AAAI 2024 Spring Symposium Series

March 25-27, 2024 | Stanford University, Stanford, California

Sponsored by the Association for the Advancement of Artificial Intelligence 

The Association for the Advancement of Artificial Intelligence is pleased to present the 2024 Spring Symposium Series, to be held at Stanford University in the Lane History Corner/Bldg. 200, Monday – Wednesday, March 25-27, 2024. 

Symposia generally range from 40–75 participants each. Participation was open to active participants as well as other interested individuals on a first-come, first-served basis. Each participant was expected to attend a single symposium. 

The program included the following eight symposia: 

• Bi-directionality in Human-AI Collaborative Systems
• Clinical Foundation Models Symposium
• Empowering Machine Learning and Large Language Models with Domain and Commonsense Knowledge (AAAI-MAKE 2024)
• Federated Learning on the Edge
• Impact of GenAI on Social and Individual Well-being
• Increasing Diversity in AI Education and Research
• Symposium on Human-Like Learning
• User-Aligned Assessment of Adaptive AI Systems

AAAI Code of Conduct for Events and Conferences 

All persons, organizations and entities that attend AAAI conferences and events are subject to the standards of conduct set forth on the AAAI Code of Conduct for Events and Conferences. 

Registration and General Information 

Registration is Closed

Registration Fees

The conference registration fee includes admission to one symposium, access to the electronic proceedings, coffee breaks, and the opening reception.  

Refund Requests

The deadline for refund requests is February 12, 2024. All refund requests must be made in writing to sss@aaai.org. A $50.00 processing fee will be assessed for all refunds.

Fee Schedule 

Member: $395.00 
Nonmember: $560.00 
Student Member: $225.00 
Nonmember student: $335.00 

AAAI Silver Registration 

(Includes 1 years of AAAI membership, plus the conference registration fee)  
Regular One-Year: $540.00 
Regular 3-Year: $830.00 
Regular 5-Year: $1,120.00 
Student (One-Year): $300.00 

Visa Information 

Letters of invitation can be requested by accepted SSS-24 authors or registrants with a completed registration with payment. You can access the visa letter form via the link in your registration confirmation email.

Event Locations:

Symposia Rooms – Lane History Corner/Bldg. 200, 450 Jane Stanford Way Bldg 200, Stanford, CA 94305
Reception – Tresidder Oak Lounge, second floor of the Tresidder Student Union Building
Plenary – Bishop Auditorium

Parking Information

General information: https://transportation.stanford.edu/how-purchase-visitor-parking
“How to” guide for visitor zone parking : https://transportation.stanford.edu/how-guide-visitor-zone-parking
Link to the map with parking zone numbers. Lots 7282 and 7213 are the closest for Symposia sessions. https://drive.google.com/file/d/1VCyTJOvNkDE43PpLI-cGtR30hylolWuo/view

Submission Requirements 

Interested individuals should submit a paper or abstract by the deadline listed below, unless otherwise indicated by the symposium organizers on their supplemental website. Please submit your submissions directly to the individual symposium according to their directions. Do not mail submissions to AAAI. See the appropriate section in each symposium description for specific submission requirements. 

Submission Site 

Please be sure to select the appropriate symposium when submitting your work. Please see the individual symposia for submission site details. 

Important Dates 

• By Nov 30:  AAAI opens registration for Spring Symposium Series 
• December 22: (unless otherwise noted): Papers due to organizers 
• January 5: (unless otherwise noted): Organizers send notifications to authors 
• January 19: (recommended): Spring Symposium Series final papers due to organizers 
• February 12: Deadline for Registration Refund Requests

Onsite Registration Schedule 

Upon arrival please check in at the registration area for your badge. AAAI will release the exact location of registration closer to the event.

Registration hours will be: 

General Event Schedule 
Each Symposium schedule may vary 

Additional Information: 

Inquiries about symposium may be directed to the Symposium Co-chairs: 

Christopher Geib (SIFT, USA)
Ron Petrick (Heriot-Watt University, UK) 
SSS-24 Co-Chairs 
ssschairs@aaai.org 

General inquiries regarding the symposium series should be directed to AAAI at sss@aaai.org. 

