GenAI4ELab/papercli-papers huggingface dataset:
Counterfactual Fairness Is Basically Demographic Parity
Authors: Lucas Rosenblatt; R. Teal Witter
Venue: AAAI 2023 (main)

Abstract: Making fair decisions is crucial to ethically implementing machine learning algorithms in social settings. In this work, we consider the celebrated definition of counterfactual fairness. We begin by showing that an algorithm which satisfies counterfactual fairness also satisfies demographic parity, a far simpler fairness constraint. Similarly, we show that all algorithms satisfying demographic parity can be trivially modified to satisfy counterfactual fairness. Together, our results indicate that counterfactual fairness is basically equivalent to demographic parity, which has important implications for the growing body of work on counterfactual fairness. We then validate our theoretical findings empirically, analyzing three existing algorithms for counterfactual fairness against three simple benchmarks. We find that two simple benchmark algorithms outperform all three existing algorithms---in terms of fairness, accuracy, and efficiency---on several data sets. Our analysis leads us to formalize a concrete fairness goal: to preserve the order of individuals within protected groups. We believe transparency around the ordering of individuals within protected groups makes fair algorithms more trustworthy. By design, the two simple benchmark algorithms satisfy this goal while the existing algorithms do not.
Detecting Anomalous Networks of Opioid Prescribers and Dispensers in Prescription Drug Data
Authors: Katie Rosman; Daniel B. Neill
Venue: AAAI 2023 (main)

Abstract: The opioid overdose epidemic represents a serious public health crisis, with fatality rates rising considerably over the past several years. To help address the abuse of prescription opioids, state governments collect data on dispensed prescriptions, yet the use of these data is typically limited to manual searches. In this paper, we propose a novel graph-based framework for detecting anomalous opioid prescribing patterns in state Prescription Drug Monitoring Program (PDMP) data, which could aid governments in deterring opioid diversion and abuse. Specifically, we seek to identify connected networks of opioid prescribers and dispensers who engage in high-risk and possibly illicit activity. We develop and apply a novel extension of the Non-Parametric Heterogeneous Graph Scan (NPHGS) to two years of de-identified PDMP data from the state of Kansas, and find that NPHGS identifies subgraphs that are significantly more anomalous than those detected by other graph-based methods. NPHGS also reveals clusters of potentially illicit activity, which may strengthen state law enforcement and regulatory capabilities. Our paper is the first to demonstrate how prescription data can systematically identify anomalous opioid prescribers and dispensers, as well as illustrating the efficacy of a network-based approach. Additionally, our technical extensions to NPHGS offer both improved flexibility and graph density reduction, enabling the framework to be replicated across jurisdictions and extended to other problem domains.
Practical Disruption of Image Translation Deepfake Networks
Authors: Nataniel Ruiz; Sarah Adel Bargal; Cihang Xie; Stan Sclaroff
Venue: AAAI 2023 (main)

Abstract: By harnessing the latest advances in deep learning, image-to-image translation architectures have recently achieved impressive capabilities. Unfortunately, the growing representational power of these architectures has prominent unethical uses. Among these, the threats of (1) face manipulation ("DeepFakes") used for misinformation or pornographic use (2) "DeepNude" manipulations of body images to remove clothes from individuals, etc. Several works tackle the task of disrupting such image translation networks by inserting imperceptible adversarial attacks into the input image. Nevertheless, these works have limitations that may result in disruptions that are not practical in the real world. Specifically, most works generate disruptions in a white-box scenario, assuming perfect knowledge about the image translation network. The few remaining works that assume a black-box scenario require a large number of queries to successfully disrupt the adversary's image translation network. In this work we propose Leaking Transferable Perturbations (LTP), an algorithm that significantly reduces the number of queries needed to disrupt an image translation network by dynamically re-purposing previous disruptions into new query efficient disruptions.
Daycare Matching in Japan: Transfers and Siblings
Authors: Zhaohong Sun; Yoshihiro Takenami; Daisuke Moriwaki; Yoji Tomita; Makoto Yokoo
Venue: AAAI 2023 (main)

Abstract: In this paper, we study a daycare matching problem in Japan and report the design and implementation of a new centralized algorithm, which is going to be deployed in one municipality in the Tokyo metropolis. There are two features that make this market different from the classical hospital-doctor matching problem: i) some children are initially enrolled and prefer to be transferred to other daycare centers; ii) one family may be associated with two or more children and is allowed to submit preferences over combinations of daycare centers. We revisit some well-studied properties including individual rationality, non-wastefulness, as well as stability, and generalize them to this new setting. We design an algorithm based on integer programming (IP) that captures these properties and conduct experiments on five real-life data sets provided by three municipalities. Experimental results show that i) our algorithm performs at least as well as currently used methods in terms of numbers of matched children and blocking coalition; ii) we can find a stable outcome for all instances, although the existence of such an outcome is not guaranteed in theory.
City-Scale Pollution Aware Traffic Routing by Sampling Max Flows Using MCMC
Authors: Shreevignesh Suriyanarayanan; Praveen Paruchuri; Girish Varma
Venue: AAAI 2023 (main)

