (BFFHQ): This real-world dataset from FFHQ (Karras et al., 2019) contains face images annotated with age (intrinsic attribute) and gender (bias attribute). Most samples are young women and old men, creating a high correlation between age and gender. For BFFHQ, we included 0.5% bias-conflicting samples in the training set and used a biasconflicting test set to ensure robust evaluation.At inference time, we evaluate the models on clean data where no bias-conflicting samples exist.Baselines. Our set of debiasing baselines includes six different approaches 1 : Vanilla network, HEX (Wang et al., 2018) , EnD (Tartaglione et al., 2021) , ReBias (Bahng et al., 2020) , LfF (Nam et al., 2020) , and LFA (Lee et al., 2021) . Vanilla refers to the classification model trained only with the original cross-entropy (CE) loss without debiasing strategies. EnD leverages the explicit bias labels, such as the color labels in the C-MNIST dataset, during the training phase. HEX and ReBias assume an image's texture as a bias type, whereas LfF, LFA, and our method do not require any prior knowledge about the bias type. Furthermore, we configure a naive debiasing approach integrated with GWT implementation: V+CCT. CCT (Hong et al., 2024) proposed an instantiation of GWT applicable to implement an interpretable model. To compare our DGW, we simply configure the direction fusion of the Vanilla network with CCT as a GWT debiasing method.Implementation details. Following the implementation details from Lee et al. (2021) , we used a fully connected network for attribute encoders with three hidden layers for C-MNIST and ResNet-18 for C-CIFAR-10 and BFFHQ. We employed a fully connected classifier with double the hidden units to handle the combined output from the intrinsic attribute encoder ϕ i and the bias attribute encoder ϕ b .During testing, only the intrinsic classifier ψ i (e) was used for final predictions. We used batch sizes of 256 for C-MNIST and C-CIFAR-10, and 64 for BFFHQ, respectively. Two concepts and size of 8 were used for C-MNIST, 5 and 16 for C-CIFAR-10, and 10 and 32 for BFFHQ, respectively. We trained our model and baselines with three trials and reported the averaged accuracy and standard deviation. More details of experimental settings are explained in the Appendix C.4.Performance Comparison. Table 1 shows that ReBias outperforms DGW on C-MNIST because it uses additional predefined bias labels. This gives ReBias a specific advantage. However, DGW excels without needing predefined bias labels, making it more versatile. DGW, with all operators, 1 We only establish baselines that can be directly tested. For example, χ 2 (Zhang et al., 2023) is not included because its code is not publicly available, and SelecMix (Hwang et al., 2022) is not included because it is a data-augmentation method that differs from our method category and has high training complexity, taking over approximately four times longer than our method. Additionally, although the authors of SelecMix claim it runs Table 1 : Test accuracy (%) on unbiased test sets of C-MNIST and C-CIFAR-10, and the biasconflicting test set of BFFHQ with varying ratio of bias-conflicting samples. ( * ) denotes methods tailored to predefined forms of bias, (°) methods using bias labels, ( †) methods relying on the easyto-learn heuristic, and ( ‡) methods combined with GWT. V+CCT indicates the direct integration of Vanilla and CCT. DGW+M refers to DGW with our mixup strategy, and DGW+R refers to DGW with residual connection. Performance for HEX and EnD is from (Lee et al., 2021) , while results for Vanilla, ReBias, LfF, LFA, V+CCT and DGW are from our evaluation. The best-performing results are shown in bold, and the second-best results are underlined. To make the analysis of interpretable attribute representation learning in our model more intuitive, let us explore the attention mask patterns A(s(n+1)latent , e) for the C-MNIST and C-CIFAR-10 datasets. In the broadcast in our formulation (A(s(n+1)latent , e) in eq. 3)), DGW generates two attention masks:A i = A(S ilatent , E i ) for intrinsic attributes, focusing on essential features like shape, andA b = A(S blatent , E b ) for biased attributes, capturing non-essential features like color. For the C-MNIST dataset, intrinsic attention masks highlight the shapes of the digits, ignoring colors. For instance, the digits "0," "6," and "8" consistently highlight shape regions (Fig. 2(a) ), showing that the model focuses on shape for classification. Conversely, bias attention masks highlight color regions, not shapes. Digits "1," "5," "2," and "8" in yellow/magenta/green show nearly identical masks (Fig. 2(b) ), indicating a focus on color. This confirms that the biased components of DGW capture color information, which is