Bi-directionality in Human-AI Collaborative Systems

The Symposium addresses the challenges in creating synergistic human and AI-based autonomous system-of-systems. Recent advances in  generative AI techniques such as Large Language Models have exacerbated the growing concerns associated with AI such as the risk, trust, and safety from the use of machines/AI in open situations. These concerns present major hurdles in the development of verified and validated engineered systems involving bi-directional pathways across the human-machine barrier; bi-directionality in this context means understanding the design and operational consequences of the human on the agent, and vice-versa. Current discussions on human-AI interactions are fragmented, focusing either on the impact of AI on human stakeholders (and relevant human factors considerations), or potential ways of involving humans in computational interventions (e.g., data annotation, behavior interpretation).  We believe the challenges associated with humans-AI collaborative systems cannot be adequately addressed if the underlying challenges associated with bi-directionality are not taken into consideration.

Topics:

We are interested in the concepts associated with bi-directionality including, but not limited to:

1. Explainability
2. Risk, trust, and safety
3. Joint awareness
4. Shared mental models
5. Systems design & engineering
6. Assurance
7. Test and evaluation

Contacts:

Please send questions to: Jie Yang (j.yang-3@tudelft.nl) related to human factors; William Lawless (w.lawless@icloud.com) related to autonomous teams; or both.

Format of Symposium:

Invited talks, panel and paper presentations, and speed talks.

Submission Requirements

Papers submitted either as a research paper of up to 8 pages or an extended abstract (1-2 pages). Authors should follow  formatting guidelines in the AAAI-24 Author Kit. 

Submission site: https://easychair.org/conferences/?conf=bhaics2024

Opportunities: 1. Revised papers for a book (Elsevier) post symposium; 2. We plan a follow-up conference with AHFE and another book (Taylor and Francis); 3. A call with Entropy is open (https://www.mdpi.com/journal/entropy/special_issues/Human_Machine_Teams).

Supplementary  URL

https://sites.google.com/view/bidirectionality2024

Clinical Foundation Models Symposium

Foundation models are rapidly emerging as powerful tools for solving various biomedical tasks, even with limited task-specific data, while specialist machine learning (ML) models can incorporate clinical domain expertise. Both approaches hold promise for improving patient outcomes and streamlining healthcare administration, but several questions remain: (1) What distinguishes them? (2) What challenges exist in their training and application? (3) How can they seamlessly fit into healthcare routines? (4) What’s the best way to regulate these technologies?

Topics

Relevant topics for the symposium include, but are not limited to: (1) Clinical foundation model data, pre-training, evaluation, and applications. (2) Innovative ML methods combining healthcare knowledge from physics, biology, and chemistry. (3) Challenges limiting the adoption of ML in healthcare, focusing on usability, trust, robustness, and fairness. (4) New clinical datasets and benchmarks. (5) Regulatory frameworks, ethical considerations, and policy implications of ML for healthcare. (6) Software using foundation models for healthcare challenges, with insights on development and ethics.

Format of Symposium

The symposium will include keynote talks, panel discussions, poster sessions, and several spotlight talks (presentations of accepted papers), technical demonstrations, and tutorials throughout the 2.5-day symposium.

Submission Requirements

We invite submissions for two non-archival tracks: Traditional and Non-traditional.

Traditional Track: We seek papers which explore novel approaches or offer fresh insights into the core questions targeted by this symposium. Full papers can be up to 4 pages long with unlimited pages for references and appendices. All submissions undergo a double-blind review, must be anonymized, and should not have been published or under review

elsewhere. Nevertheless, authors can later publish their work in other venues due to the symposium’s non-archival nature.

Non-traditional Track: We invite all sorts of non-traditional research contributions. This includes papers describing software tools, datasets, benchmarks, clinical abstracts, or previously published research findings that can stimulate insightful discussions among the symposium attendees. These submissions should be up to 2 pages long with unlimited pages for references and appendices. The review process is single-blind. Submissions can be previously published or under review elsewhere, and accepted papers may also be published in other archival venues later.

Submission Site Information

https://openreview.net/group?id=AAAI.org/2024/Spring_Symposium_Series/Clinical_FMs

Submission Format:

Authors should follow the formatting guidelines outlined in the AAAI-24 Author Kit.

Important Dates

All deadlines are 11:59pm UTC-12:00 (anywhere on Earth).

