On the Opportunity of Causal Deep Generative Models: A Survey and Future Directions

Deep generative models have gained popularity in recent years due to their ability to accurately replicate inherent empirical distributions and yield novel samples. In particular, certain advances are proposed wherein the model engenders data examples following specified attributes. Nevertheless, several challenges still exist and are to be overcome, i.e., difficulty in extrapolating out-of-sample data and insufficient learning of disentangled representations. Structural causal models (SCMs), on the other hand, encapsulate the causal factors that govern a generative process and characterize a generative model based on causal relationships, providing crucial insights for addressing the current obstacles in deep generative models. In this paper, we present a comprehensive survey of Causal deep Generative Models (CGMs), which combine SCMs and deep generative models in a way that boosts several trustworthy properties such as robustness, fairness, and interpretability. We provide an overview of the recent advances in CGMs, categorize them based on generative types, and discuss how causality is introduced into the family of deep generative models. We also explore potential avenues for future research in this field.

IronForge: An Open, Secure, Fair, Decentralized Federated Learning

Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, the over-dependence on a central coordinator, and the lack of an open and fair incentive mechanism collectively hinder its further development. We propose \textsc{IronForge}, a new generation of FL framework, that features a Directed Acyclic Graph (DAG)-based data structure and eliminates the need for central coordinators to achieve fully decentralized operations. \textsc{IronForge} runs in a public and open network, and launches a fair incentive mechanism by enabling state consistency in the DAG, so that the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of \textsc{IronForge}. Experimental results based on a newly developed testbed FLSim highlight the superiority of \textsc{IronForge} to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, \textsc{IronForge} is the first secure and fully decentralized FL framework that can be applied in open networks with realistic network and training settings.

Operationalising Responsible AI Using a Pattern-Oriented Approach: A Case Study on Chatbots in Financial Services

Responsible AI is the practice of developing and using AI systems in a way that benefits the humans, society, and environment, while minimising the risk of negative consequences. Various responsible AI principles have been released recently. However, those principles are very abstract and not practical enough. Further, significant efforts have been put on algorithm-level solutions which are usually confined to a narrow set of principles (such as fairness and privacy). To bridge the gap, we adopt a pattern-oriented approach and build a responsible AI pattern catalogue for operationalising responsible AI from a system perspective. In this article, we first summarise the major challenges in operationalising responsible AI at scale and introduce how we use responsible AI pattern catalogue to address those challenges. Then, we discuss the case study we have conducted using the chatbot development use case to evaluate the usefulness of the pattern catalogue.

Responsible AI Pattern Catalogue: A Multivocal Literature Review

Responsible AI has been widely considered as one of the greatest scientific challenges of our time and the key to increase the adoption of AI. A number of AI ethics principles frameworks have been published recently. However, without further best practice guidance, practitioners are left with nothing much beyond truisms. Also, significant efforts have been placed at algorithm-level rather than system-level, mainly focusing on a subset of mathematics-amenable ethical principles (such as fairness). Nevertheless, ethical issues can occur at any step of the development lifecycle crosscutting many AI and non-AI components of systems beyond AI algorithms and models. To operationalize responsible AI from a system perspective, in this paper, we present a Responsible AI Pattern Catalogue based on the results of a Multivocal Literature Review (MLR). Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle. The Responsible AI Pattern Catalogue classifies the patterns into three groups: multi-level governance patterns, trustworthy process patterns, and responsible-AI-by-design product patterns. These patterns provide a systematic and actionable guidance for stakeholders to implement responsible AI.

Responsible AI Pattern Catalogue: a Multivocal Literature Review

Responsible AI has been widely considered as one of the greatest scientific challenges of our time and the key to unlock the AI market and increase the adoption. To address the responsible AI challenge, a number of AI ethics principles frameworks have been published recently, which AI systems are supposed to conform to. However, without further best practice guidance, practitioners are left with nothing much beyond truisms. Also, significant efforts have been placed at algorithm-level rather than system-level, mainly focusing on a subset of mathematics-amenable ethical principles (such as privacy and fairness). Nevertheless, ethical issues can occur at any step of the development lifecycle crosscutting many AI, non-AI and data components of systems beyond AI algorithms and models. To operationalize responsible AI from a system perspective, in this paper, we adopt a pattern-oriented approach and present a Responsible AI Pattern Catalogue based on the results of a systematic Multivocal Literature Review (MLR). Rather than staying at the ethical principle level or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle. The Responsible AI Pattern Catalogue classifies patterns into three groups: multi-level governance patterns, trustworthy process patterns, and responsible-AI-by-design product patterns. These patterns provide a systematic and actionable guidance for stakeholders to implement responsible AI.

Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects

We regularly consider answering counterfactual questions in practice, such as "Would people with diabetes take a turn for the better had they choose another medication?". Observational studies are growing in significance in answering such questions due to their widespread accumulation and comparatively easier acquisition than Randomized Control Trials (RCTs). Recently, some works have introduced representation learning and domain adaptation into counterfactual inference. However, most current works focus on the setting of binary treatments. None of them considers that different treatments' sample sizes are imbalanced, especially data examples in some treatment groups are relatively limited due to inherent user preference. In this paper, we design a new algorithmic framework for counterfactual inference, which brings an idea from Meta-learning for Estimating Individual Treatment Effects (MetaITE) to fill the above research gaps, especially considering multiple imbalanced treatments. Specifically, we regard data episodes among treatment groups in counterfactual inference as meta-learning tasks. We train a meta-learner from a set of source treatment groups with sufficient samples and update the model by gradient descent with limited samples in target treatment. Moreover, we introduce two complementary losses. One is the supervised loss on multiple source treatments. The other loss which aligns latent distributions among various treatment groups is proposed to reduce the discrepancy. We perform experiments on two real-world datasets to evaluate inference accuracy and generalization ability. Experimental results demonstrate that the model MetaITE matches/outperforms state-of-the-art methods.

Interventional Recommendation with Contrastive Counterfactual Learning for Better Understanding User Preferences

Recently, there has been a surging interest in formulating recommendations in the context of causal inference. The studies regard the recommendation as an intervention in causal inference and frame the users' preferences as interventional effects to improve recommender systems' generalization. Many studies in the field of causal inference for recommender systems have been focusing on utilizing propensity scores from the causal community that reduce the bias while inducing additional variance. Alternatively, some studies suggest the existence of a set of unbiased data from randomized controlled trials while it requires to satisfy certain assumptions that may be challenging in practice. In this paper, we first design a causal graph representing recommender systems' data generation and propagation process. Then, we reveal that the underlying exposure mechanism biases the maximum likelihood estimation (MLE) on observational feedback. In order to figure out users' preferences in terms of causality behind data, we leverage the back-door adjustment and do-calculus, which induces an interventional recommendation model (IREC). Furthermore, considering the confounder may be inaccessible for measurement, we propose a contrastive counterfactual learning method (CCL) for simulating the intervention. In addition, we present two extra novel sampling strategies and show an intriguing finding that sampling from counterfactual sets contributes to superior performance. We perform extensive experiments on two real-world datasets to evaluate and analyze the performance of our model IREC-CCL on unbiased test sets. Experimental results demonstrate our model outperforms the state-of-the-art methods.

Psychologically-Inspired, Unsupervised Inference of Perceptual Groups of GUI Widgets from GUI Images

Graphical User Interface (GUI) is not merely a collection of individual and unrelated widgets, but rather partitions discrete widgets into groups by various visual cues, thus forming higher-order perceptual units such as tab, menu, card or list. The ability to automatically segment a GUI into perceptual groups of widgets constitutes a fundamental component of visual intelligence to automate GUI design, implementation and automation tasks. Although humans can partition a GUI into meaningful perceptual groups of widgets in a highly reliable way, perceptual grouping is still an open challenge for computational approaches. Existing methods rely on ad-hoc heuristics or supervised machine learning that is dependent on specific GUI implementations and runtime information. Research in psychology and biological vision has formulated a set of principles (i.e., Gestalt theory of perception) that describe how humans group elements in visual scenes based on visual cues like connectivity, similarity, proximity and continuity. These principles are domain-independent and have been widely adopted by practitioners to structure content on GUIs to improve aesthetic pleasant and usability. Inspired by these principles, we present a novel unsupervised image-based method for inferring perceptual groups of GUI widgets. Our method requires only GUI pixel images, is independent of GUI implementation, and does not require any training data. The evaluation on a dataset of 1,091 GUIs collected from 772 mobile apps and 20 UI design mockups shows that our method significantly outperforms the state-of-the-art ad-hoc heuristics-based baseline. Our perceptual grouping method creates the opportunities for improving UI-related software engineering tasks.

Towards a Roadmap on Software Engineering for Responsible AI

Although AI is transforming the world, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and frameworks for responsible AI have been issued recently. However, they are high level and difficult to put into practice. On the other hand, most AI researchers focus on algorithmic solutions, while the responsible AI challenges actually crosscut the entire engineering lifecycle and components of AI systems. To close the gap in operationalizing responsible AI, this paper aims to develop a roadmap on software engineering for responsible AI. The roadmap focuses on (i) establishing multi-level governance for responsible AI systems, (ii) setting up the development processes incorporating process-oriented practices for responsible AI systems, and (iii) building responsible-AI-by-design into AI systems through system-level architectural style, patterns and techniques.