GenAI4ELab/papercli-papers huggingface dataset:
Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness
Authors: Abdelrahman Zayed; Prasanna Parthasarathi; Gonçalo Mordido; Hamid Palangi; Samira Shabanian; Sarath Chandar
Venue: AAAI 2023 (main)

Abstract: Data-driven predictive solutions predominant in commercial applications tend to suffer from biases and stereotypes, which raises equity concerns. Prediction models may discover, use, or amplify spurious correlations based on gender or other protected personal characteristics, thus discriminating against marginalized groups. Mitigating gender bias has become an important research focus in natural language processing (NLP) and is an area where annotated corpora are available. Data augmentation reduces gender bias by adding counterfactual examples to the training dataset. In this work, we show that some of the examples in the augmented dataset can be not important or even harmful to fairness. We hence propose a general method for pruning both the factual and counterfactual examples to maximize the model’s fairness as measured by the demographic parity, equality of opportunity, and equality of odds. The fairness achieved by our method surpasses that of data augmentation on three text classification datasets, using no more than half of the examples in the augmented dataset. Our experiments are conducted using models of varying sizes and pre-training settings. WARNING: This work uses language that is offensive in nature.
On the Effectiveness of Curriculum Learning in Educational Text Scoring
Authors: Zijie Zeng; Dragan Gasevic; Guangliang Chen
Venue: AAAI 2023 (main)

Abstract: Automatic Text Scoring (ATS) is a widely-investigated task in education. Existing approaches often stressed the structure design of an ATS model and neglected the training process of the model. Considering the difficult nature of this task, we argued that the performance of an ATS model could be potentially boosted by carefully selecting data of varying complexities in the training process. Therefore, we aimed to investigate the effectiveness of curriculum learning (CL) in scoring educational text. Specifically, we designed two types of difficulty measurers: (i) pre-defined, calculated by measuring a sample's readability, length, the number of grammatical errors or unique words it contains; and (ii) automatic, calculated based on whether a model in a training epoch can accurately score the samples. These measurers were tested in both the easy-to-hard to hard-to-easy training paradigms. Through extensive evaluations on two widely-used datasets (one for short answer scoring and the other for long essay scoring), we demonstrated that (a) CL indeed could boost the performance of state-of-the-art ATS models, and the maximum improvement could be up to 4.5%, but most improvements were achieved when assessing short and easy answers; (b) the pre-defined measurer calculated based on the number of grammatical errors contained in a text sample tended to outperform the other difficulty measurers across different training paradigms.
Censored Fairness through Awareness
Authors: Wenbin Zhang; Tina Hernandez-Boussard; Jeremy Weiss
Venue: AAAI 2023 (main)

Abstract: There has been increasing concern within the machine learning community and beyond that Artificial Intelligence (AI) faces a bias and discrimination crisis which needs AI fairness with urgency. As many have begun to work on this problem, most existing work depends on the availability of class label for the given fairness definition and algorithm which may not align with real-world usage. In this work, we study an AI fairness problem that stems from the gap between the design of a "fair" model in the lab and its deployment in the real-world. Specifically, we consider defining and mitigating individual unfairness amidst censorship, where the availability of class label is not always guaranteed due to censorship, which is broadly applicable in a diversity of real-world socially sensitive applications. We show that our method is able to quantify and mitigate individual unfairness in the presence of censorship across three benchmark tasks, which provides the first known results on individual fairness guarantee in analysis of censored data.
A Continual Pre-training Approach to Tele-Triaging Pregnant Women in Kenya
Authors: Wenbo Zhang; Hangzhi Guo; Prerna Ranganathan; Jay Patel; Sathyanath Rajasekharan; Nidhi Danayak; Manan Gupta; Amulya Yadav
Venue: AAAI 2023 (main)