Submission deadline: Jan 5, 2024
Notification of acceptance: Feb 9, 2024
Final versions of papers due: Mar 8, 2024
Symposium: Mar 25, 2024 – Mar 27, 2024

Organizing Committee

• Mononito Goswami,  Co-Chair, Carnegie Mellon University, mgoswami@andrew.cmu.edu
  Xinyu (Rachel) Li,  Co-Chair, Carnegie Mellon University, xinyul2@andrew.cmu.edu
  Artur Dubrawski, Program Chair, Carnegie Mellon University,  awd@cs.cmu.edu
  Su-In Lee, University of Washington, suinlee@cs.washington.edu
  Frederic Sala, University of Wisconsin-Madison, fredsala@cs.wisc.edu
  Jimeng Sun, University of Illinois Urbana-Champaign, jimeng@illinois.edu
  Giles Clermont, University of Pittsburgh, cler@pitt.edu
  Tristan Naumann, Microsoft Research, tristan@microsoft.com

Symposium URL

Further details can be found on our symposium website: https://clinicalfoundationmodels.github.io/. Please direct questions to: clinicalfms@gmail.com and follow us on Twitter/X at @clinicalfms.

Empowering Machine Learning and Large Language Models with Domain and Commonsense Knowledge (AAAI-MAKE 2024)

AAAI-MAKE 2024 brings together a diverse community of researchers, practitioners, and industry professionals from the fields of machine learning, knowledge engineering, and large language models (LLMs) to explore the synergy between these domains. The symposium aims to address the critical challenges of trustworthiness, interpretability, and the integration of commonsense reasoning in AI systems. Through a series of keynotes, paper presentations and discussions of the 2.5-day symposium, participants will delve into novel solutions empowering machine learning and large language models with domain and commonsense knowledge.

Topics and Format

• Trustworthy and knowledge-based explainable AI
• Evaluation of Commonsense Knowledge and Reasoning in Large Language Models
• Commonsense Knowledge in Foundation and Large Language Models
• Interpretability and Safety of Large Language Models with Commonsense Knowledge
• Human-Centered AI, Dialogue Systems, and Conversational AI
• Hybrid and Neurosymbolic AI
• Combination of Knowledge Engineering and Machine Learning
• Integration of Commonsense Knowledge Sources with LLMs and Downstream Tasks

Since AAAI-MAKE 2024 is a dedicated symposium on empowering ML/LLMs with domain/commonsense knowledge, the contributions should address hybrid approaches. The symposium involves presentations of accepted papers, keynotes, (panel) discussions, and plenary sessions.

Submission

We solicit full papers (6 to 8 pages) and position/short/poster papers (2 to 4 pages) that can include recent or ongoing research, challenges with datasets, and surveys. All submissions must reflect the formatting instructions of the AAAI author kit and be submitted through EasyChair (https://easychair.org/conferences/?conf=sss24).

The invitation of contributors and presenters will be based on a rigorous single-blinded review of papers by the program committee. Accepted papers shall be published as part of the “Proceedings of the AAAI Symposium Series” by the AAAI Library.

Important Dates

• Submission: 7th of January 2024
• Notification: 31st of January 2024
• Camera-ready submission: tbd
• Registration: 29th February 2024
• Symposium: 25-27 of March 2024

Organizing Committee

• Andreas Martin (primary contact), FHNW, Olten, Switzerland
• Pedro A. Colon-Hernandez, Apple Inc., CA, USA
• Maaike de Boer, TNO, The Hague, Netherlands
• Aurona Gerber, University of the Western Cape, Cape Town, South Africa
• Knut Hinkelmann, FHNW, Olten, Switzerland
• Pascal Hitzler, Kansas State University, Manhattan, KS, USA
• Jane Yung-jen Hsu, National Taiwan University, Taipei, Taiwan
• Yen-Ling Kuo, University of Virginia, VA, USA
• Xiang Lorraine Li, University of Pittsburgh, PA, USA
• Thomas Schmid, Martin Luther University Halle-Wittenberg, Germany
• Paulo Shakarian, Arizona State University, Tempe, AZ, USA
• Reinhard Stolle, Argo AI GmbH, München, Germany
• Frank van Harmelen, VU University, Amsterdam, Netherlands

Symposium Website:

https://www.aaai-make.info

Federated Learning on the Edge

Traditional Artificial Intelligence (AI) models predominantly rely on centralized computing architectures, limiting their potential in scenarios where real-time decision-making on low-latency devices is required. AI on The Edge has emerged to overcome these limitations, allowing AI algorithms and models to be deployed directly on edge devices, such as sensors, IoT devices, and autonomous systems. This shift in computation distribution reduces latency, improves responsiveness, and aims to enhance privacy, security, and bandwidth consumption. The next iteration for Edge AI is to allow devices to learn together and collaborate under a unified system architecture. The Federated Learning (FL) computational paradigm can facilitate this transition. This symposium invites academia, industry, and government researchers to explore Federated Learning on The Edge and its unique challenges and opportunities. The symposium will invite submissions of extended abstracts (to be developed into four-page manuscripts). In addition, the symposium will host invited keynote and session speakers. Overall, this symposium will offer a unique opportunity for participants from various backgrounds and agencies to engage in lively discussions, network with peers, and foster collaborations to advance and guide research and development for Federated Learning on The Edge.

