United States

AAAI 2022

AAAI Organizational Awards Ceremony

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

keynote

The U.S.A. National Science Foundation has established the Artificial Intelligence Research Institutes awards in 2020. These Institutes represent the nation’s most significant federal investment in AI research and education to date. The panelists will present their Institute’s research vision and results.

AI Institutes Panel

With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions in criminal justice, healthcare, financial lending, and beyond. Interpretability of machine learning models is critical when the cost of a wrong decision is high. Throughout my career, I have had the opportunity to work with power engineers, doctors, and police detectives. Using interpretable models has been the key to allowing me to help them with important high-stakes societal problems. Interpretability can bring us out of the “dark” age of the black box into the age of insight and enlightenment.

Interpretable Machine Learning: Bringing Data Science Out of the â€œDark Ageâ€

The One Hundred Year Study on Artificial Intelligence (AI100) is a long-term investigation of the field of AI and its influences on people, their communities, and society. With oversight from a standing committee administered out of Stanford University, the AI100 convenes a study panel every five years with the goal of issuing a report accessible to AI researchers, policymakers, industry leaders, and the public at large. The reports describe the technical and societal challenges and opportunities that have arisen since the previous report and envision potential future advances. This talk will summarize the 2nd report, issued September 2021.

Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report

AAAI Community Meeting

Understanding how a city's physical appearance and environmental surroundings impact society traits, such as safety, is an essential issue in social artificial intelligence. To demonstrate the relationship, most existing studies utilize subjective human perceptual attributes, categorization only for a few violent crimes, and images taken from still shot images. These lead to difficulty in identifying location-specific characteristics for urban safety. In this work, to address this problem, we propose a large-scale dataset and a novel method by adopting a concept of â€œDeviance" which explains behaviors violating social norms, both formally (e.g. crime) and informally (e.g. civil complaints). We first collect a geo-tagged dataset consisting of incident report data for seven metropolitan cities, with corresponding sequential images around incident sites obtained from Google street view. We also design a convolutional neural network that learns spatio-temporal visual attributes of deviant streets. Experimental results show that our framework can reliably recognize real-world deviance in various cities. Furthermore, we analyze which visual attribute is important for deviance identification and severity estimation. We have released our dataset and source codes at our project page: https://deviance-project.github.io/DevianceNet/

DevianceNet: Learning to Predict Deviance from A Large-Scale Geo-Tagged Dataset

Impossibility results show that important fairness measures (independence, separation, sufficiency) cannot be satisfied at the same time under reasonable assumptions. This paper explores whether we can satisfy and/or improve these fairness measures simultaneously to a certain degree. We introduce information-theoretic formulations of the fairness measures and define degrees of fairness based on these formulations. The information-theoretic formulations suggest unexplored theoretical relations between the three fairness measures. In the experimental part, we use the information-theoretic expressions as regularizers to obtain fairness-regularized predictors for three standard datasets. Our experiments show that a) fairness regularization directly increases fairness measures, in line with existing work, and b) some fairness regularizations indirectly increase other fairness measures, as suggested by our theoretical findings. This establishes that it is possible to increase the degree to which some fairness measures are satisfied at the same time -- some fairness measures are gradually compatible.

Gradual (In)Compatibility of Fairness Criteria

Knowledge of the changing traffic is critical in risk management. Customs offices worldwide have traditionally relied on local resources to accumulate such knowledge and detect tax frauds. This naturally poses countries with weak infrastructure to become tax havens of potentially illicit trades. The current paper proposes DAS, a memory bank platform to facilitate knowledge sharing across multi-national customs administrations to support each other. We propose a domain adaptation method to share transferable knowledge of frauds as prototypes while safeguarding the local trade information. Data encompassing over 8 million import declarations have been used to test the feasibility of this new system, which shows that participating countries may benefit up to 2-11 times in fraud detection with the help of shared knowledge. We discuss implications for substantial tax revenue potential and strengthened policy against illicit trades.

Knowledge Sharing via Domain Adaptation in Customs Fraud Detection

Food waste and insecurity are two societal challenges that coexist in many parts of the world. A prominent force to combat these issues, food rescue platforms match food donations to organizations that serve underprivileged communities, and then rely on external volunteers to transport the food. Previous work has developed machine learning models for food rescue volunteer engagement. However, having long worked with domain practitioners to deploy AI tools to help with food rescues, we understand that there are four main pain points that keep such a machine learning model from being actually useful in practice: small data, data collected only under the default intervention, unmodeled objectives due to communication gap, and unforeseen consequences of the intervention. In this paper, we introduce bandit data-driven optimization which not only helps address these pain points in food rescue, but also is applicable to other nonprofit domains that share similar challenges. Bandit data-driven optimization combines the advantages of online bandit learning and offline predictive analytics in an integrated framework. We propose PROOF, a novel algorithm for this framework and formally prove that it has no-regret. We show that PROOF performs better than existing baseline on food rescue volunteer recommendation.

Bandit Data-Driven Optimization for Crowdsourcing Food Rescue Platforms

Federated Learning (FL) is a machine learning paradigm where  local nodes collaboratively train a central model while the training data remains decentralized. Existing FL methods typically share model parameters or employ co-distillation to address the issue of unbalanced data distribution. However, they suffer from communication bottlenecks. More importantly, they risk privacy leakage risk. In this work, we develop a privacy preserving  and communication efficient method in a FL framework with one-shot offline knowledge distillation using unlabeled, cross-domain, non-sensitive public data. We propose a quantized and noisy ensemble of local predictions from completely trained local models for stronger privacy guarantees without sacrificing accuracy. Based on extensive experiments on image classification and text classification tasks, we show that our method outperforms baseline FL algorithms with superior performance in both accuracy and data privacy preservation.

Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation

Recent works in artificial intelligence fairness attempt to mitigate discrimination by proposing constrained optimization programs that achieve parity for some fairness statistic. Most assume availability of the class label, which is impractical in many real-world applications such as precision medicine, actuarial analysis and recidivism prediction. Here we consider fairness in longitudinal right-censored environments, where the time to event might be unknown, resulting in censorship of the class label and inapplicability of existing fairness studies. We devise applicable fairness measures, propose a debiasing algorithm, and provide necessary theoretical constructs to bridge fairness with and without censorship for these important and socially-sensitive tasks. Our experiments on four censored datasets confirm the utility of our approach.

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