Abstract: A significant cause of air pollution in urban areas worldwide is the high volume of road traffic. Long-term exposure to severe pollution can cause serious health issues. One approach towards tackling this problem is to design a pollution-aware traffic routing policy that balances multiple objectives of i) avoiding extreme pollution in any area ii) enabling short transit times, and iii) making effective use of the road capacities. We propose a novel sampling-based approach for this problem. We give the first construction of a Markov Chain that can sample integer max flow solutions of a planar graph, with theoretical guarantees that the probabilities depend on the aggregate transit length. We designed a traffic policy using diverse samples and simulated traffic on real-world road maps using the SUMO traffic simulator. We observe a considerable decrease in areas with severe pollution when experimented with maps of large cities across the world compared to other approaches.
Weather2vec: Representation Learning for Causal Inference with Non-local Confounding in Air Pollution and Climate Studies
Authors: Mauricio Tec; James G. Scott; Corwin M. Zigler
Venue: AAAI 2023 (main)

Abstract: Estimating the causal effects of a spatially-varying intervention on a spatially-varying outcome may be subject to non-local confounding (NLC), a phenomenon that can bias estimates when the treatments and outcomes of a given unit are dictated in part by the covariates of other nearby units. In particular, NLC is a challenge for evaluating the effects of environmental policies and climate events on health-related outcomes such as air pollution exposure. This paper first formalizes NLC using the potential outcomes framework, providing a comparison with the related phenomenon of causal interference. Then, it proposes a broadly applicable framework, termed weather2vec, that uses the theory of balancing scores to learn representations of non-local information into a scalar or vector defined for each observational unit, which is subsequently used to adjust for confounding in conjunction with causal inference methods. The framework is evaluated in a simulation study and two case studies on air pollution where the weather is an (inherently regional) known confounder.
Evaluating Digital Agriculture Recommendations with Causal Inference
Authors: Ilias Tsoumas; Georgios Giannarakis; Vasileios Sitokonstantinou; Alkiviadis Koukos; Dimitra Loka; Nikolaos Bartsotas; Charalampos Kontoes; Ioannis Athanasiadis
Venue: AAAI 2023 (main)

Abstract: In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of smart farming tools. Even though recent advancements in AI-driven digital agriculture can offer high-performing predictive functionalities, they lack tangible quantitative evidence on their benefits to the farmers. Field experiments can derive such evidence, but are often costly, time consuming and hence limited in scope and scale of application. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators (e.g., yield in this case). This way, we can increase farmers' trust via enhancing the transparency of the digital agriculture market, and in turn accelerate the adoption of technologies that aim to secure farmer income resilience and global agricultural sustainability against a changing climate. As a case study, we designed and implemented a recommendation system for the optimal sowing time of cotton based on numerical weather predictions, which was used by a farmers' cooperative during the growing season of 2021. We then leverage agricultural knowledge, collected yield data, and environmental information to develop a causal graph of the farm system. Using the back-door criterion, we identify the impact of sowing recommendations on the yield and subsequently estimate it using linear regression, matching, inverse propensity score weighting and meta-learners. The results revealed that a field sown according to our recommendations exhibited a statistically significant yield increase that ranged from 12% to 17%, depending on the method. The effect estimates were robust, as indicated by the agreement among the estimation methods and four successful refutation tests. We argue that this approach can be implemented for decision support systems of other fields, extending their evaluation beyond a performance assessment of internal functionalities.
Everyone’s Voice Matters: Quantifying Annotation Disagreement Using Demographic Information
Authors: Ruyuan Wan; Jaehyung Kim; Dongyeop Kang
Venue: AAAI 2023 (main)

Abstract: In NLP annotation, it is common to have multiple annotators label the text and then obtain the ground truth labels based on major annotators’ agreement. However, annotators are individuals with different backgrounds and various voices. When annotation tasks become subjective, such as detecting politeness, offense, and social norms, annotators’ voices differ and vary. Their diverse voices may represent the true distribution of people’s opinions on subjective matters. Therefore, it is crucial to study the disagreement from annotation to understand which content is controversial from the annotators. In our research, we extract disagreement labels from five subjective datasets, then fine-tune language models to predict annotators’ disagreement. Our results show that knowing annotators’ demographic information (e.g., gender, ethnicity, education level), in addition to the task text, helps predict the disagreement. To investigate the effect of annotators’ demographics on their disagreement level, we simulate different combinations of their artificial demographics and explore the variance of the prediction to distinguish the disagreement from the inherent controversy from text content and the disagreement in the annotators’ perspective. Overall, we propose an innovative disagreement prediction mechanism for better design of the annotation process that will achieve more accurate and inclusive results for NLP systems. Our code and dataset are publicly available.
MixFairFace: Towards Ultimate Fairness via MixFair Adapter in Face Recognition
Authors: Fu-En Wang; Chien-Yi Wang; Min Sun; Shang-Hong Lai
Venue: AAAI 2023 (main)