Topics

• FL systems, topologies & architectures for the edge
• FL algorithmic optimizations for the edge
• FL for resource-constrained & unreliable edge devices
• FL for low size, weight, and power edge devices
• FL for 4G, 5G, 6G-and-beyond edge networks
• FL at the tactical edge   
• FL for scalable, secure & private learning on the edge
• FL for lifelong learning on the edge   
• FL for catastrophic forgetting on the edge
• Practical FL for the edge
• Hardware optimizations for FL on the edge
• Hardware-software co-design for FL on the edge
• Efficient Collaborative inference on the edge
• Open problems and challenges for FL on the edge
• Visionary perspectives for FL on the edge

Format of Symposium

For the first two days of the symposium, we plan to start each day with one keynote speaker session (~45mins), followed by an invited talk session consisting of 4 presentations (~20mins/presentation), and an accepted papers presentation session (~15mins/paper). Right after the paper presentation session, we plan to have a discussion panel (~90mins) among the day’s speakers and conclude the day with a poster session and a networking event.

For the final day of the symposium, we will have a 2-hour presentation session for invited talks followed by a discussion panel among the invited speakers.

Submission Requirements

All keynote and invited speakers will provide a 1-page extended abstract to be included in the proceedings. For the Call for Papers (CFP), we will invite submissions of papers with a 4-page content length and unlimited pages for references and appendices. Every submitted paper will follow the AAAI 2024 format (https://aaai.org/authorkit24-2/) and will need to provide at least the “ML: Distributed Machine Learning & Federated Learning” keyword from the AAAI list. Other keywords can be found at (https://aaai.org/conference/aaai/aaai-23/keywords/). All authors can download the AAAI 2024 Word and LaTeX template from here as well: https://drive.google.com/file/d/186NfMBwL408frGzp6dSet_RdPyGkC2ea/view?usp=drive_link

Important Dates

All deadlines are 11:59pm UTC (anywhere on Earth)

• Invited talk confirmation deadline:12 January 2024
• Invited talk 1-page extended abstract submission deadline: 15 February 2024
• Regular paper submission deadline: 12 January 2024
• Regular paper notification of acceptance: 26 January 2024
• Regular paper camera ready submission deadline: 23 February 2024
• Registration deadline: 23 February 2024
• Symposium: 25 – 27 March 2024

Submission Site Information

Submission: https://easychair.org/conferences/?conf=sss24

Contact: fledge2024@gmail.com

Symposium Committee

• Dimitris Stripelis, FedML Inc., University of Southern California
• George Sklivanitis, Center for Connected Autonomy and AI, Florida Atlantic University
• Joseph M Carmack, BAE Systems Inc.
• Jennifer M Sierchio, BAE Systems Inc.
• Rajeev Sahay, Department of Electrical and Computer Engineering, UC San Diego

Symposium External URL

https://sites.google.com/view/fledge2024

Impact of GenAI on Social and Individual Well-being

Generative AI (GenAI) presents significant opportunities and challenges in the realms of individual and societal well-being. While its benefits in fields like healthcare, arts, and education are vast, it also necessitates careful consideration of ethics, privacy, fairness, and security. This symposium will explore two main themes:

1. Individual Impact of GenAI on Well-being: This focuses on the benefits and implications of GenAI on personal well-being, excluding societal factors. Discussions will revolve around its influence on work efficiency, personalized healthcare, education, entertainment, and the importance of privacy.

2. Social Impact of GenAI on Well-being: This delves into the broader societal implications, touching on topics such as employment changes due to AI, preventing increased social inequalities, the role of AI in healthcare quality, and the risks of misinformation. The goal is to understand both the potential pros and cons of GenAI in society, emphasizing the need for unbiased, fair, and socially responsible outcomes.

The symposium welcomes both technical and philosophical discussions, spanning ethical design, machine learning, robotics, and social media. Themes like responsible social media use, beneficial robotics, AI/VR’s role in combating loneliness, and health promotion will be central. The event aims to highlight advancements, challenges, and potential applications in GenAI, advocating for social responsibility in well-being and encouraging evaluations of digital experiences and insights into human health.