Abstract: Access to high-quality maternal health care services is limited in Kenya, which resulted in ∼36,000 maternal and neonatal deaths in 2018. To tackle this challenge, Jacaranda Health (a non-profit organization working on maternal health in Kenya) developed PROMPTS, an SMS based tele-triage system for pregnant and puerperal women, which has more than 350,000 active users in Kenya. PROMPTS empowers pregnant women living far away from doctors and hospitals to send SMS messages to get quick answers (through human helpdesk agents) to questions about their medical symptoms and pregnancy status. Unfortunately, ∼1.1 million SMS messages are received by PROMPTS every month, which makes it challenging for helpdesk agents to ensure that these messages can be interpreted correctly and evaluated by their level of emergency to ensure timely responses and/or treatments for women in need. This paper reports on a collaborative effort with Jacaranda Health to develop a state-of-the-art natural language processing (NLP) framework, TRIM-AI (TRIage for Mothers using AI), which can automatically predict the emergency level (or severity of medical condition) of a pregnant mother based on the content of their SMS messages. TRIM-AI leverages recent advances in multi-lingual pre-training and continual pre-training to tackle code-mixed SMS messages (between English and Swahili), and achieves a weighted F1 score of 0.774 on real-world datasets. TRIM-AI has been successfully deployed in the field since June 2022, and is being used by Jacaranda Health to prioritize the provision of services and care to pregnant women with the most critical medical conditions. Our preliminary A/B tests in the field show that TRIM-AI is ∼17% more accurate at predicting high-risk medical conditions from SMS messages sent by pregnant Kenyan mothers, which reduces the helpdesk’s workload by ∼12%.
Future Aware Pricing and Matching for Sustainable On-Demand Ride Pooling
Authors: Xianjie Zhang; Pradeep Varakantham; Hao Jiang
Venue: AAAI 2023 (main)

Abstract: The popularity of on-demand ride pooling is owing to the benefits offered to customers (lower prices), taxi drivers (higher revenue), environment (lower carbon footprint due to fewer vehicles) and aggregation companies like Uber (higher revenue). To achieve these benefits, two key interlinked challenges have to be solved effectively: (a) pricing -- setting prices to customer requests for taxis; and (b) matching -- assignment of customers (that accepted the prices) to taxis/cars. Traditionally, both these challenges have been studied individually and using myopic approaches (considering only current requests), without considering the impact of current matching on addressing future requests. In this paper, we develop a novel framework that handles the pricing and matching problems together, while also considering the future impact of the pricing and matching decisions. In our experimental results on a real-world taxi dataset, we demonstrate that our framework can significantly improve revenue (up to 17% and on average 6.4%) in a sustainable manner by reducing the number of vehicles  (up to 14% and on average 10.6%) required to obtain a given fixed revenue and the overall distance travelled by vehicles (up to 11.1% and on average 3.7%). That is to say, we are able to provide an ideal win-win scenario for all stakeholders (customers, drivers, aggregator, environment) involved by obtaining higher revenue for customers, drivers, aggregator (ride pooling company) while being good for the environment (due to fewer number of vehicles on the road and lesser fuel consumed).
A Crowd-AI Collaborative Duo Relational Graph Learning Framework towards Social Impact Aware Photo Classification
Authors: Yang Zhang; Ziyi Kou; Lanyu Shang; Huimin Zeng; Zhenrui Yue; Dong Wang
Venue: AAAI 2023 (main)