Abstract: Although significant progress has been made in face recognition, demographic bias still exists in face recognition systems. For instance, it usually happens that the face recognition performance for a certain demographic group is lower than the others. In this paper, we propose MixFairFace framework to improve the fairness in face recognition models. First of all, we argue that the commonly used attribute-based fairness metric is not appropriate for face recognition. A face recognition system can only be considered fair while every person has a close performance. Hence, we propose a new evaluation protocol to fairly evaluate the fairness performance of different approaches. Different from previous approaches that require sensitive attribute labels such as race and gender for reducing the demographic bias, we aim at addressing the identity bias in face representation, i.e., the performance inconsistency between different identities, without the need for sensitive attribute labels. To this end, we propose MixFair Adapter to determine and reduce the identity bias of training samples. Our extensive experiments demonstrate that our MixFairFace approach achieves state-of-the-art fairness performance on all benchmark datasets.
PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning
Authors: Huandong Wang; Changzheng Gao; Yuchen Wu; Depeng Jin; Lina Yao; Yong Li
Venue: AAAI 2023 (main)

Abstract: Generating human mobility trajectories is of great importance to solve the lack of large-scale trajectory data in numerous applications, which is caused by privacy concerns. However, existing mobility trajectory generation methods still require real-world human trajectories centrally collected as the training data, where there exists an inescapable risk of privacy leakage. To overcome this limitation, in this paper, we propose PateGail, a privacy-preserving imitation learning model to generate mobility trajectories, which utilizes the powerful generative adversary imitation learning model to simulate the decision-making process of humans. Further, in order to protect user privacy, we train this model collectively based on decentralized mobility data stored in user devices, where personal discriminators are trained locally to distinguish and reward the real and generated human trajectories. In the training process, only the generated trajectories and their rewards obtained based on personal discriminators are shared between the server and devices, whose privacy is further preserved by our proposed perturbation mechanisms with theoretical proof to satisfy differential privacy. Further, to better model the human decision-making process, we propose a novel aggregation mechanism of the rewards obtained from personal discriminators. We theoretically prove that under the reward obtained based on the aggregation mechanism, our proposed model maximizes the lower bound of the discounted total rewards of users. Extensive experiments show that the trajectories generated by our model are able to resemble real-world trajectories in terms of five key statistical metrics, outperforming state-of-the-art algorithms by over 48.03%. Furthermore, we demonstrate that the synthetic trajectories are able to efficiently support practical applications, including mobility prediction and location recommendation.
Noise Based Deepfake Detection via Multi-Head Relative-Interaction
Authors: Tianyi Wang; Kam Pui Chow
Venue: AAAI 2023 (main)

Abstract: Deepfake brings huge and potential negative impacts to our daily lives. As the real-life Deepfake videos circulated on the Internet become more authentic, most existing detection algorithms have failed since few visual differences can be observed between an authentic video and a Deepfake one. However, the forensic traces are always retained within the synthesized videos. In this study, we present a noise-based Deepfake detection model, NoiseDF for short, which focuses on the underlying forensic noise traces left behind the Deepfake videos. In particular, we enhance the RIDNet denoiser to extract noise traces and features from the cropped face and background squares of the video image frames. Meanwhile, we devise a novel Multi-Head Relative-Interaction method to evaluate the degree of interaction between the faces and backgrounds that plays a pivotal role in the Deepfake detection task. Besides outperforming the state-of-the-art models, the visualization of the extracted Deepfake forensic noise traces has further displayed the evidence and proved the robustness of our approach.
Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation
Authors: Sheng Xiang; Mingzhi Zhu; Dawei Cheng; Enxia Li; Ruihui Zhao; Yi Ouyang; Ling Chen; Yefeng Zheng
Venue: AAAI 2023 (main)

Abstract: Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well exploit many natural features from unlabeled data. Therefore, we propose a semi-supervised graph neural network for fraud detection. Specifically, we leverage transaction records to construct a temporal transaction graph, which is composed of temporal transactions (nodes) and interactions (edges) among them. Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation. We further model the fraud patterns through risk propagation among transactions. The extensive experiments are conducted on a real-world transaction dataset and two publicly available fraud detection datasets. The result shows that our proposed method, namely GTAN, outperforms other state-of-the-art baselines on three fraud detection datasets. Semi-supervised experiments demonstrate the excellent fraud detection performance of our model with only a tiny proportion of labeled data.
Privacy-Preserved Evolutionary Graph Modeling via Gromov-Wasserstein Autoregression
Authors: Yue Xiang; Dixin Luo; Hongteng Xu
Venue: AAAI 2023 (main)