Topics:

We welcome the technical and philosophical discussions on “Impact of GenAI on Social and Individual Well-being”, in the design and implementation of ethics, machine learning software, robotics, and social media (but not limited). For example, interpretable forecasts, sound social media, helpful robotics, fighting loneliness with AI/VR, and promoting good health are the important scope of our discussions.

Format:

The symposium is organized by the invited talks, presentations, posters, and interactive demos.

Submissions:

Interested participants should submit either full papers (8 pages maximum) or extended abstracts (2 pages maximum). Extend abstracts should state your presentation types (long paper (6-8 pages), short paper (1-2 pages), demonstration, or poster presentation). All submissions should be uploaded to AAAI’s EasyChair site at https://easychair.org/conferences/?conf=sss24, and in addition, email your submissions to sss2024-genai[at]cas.lab.uec.ac.jp by January 12, 2024

Important Dates:

The submission deadline: January 12, 2024

Author Notification: January 26, 2024

Camera-ready Papers: February 15th, 2024 (It might be changed.)

Registration deadline: March 4th, 2024

Symposium: March 25th-27th, 2024

Publication of post proceeding: October 31st, 2024 (It might be changed.)

Organizing Committee:

Co-chairs: Takashi Kido (Teikyo University, Japan) Keiki Takadama (The University of Electro-Communications, Japan).

For More Information

Please see the symposium website: http://www.cas.lab.uec.ac.jp/wordpress/aaai_spring_2024/

Increasing Diversity in AI Education and Research

AI’s ubiquitous rise has revolutionized industries, societies, and economies, but a stark imbalance persists in representation, particularly among those affiliated with Minority-Serving Institutions (MSIs). Bridging this gap is critical for fostering inclusive AI development and deployment. The symposium aims to provide a platform for stakeholders to address barriers faced by historically excluded and marginalized students in AI education. We seek candid conversations on strategies to attract and retain diverse talents, expanding beyond administrative guidelines to redefine AI goals and priorities. We invite a broad range of submissions on the outlined topics and related areas. Join us in shaping the future of AI education in K-12 and higher ed by contributing your insights, research, and experiences. Be part of a transformative dialogue that goes beyond discussions to catalyze meaningful change.

Topics

• AI at Minority-Serving Institutions (MSIs): This symposium will spotlight the significant potential within Minority-Serving Institutions (MSIs) in cultivating AI talent. Presentations and discussions will highlight the successes and insights garnered from these institutions. Topics include current training initiatives at HBCUs, HSIs, TCUs, and AAPISIs, seeking to distill actionable strategies for wider adoption. Papers are encouraged that spotlight successful collaborations between MSIs, non-MSIs, industry, and government agencies.
• AI for good: The impact of reframing AI from making machines more intelligent than humans to a human-centered and social justice focus for advancing AI has the potential to increase the diversity of people drawn to AI education and research. Topics include how AI for good creates diverse pathways, as well as a broad range of careers for AI professionals in industry, policy, entrepreneurship, and beyond. Topics include how institutions committed to AI for good can attract and train students to thrive in the dynamic landscape of AI.
• Addressing Digital Inequality: In an era defined by technological advancement, ensuring accessibility is fundamental to achieving true inclusivity. This topic includes the critical issue of digital inequality and its profound impact on AI education. This topic addresses the multifaceted challenges that arise from uneven access to resources and technologies, particularly for students from underrepresented backgrounds.

Format

The symposium will consist of invited speakers, paper presentations, panel discussion, breakout discussions, and a poster session.

Submission

Interested participants should submit either full papers (8 pages maximum) or extended abstracts (2 pages maximum). Submissions should state your contribution type: position paper, research paper, methods paper, review paper, or experience paper.

Submissions are closed.