Abstract: In artificial intelligence (AI), negative social impact (NSI) represents the negative effect on the society as a result of mistakes conducted by AI agents. While the photo classification problem has been widely studied in the AI community, the NSI made by photo misclassification is largely ignored due to the lack of quantitative measurements of the NSI and effective approaches to reduce it. In this paper, we focus on an NSI-aware photo classification problem where the goal is to develop a novel crowd-AI collaborative learning framework that leverages online crowd workers to quantitatively estimate and effectively reduce the NSI of misclassified photos. Our problem is motivated by the limitations of current NSI-aware photo classification approaches that either 1) cannot accurately estimate NSI because they simply model NSI as the semantic difference between true and misclassified categories or 2) require costly human annotations to estimate NSI of pairwise class categories. To address such limitations, we develop SocialCrowd, a crowdsourcing-based NSI-aware photo classification framework that explicitly reduces the NSI of photo misclassification by designing a duo relational NSI-aware graph with the NSI estimated by online crowd workers. The evaluation results on two large-scale image datasets show that SocialCrowd not only reduces the NSI of photo misclassification but also improves the classification accuracy on both datasets.
People Taking Photos That Faces Never Share: Privacy Protection and Fairness Enhancement from Camera to User
Authors: Junjie Zhu; Lin Gu; Xiaoxiao Wu; Zheng Li; Tatsuya Harada; Yingying Zhu
Venue: AAAI 2023 (main)

Abstract: The soaring number of personal mobile devices and public cameras poses a threat to fundamental human rights and ethical principles. For example, the stolen of private information such as face image by malicious third parties will lead to catastrophic consequences. By manipulating appearance of face in the image, most of existing protection algorithms are effective but irreversible. Here, we propose a practical and systematic solution to invertiblely protect face information in the full-process pipeline from camera to final users. Specifically, We design a novel lightweight Flow-based Face Encryption Method (FFEM) on the local embedded system privately connected to the camera,  minimizing the risk of  eavesdropping during data transmission. FFEM uses a flow-based face encoder to encode each face to a Gaussian distribution and encrypts the encoded face feature by random rotating the Gaussian distribution with the rotation matrix is as the password. While encrypted latent-variable face  images  are sent to users through public but less reliable channels, password will be protected through more secure channels through technologies such as asymmetric encryption, blockchain, or other sophisticated security schemes. User could select to decode an image with fake faces from the encrypted image on the public channel. Only trusted users are able to recover the original face  using the encrypted matrix transmitted in secure channel. More interestingly, by  tuning Gaussian ball in latent space, we could control the fairness of the replaced face on attributes such as gender and race. Extensive experiments demonstrate that our solution could protect privacy and enhance fairness with minimal effect on high-level downstream task.
OpenMapFlow: A Library for Rapid Map Creation with Machine Learning and Remote Sensing Data
Authors: Ivan Zvonkov; Gabriel Tseng; Catherine Nakalembe; Hannah Kerner
Venue: AAAI 2023 (main)

Abstract: The desired output for most real-world tasks using machine learning (ML) and remote sensing data is a set of dense predictions that form a predicted map for a geographic region. However, most prior work involving ML and remote sensing follows the traditional practice of reporting metrics on a set of independent, geographically-sparse samples and does not perform dense predictions. To reduce the labor of producing dense prediction maps, we present OpenMapFlow---an open-source python library for rapid map creation with ML and remote sensing data. OpenMapFlow provides 1) a data processing pipeline for users to create labeled datasets for any region, 2) code to train state-of-the-art deep learning models on custom or existing datasets, and 3) a cloud-based architecture to deploy models for efficient map prediction. We demonstrate the benefits of OpenMapFlow through experiments on three binary classification tasks: cropland, crop type (maize), and building mapping. We show that OpenMapFlow drastically reduces the time required for dense prediction compared to traditional workflows. We hope this library will stimulate novel research in areas such as domain shift, unsupervised learning, and societally-relevant applications and lessen the barrier to adopting research methods for real-world tasks.
Formally Verified SAT-Based AI Planning
Authors: Mohammad Abdulaziz; Friedrich Kurz
Venue: AAAI 2023 (main)

Abstract: We present an executable formally verified SAT encoding of ground classical AI planning problems. We use the theorem prover Isabelle/HOL to perform the verification. We experimentally test the verified encoding and show that it can be used for reasonably sized standard planning benchmarks. We also use it as a reference to test a state-of-the-art SAT-based
planner, showing that it sometimes falsely claims that problems have no solutions of certain lengths.
Shielding in Resource-Constrained Goal POMDPs
Authors: Michal Ajdarów; Šimon Brlej; Petr Novotný
Venue: AAAI 2023 (main)

