United States

AAAI 2022

Presidential Address

### Welcome!
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

keynote

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. 

Trustworthy AI is being enabled by new federal strategies, policies, and collaborative possibilities – bringing to bear a new paradigm. AI also brings the promise of better and more efficient care, especially for our nation’s Veterans. This session will share several use cases from the work of the National Artificial Intelligence Institute (NAII) at the VA and collaborations, including pilots that empower Veterans to take control of medication adherence, physicians to evaluate COVID-19-associated prognosis and needs, and VHA staff to triage text-based input to quickly identify and assist Veterans in crisis. Establishing an AI network to scale up such research and development will also be covered.

Toward an AI Network for Trustworthy AI

The panelists  present the vision and AI funding priorities of their respective organizations and answer questions from the audience.

Funding Panel

Opening Ceremony and Conference Awards

AAAI Organizational Awards Ceremony

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.

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