Abstract: Real-world graphs like social networks are often evolutionary over time, whose observations at different timestamps lead to graph sequences. Modeling such evolutionary graphs is important for many applications, but solving this problem often requires the correspondence between the graphs at different timestamps, which may leak private node information, e.g., the temporal behavior patterns of the nodes. We proposed a Gromov-Wasserstein Autoregressive (GWAR) model to capture the generative mechanisms of evolutionary graphs, which does not require the correspondence information and thus preserves the privacy of the graphs' nodes. This model consists of two autoregressions, predicting the number of nodes and the probabilities of nodes and edges, respectively. The model takes observed graphs as its input and predicts future graphs via solving a joint graph alignment and merging task. This task leads to a fused Gromov-Wasserstein (FGW) barycenter problem, in which we approximate the alignment of the graphs based on a novel inductive fused Gromov-Wasserstein (IFGW) distance. The IFGW distance is parameterized by neural networks and can be learned under mild assumptions, thus, we can infer the FGW barycenters without iterative optimization and predict future graphs efficiently. Experiments show that our GWAR achieves encouraging performance in modeling evolutionary graphs in privacy-preserving scenarios.
Auto-CM: Unsupervised Deep Learning for Satellite Imagery Composition and Cloud Masking Using Spatio-Temporal Dynamics
Authors: Yiqun Xie; Zhili Li; Han Bao; Xiaowei Jia; Dongkuan Xu; Xun Zhou; Sergii Skakun
Venue: AAAI 2023 (main)

Abstract: Cloud masking is both a fundamental and a critical task in the vast majority of Earth observation problems across social sectors, including agriculture, energy, water, etc. The sheer volume of satellite imagery to be processed has fast-climbed to a scale (e.g., >10 PBs/year) that is prohibitive for manual processing. Meanwhile, generating reliable cloud masks and image composite is increasingly challenging due to the continued distribution-shifts in the imagery collected by existing sensors and the ever-growing variety of sensors and platforms. Moreover, labeled samples are scarce and geographically limited compared to the needs in real large-scale applications. In related work, traditional remote sensing methods are often physics-based and rely on special spectral signatures from multi- or hyper-spectral bands, which are often not available in data collected by many -- and especially more recent -- high-resolution platforms. Machine learning and deep learning based methods, on the other hand, often require large volumes of up-to-date training data to be reliable and generalizable over space. We propose an autonomous image composition and masking (Auto-CM) framework to learn to solve the fundamental tasks in a label-free manner, by leveraging different dynamics of events in both geographic domains and time-series. Our experiments show that Auto-CM outperforms existing methods on a wide-range of data with different satellite platforms, geographic regions and bands.
ERASER: AdvERsArial Sensitive Element Remover for Image Privacy Preservation
Authors: Guang Yang; Juan Cao; Danding Wang; Peng Qi; Jintao Li
Venue: AAAI 2023 (main)

Abstract: The daily practice of online image sharing enriches our lives, but also raises a severe issue of privacy leakage. To mitigate the privacy risks during image sharing, some researchers modify the sensitive elements in images with visual obfuscation methods including traditional ones like blurring and pixelating, as well as generative ones based on deep learning. However, images processed by such methods may be recovered or recognized by models, which cannot guarantee privacy. Further, traditional methods make the images very unnatural with low image quality. Although generative methods produce better images, most of them suffer from insufficiency in the frequency domain, which influences image quality. Therefore, we propose the AdvERsArial Sensitive Element Remover (ERASER) to guarantee both image privacy and image quality. 1) To preserve image privacy, for the regions containing sensitive elements, ERASER guarantees enough difference after being modified in an adversarial way. Specifically, we take both the region and global content into consideration with a Prior Transformer and obtain the corresponding region prior and global prior. Based on the priors, ERASER is trained with an adversarial Difference Loss to make the content in the regions different. As a result, ERASER can reserve the main structure and change the texture of the target regions for image privacy preservation. 2) To guarantee the image quality, ERASER improves the frequency insufficiency of current generative methods. Specifically, the region prior and global prior are processed with Fast Fourier Convolution to capture characteristics and achieve consistency in both pixel and frequency domains. Quantitative analyses demonstrate that the proposed ERASER achieves a balance between image quality and image privacy preservation, while qualitative analyses demonstrate that ERASER indeed reduces the privacy risk from the visual perception aspect.