Abstract: We consider partially observable Markov decision processes (POMDPs) modeling an agent that needs a supply of a certain resource (e.g., electricity stored in batteries) to operate correctly. The resource is consumed by the agent's actions and can be replenished only in certain states. The agent aims to minimize the expected cost of reaching some goal while preventing resource exhaustion, a problem we call resource-constrained goal optimization (RSGO). We take a two-step approach to the RSGO problem. First, using formal methods techniques, we design an algorithm computing a shield for a given scenario: a procedure that observes the agent and prevents it from using actions that might eventually lead to resource exhaustion. Second, we augment the POMCP heuristic search algorithm for POMDP planning with our shields to obtain an algorithm solving the RSGO problem. We implement our algorithm and present experiments showing its applicability to benchmarks from the literature.
Implicit Bilevel Optimization: Differentiating through Bilevel Optimization Programming
Authors: Francesco Alesiani
Venue: AAAI 2023 (main)

Abstract: Bilevel Optimization Programming is used to model complex and conflicting interactions between agents, for example in Robust AI or Privacy preserving AI. Integrating bilevel mathematical programming within deep learning is thus an essential objective for the Machine Learning community.  
Previously proposed approaches only consider single-level programming. In this paper, we extend existing single-level optimization programming approaches and thus propose Differentiating through Bilevel Optimization Programming (BiGrad) for end-to-end learning of models that use Bilevel Programming as a layer. 
BiGrad has wide applicability and can be used in modern machine learning frameworks. BiGrad is applicable to both continuous and combinatorial Bilevel optimization problems. We describe a class of gradient estimators for the combinatorial case which reduces the requirements in terms of computation complexity; for the case of the continuous variable, the gradient computation takes advantage of the push-back approach (i.e. vector-jacobian product) for an efficient implementation. Experiments show that the BiGrad successfully extends existing single-level approaches to Bilevel Programming.
Query-Based Hard-Image Retrieval for Object Detection at Test Time
Authors: Edward Ayers; Jonathan Sadeghi; John Redford; Romain Mueller; Puneet K. Dokania
Venue: AAAI 2023 (main)

Abstract: There is a longstanding interest in capturing the error behaviour of object detectors by finding images where their performance is likely to be unsatisfactory. In real-world applications such as autonomous driving, it is also crucial to characterise potential failures beyond simple requirements of detection performance. For example, a missed detection of a pedestrian close to an ego vehicle will generally require closer inspection than a missed detection of a car in the distance. The problem of predicting such potential failures at test time  has largely been overlooked in the literature and conventional approaches based on detection uncertainty fall short in that they are agnostic to such fine-grained characterisation of errors. In this work, we propose to reformulate the problem of finding "hard" images as a query-based hard image retrieval task, where queries are specific definitions of "hardness", and offer a simple and intuitive method that can solve this task for a large family of queries. Our method is entirely post-hoc, does not require ground-truth annotations, is independent of the choice of a detector, and relies on an efficient Monte Carlo estimation that uses a simple stochastic model in place of the ground-truth. We show experimentally that it can be applied successfully to a wide variety of queries for which it can reliably identify hard images for a given detector without any labelled data. We provide results on ranking and classification tasks using the widely used RetinaNet, Faster-RCNN, Mask-RCNN, and Cascade Mask-RCNN object detectors. The code for this project is available at https://github.com/fiveai/hardest.
Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty
Authors: Thom Badings; Licio Romao; Alessandro Abate; Nils Jansen
Venue: AAAI 2023 (main)