irrelevant for digit recognition.For the C-CIFAR-10 dataset, intrinsic masks focus on uncorrupted parts of the images (Fig. 3(b) ), highlighting true object features. For example, masks for a truck, car, dog, and horse highlight uncorrupted areas, avoiding noise. Bias masks, on the other hand, focus on corrupted areas, showing on an RTX 3090, we found that our environment with a 24GB RTX A6000 could not handle the real-life dataset BFFHQ, indicating significant computational resource requirements. no overlap with intrinsic masks (Fig. 3(c) ). This complementary relationship illustrates the effective segregation of essential (intrinsic) and non-essential (biased) information.In summary, for C-MNIST, intrinsic masks focus on digit shapes, while bias masks focus on colors. For C-CIFAR-10, intrinsic masks highlight uncorrupted parts, and bias masks cover corrupted parts. This clear separation supports the model's robustness and interpretability, ensuring decisions are based on relevant features while ignoring spurious correlations. More visualization results can be found in Appendix C.5.We provide additional analysis to compare our DGW (DGW+M in Table 1 ) method with Vanilla and LFA (Lee et al., 2021) . More experimental results with different settings can be found in Appendix C.6. t-SNE and Clustering. We measure clustering performance using t-SNE (van der Maaten and Hinton, 2008) and V-Score (Rosenberg and Hirschberg, 2007) on features from various models capturing intrinsic and bias attributes on C-MNIST. V-Score represents homogeneity and completeness, with higher values indicating better clustering. In Fig. 4 , our DGW's ϕ i captures intrinsic attributes effectively, resulting in tighter clusters and better separation, as indicated by the V-Score. Bias attributes are well captured by the ϕ b , as shown in Fig. 4(d) .Model Similarity. We visualize model similarity using Centered Kernel Alignment (CKA) (Raghu et al., 2021; Kornblith et al., 2019; Cortes et al., 2012) , comparing similarities between all pairs of layers for different models. In this analysis, I and B denote ϕ i and ϕ b . As shown in Fig. 5 , Vanilla and LFA possess similar weights across many layers, while DGW shows fewer similarities in both initial and deeper layers, indicating different behavior across layers compared to baselines.Model Reliability. We evaluate model generalizability using Expected Calibration Error (ECE) and Negative Log Likelihood (NLL) (Guo et al., 2017) . ECE measures calibration error, and NLL  
conclusion: 
 In this work, we introduced Debiasing Global Workspace (DGW), a framework designed to learn debiased representations of attributes in neural networks. By leveraging attention mechanisms inspired by the Global Workspace Theory, our method effectively differentiates between intrinsic and biased attributes, enhancing both performance and interpretability. Comprehensive evaluations across various biased datasets demonstrated that DGW improves model robustness and generalizability on biased data and provides interpretable insights into the model's decision-making process. Our approach results in tighter clusters and better model separation, indicating superior performance in both intra-and inter-classification tasks. Furthermore, DGW shows enhanced model reliability and generalizability, making it a better solution for addressing biases in real-world applications. Future work could focus on reducing this complexity, exploring the scalability of DGW to even larger and more diverse datasets, and extending the framework into a general-purpose drop-in layer to enhance robust performance across a wider range of image recognition tasks.Limitations. We acknowledge that introducing our modules can increase training complexity, including model size and training time. This represents a trade-off between performance and decisionmaking transparency. Although our additional overhead is minimal, further analysis is necessary to optimize and streamline the process. In contrast, our method shows significantly lower similarity values, indicating different weights and behaviors across layers compared to Vanilla and LFA. Our method affects deeper layers more, where the attention module is inserted, suggesting a distinct impact on model behavior.Model Reliability. To evaluate the generalizability of models, we measure Expected Calibration Error (ECE) and Negative Log Likelihood (NLL) (Guo et al., 2017) , where ECE is to measure calibration error and NLL is to calculate the probabilistic quality of a model. In detail, ECE aims to evaluate whether the predictions of a model are reliable and accurate, which is a simple yet sufficient metric for assessing model calibration and reflecting model generalizability (Guo et al., 2017) .In Table A-  
 
 Appendix: Preprint. 


Decision: In light of the positive reviewers' feedback and relevancy of the submission, we are pleased to accept this paper for presentation at UniReps 2024. We kindly ask the authors to incorporate the reviewers' suggestions and feedback in the final camera-ready version of the manuscript.

Reviews: review: title: title: review
main_review: main_review: The paper combines LfF [49] and LFA [38] with Global Workspace Theory (GWT) (and entropy regularization) into a framework for debiasing called Debiasing Global Workspace (DGW). Specifically, LfF trains two separate classifiers (bias-aligned and bias-conflicting) in an unsupervised manner, exploiting that hard to classify samples are likely bias-conflicting samples. DGW adds an attention module between the representation and classifier part to construct a shared latent space. Additionally, samples are augmented with LFA to increase the amount of bias-conflicting samples.

Strengths:
- S1 The theoretical foundation of GWT is an interesting approach to address the problem
- S2 The approach outperforms unsupervised baselines under consideration in the evaluation

Weaknesses:
- W1 It remains unclear, whether the performance gains are due to DGW or simply due to the combination of LfF and LFA
- W2 While the method is reported as introducing little overhead, the bunch of additional (hyper-)parameters, such as weights for individual loss terms, number of slots and dimensionality in the shared latent space seem to contradict this claim. In particular since different configurations are employed per dataset and ratio of bias-conflicting samples in the training set.
- W3 Whether attention yields interpretability [a1]  or not [a2] in subject to discussion. Either way, the results by anecdotal evidence don't allow to draw strong conclusions, in particular as the interpretation of Fig. 3 is not clear cut.

To me W1 is crucial and I would have expected an ablation study that shows the contribution of individual parts on the performance changes and additionally a baseline that combines LfF and LFA without DGW (e.g., via a simple ensemble). While bibliography is comparably long, related work falls a bit short on recent approaches, such as [a3, a4, a5, ...]. Similarly, an evaluation on commonly used benchmarks in recent work (such as Waterbirds or CelebA) would be desirable.

Minor: the supplementary material indicates that a Titan GPU with 12GB was used for the experiments while footnote 1 indicates that an RTX A6000 with 24GB "could not handle the real-life dataset BFFHQ" (for which reasons?) with SelectMix. Afaik, the A6000 has 48GB, while the older RTX 6000 (without A) has 24GB.