Abstract: Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Stochastic noise causes aleatoric uncertainty, whereas imprecise knowledge of model parameters leads to epistemic uncertainty. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. However, the underlying models exclusively capture aleatoric but not epistemic uncertainty, and thus require that model parameters are known precisely. Our contribution to overcoming this restriction is a novel abstraction-based controller synthesis method for continuous-state models with stochastic noise and uncertain parameters. By sampling techniques and robust analysis, we capture both aleatoric and epistemic uncertainty, with a user-specified confidence level, in the transition probability intervals of a so-called interval Markov decision process (iMDP). We synthesize an optimal policy on this iMDP, which translates (with the specified confidence level) to a feedback controller for the continuous model with the same performance guarantees. Our experimental benchmarks confirm that accounting for epistemic uncertainty leads to controllers that are more robust against variations in parameter values.
Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling
Authors: Sirui Bi; Victor Fung; Jiaxin Zhang
Venue: AAAI 2023 (main)

Abstract: In the scope of "AI for Science", solving inverse problems is a longstanding challenge in materials and drug discovery, where the goal is to determine the hidden structures given a set of desirable properties. Deep generative models are recently proposed to solve inverse problems, but these are currently struggling in expensive forward operators, precisely localizing the exact solutions and fully exploring the parameter spaces without missing solutions. In this work, we propose a novel approach (called iPage) to accelerate the inverse learning process by leveraging probabilistic inference from deep invertible models and deterministic optimization via fast gradient descent.  Given a target property, the learned invertible model provides a posterior over the parameter space; we identify these posterior samples as an intelligent prior initialization which enables us to narrow down the search space. We then perform gradient descent to calibrate the inverse solutions within a local region. Meanwhile, a space-filling sampling is imposed on the latent space to better explore and capture all possible solutions. We evaluate our approach on three benchmark tasks and create two datasets of real-world applications from quantum chemistry and additive manufacturing and find our method achieves superior performance compared to several state-of-the-art baseline methods. The iPage code is available at https://github.com/jxzhangjhu/MatDesINNe.
Attention-Conditioned Augmentations for Self-Supervised Anomaly Detection and Localization
Authors: Behzad Bozorgtabar; Dwarikanath Mahapatra
Venue: AAAI 2023 (main)

Abstract: Self-supervised anomaly detection and localization are critical to real-world scenarios in which collecting anomalous samples and pixel-wise labeling is tedious or infeasible, even worse when a wide variety of unseen anomalies could surface at test time. Our approach involves a pretext task in the context of masked image modeling, where the goal is to impose agreement between cluster assignments obtained from the representation of an image view containing saliency-aware masked patches and the uncorrupted image view. We harness the self-attention map extracted from the transformer to mask non-salient image patches without destroying the crucial structure associated with the foreground object. Subsequently, the pre-trained model is fine-tuned to detect and localize simulated anomalies generated under the guidance of the transformer's self-attention map. We conducted extensive validation and ablations on the benchmark of industrial images and achieved superior performance against competing methods. We also show the adaptability of our method to the medical images of the chest X-rays benchmark.
Robust-by-Design Classification via Unitary-Gradient Neural Networks
Authors: Fabio Brau; Giulio Rossolini; Alessandro Biondi; Giorgio Buttazzo
Venue: AAAI 2023 (main)

Abstract: The use of neural networks in safety-critical systems requires safe and robust models, due to the existence of adversarial attacks. Knowing the minimal adversarial perturbation of any input x, or, equivalently, knowing the distance of x from the classification boundary, allows evaluating the classification robustness, providing certifiable predictions. Unfortunately, state-of-the-art techniques for computing such a distance are computationally expensive and hence not suited for online applications. This work proposes a novel family of classifiers, namely Signed Distance Classifiers (SDCs), that, from a theoretical perspective, directly output the exact distance of x from the classification boundary, rather than a probability score (e.g., SoftMax). SDCs represent a family of robust-by-design classifiers. To practically address the theoretical requirements of an SDC, a novel network architecture named Unitary-Gradient Neural Network is presented. Experimental results show that the proposed architecture approximates a signed distance classifier, hence allowing an online certifiable classification of x at the cost of a single inference.