[a1] Attention is not not Explanation - https://arxiv.org/pdf/1908.04626  
[a2] Attention is not Explanation - https://arxiv.org/pdf/1902.10186  
[a3] Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations - https://arxiv.org/pdf/2204.02937  
[a4] Using Early Readouts to Mediate Featural Bias in Distillation - https://arxiv.org/abs/2310.18590  
[a5] Towards Last-layer Retraining for Group Robustness with Fewer Annotations - https://arxiv.org/abs/2309.08534  
[a6] Deep learning face attributes in the wild - https://openaccess.thecvf.com/content_iccv_2015/html/Liu_Deep_Learning_Face_ICCV_2015_paper.html  
[a7] Distributionally Robust Neural Networks - https://openreview.net/pdf?id=ryxGuJrFvS
title: title: A new debiasing technique that does not require spurious label annotations
main_review: main_review: This paper presents a novel debiasing framework, called Debiasing Global Workspace (DGW), which is inspired by Global Workspace Theory (GWT) from cognitive science. DGW offers an alternative approach by leveraging GWT. It proposes specialized modules for learning disentangled representations of intrinsic and biased attributes, with a shared "global workspace" to broadcast relevant information to these modules. The DGW architecture has intrinsic and bias attribute specialists and uses attention-based mechanisms to selectively broadcast information. This allows the model to focus on the intrinsic features during backpropagation, ignoring bias features.
The method is tested on several datasets, including Colored MNIST, Corrupted CIFAR-10, and Biased FFHQ. The results show that DGW achieves better or comparable performance to other debiasing methods, with improved interpretability.

**Strengths**

Novelty: The use of Global Workspace Theory (GWT) for debiasing is innovative, especially in how it mimics modular information processing in the human brain.

Interpretability: DGW provides visualizations of attention masks, allowing insights into which features the model relies on for predictions.

Generalizability: DGW does not require predefined bias labels, making it versatile for different datasets and tasks.

**Weaknesses**

Clarity in Explanation:

The introduction of the DGW framework could be more concise. The explanation of how GWT is adapted could benefit from additional clarity, especially for readers unfamiliar with cognitive theories.

 Figure 1 is difficult to read.

Computational Complexity:

While the paper acknowledges that DGW increases training complexity, it would be beneficial to quantify this increase. A direct comparison of training times and computational resources between DGW and baseline models would make the trade-offs clearer.
title: title: Review for Debiasing Global Workspace: A Cognitive Neural Framework for Learning Debiased and Interpretable Representations
main_review: main_review: This paper proposes a biologically-inspired architecture for what is essentially invariant learning - aiming to learn robust features from potentially biased data. The method is validated empirically on biased versions of the CV MNIST and CIFAR datasets.

Strengths:

The paper is well written, the experiments seem sound and broadly support the claims. I appreciate the visualisations alongside the qualitative evaluation.

Weaknesses:

For all the complexity of the proposed architecture, the results are not very convincing - the performance on the in-distribution data suffers and the improvements on counterfactual examples are marginal.

I am also surprised that other IRM methods are not discussed (e.g. (1)) - they do require partitioning data in environments, but there have been methods proposed to automatically induce those (e.g. (2)).

Some of the claims in the introduction are somewhat detached from reality in my view - e.g. "In modern ML and AI, it has been argued that it is better to build an intelligent system from many interacting specialized modules rather than a single “monolithic” entity to deal with a broad spectrum of conditions and tasks" (ll40-42). I would argue that "modern ML and AI" is where it is precisely because the opposite is the case: large monolithic generalist models that perform well on a variety of tasks and are capable of generalisation to new unseen ones.

(1): https://openaccess.thecvf.com/content/ICCV2021/html/Teney_Unshuffling_Data_for_Improved_Generalization_in_Visual_Question_Answering_ICCV_2021_paper.html

(2): https://arxiv.org/abs/2010.07249

Edit: After finishing the review I have found an existing preprint which has significant overlaps with this work: https://arxiv.org/pdf/2403.14140v1 I recommend the workshop organisers verify that the preprint is indeed from the authors of this submission.
title: title: Debiasing framework is thoroughly benchmarked
main_review: main_review: With my limited knowledge in neuroscience; the paper has immense clarity in detailing of the proposed framework. The datasets were appropriately chosen for the benchmarks and analysis for interpretable attribute representation (intrinsic and biased) proves the efficacy of the approach. 

The proposed framework is a significant contribution and will find application in multiple NLP and ML tasks. 

For readers and practitioners who would like to use this approach, it is advisable to add a section explaining the components of global workspace theory, how it maps to the proposed framework and motivation section to make this framework more intuitive/understood.