Organizing Committee

Jessica Coates (Spelman College), Nate Derbinsky (Northeastern), Bonnie Dorr (University of Florida), Judy Goldsmith (University of Kentucky), Naja Mack (Morgan State), Mary Lou Maher (UNC Charlotte), Jamie Payton (Temple and Invite AI), Jodi Reeves (National University and TILOS), Mehran Sahami (Stanford), Neelu Sinha (Fairleigh Dickinson University), Melo-Jean Yap (Johns Hopkins), Clement G. Yedjou (Florida A&M)

 For More Information 

https://sites.google.com/uncc.edu/aaai-diversity-in-ai-education/home

Symposium on Human-Like Learning

Recent machine-learning research has made incredible progress across a wide range of tasks. While many systems can achieve human-like performance, one area that is currently under explored is how to realize human-like learning capabilities within these systems. For example, machine learning typically employs batch training and requires more data and computation than people to achieve similar capabilities. The resulting models are effective, but difficult to update in the face of new data without costly retraining. In contrast, humans excel at rapidly assimilating new information on the fly from a limited number of examples. More research is needed to investigate human-like capabilities, such as efficient, incremental learning, and to explore the design of artificial systems that can also exhibit them.

General Topics

• Identification of key characteristics of human-like learning to target in AI/ML research, and what makes them challenging for current approaches;
• Ongoing and proposed research into how to create artificial systems that exhibit human-like learning;
• Approaches for evaluating such systems; and
• Exploration of the broader context and impacts of this research, such as how human-like learning systems might complement/benefit current machine learning systems and humanity.

Relevant Topics (Not Exhaustive)

• Cognitive architectures
• Interactive task learning
• Extended/continual learning
• Probabilistic programming
• Concept formation
• Analogical and case-based learning
• Logic-based learning
• Simulated students
• Human-like neural network learning

Format 

The symposium will consist of a small number of invited speakers, followed by approximately 20 technical presentations and a poster session. Each talk will be allotted 30-minutes (20 for the talk and 10 for discussion). There will also be coffee breaks and time for broader reflections and discussions.

Submission 

Authors will submit abstracts, which the organizing committee will use to decide on session topics and presentations. Speaker abstracts will also be shared with attendees. Authors of submissions that are not presented as talks will be invited to participate in a poster session during the first day. In choosing presenters, the committee will give preference to submissions that are more closely aligned to the overarching theme, while also trying to give coverage to different aspects of the theme.

Submissions are closed.

Organizing Committee 

• Christopher J. MacLellan, Georgia Institute of Technology, cmaclell@gatech.edu
• Ute Schmid, University of Bamberg, ute.schmid@uni-bamberg.de
• Douglas Fisher, Vanderbilt University, douglas.h.fisher@vanderbilt.edu
• Randolph M. Jones, Soar Technology, LLC, rjones@soartech.com

 For More Information  

More information on the symposium can be found: https://humanlikelearning.com/aaai24-ss/

User-Aligned Assessment of Adaptive AI Systems

This symposium addresses research gaps in assessing the compliance of adaptive AI systems (systems capable of planning/learning) in the presence of post-deployment changes in requirements, in user-specific objectives, in deployment environments, and in the AI systems themselves. Although there are growing calls for better safety assessment and regulation of AI systems by users, the government, and even the industry itself, broad questions remain on approaches for conceptualizing, expressing, managing, and enforcing such regulations for adaptive AI systems.

These research problems go beyond the classical notions of verification and validation, where operational requirements and system specifications are available a priori. In contrast, adaptive AI systems such as household robots are expected to be designed to adapt to day-to-day changes in the requirements (which can be user-provided), environments, and as a result of system updates and learning. The symposium will feature invited talks by researchers from AI and formal methods, as well as talks on contributed papers.

Topics of interest include:

• Learning predictive models of agent behavior.
• Post-deployment assessment of AI system capabilities.
• Algorithmic paradigms for assessment of safety and/or compliance of AI systems with evolving regulations.
• Self-assessment and monitoring.
• Differential assessment of AI systems following system updates or learning.
• Assessment of black-box AI systems.
• Types of assessment frameworks and ecosystems.
• Specification languages and representations for specifying requirements on AI systems.
• Assessment of LLM-based agents.
• Specification and assessment of compliance w.r.t. ethics/ethical properties.
• Regulation, management, and enforcement of AI assessment paradigms.

Invited Speakers (Tentative)

Kamalika Chaudhuri, University of California San Diego, and Meta AI

Chuchu Fan, Massachusetts Institute of Technology

Sriraam Natarajan, University of Texas Dallas

Stuart Russell, University of California Berkeley

Sriram Sankaranarayanan, University of Colorado Boulder

Sanjit A. Seshia, University of California Berkeley

Symposium Format

The symposium will feature invited talks, talks on contributed papers, and discussions. The symposium will be in-person and is scheduled for 2.5 days.

Submission Instructions

Submissions can describe either work in progress or mature work. Two types of papers can be submitted: full technical papers (up to 8 pages + references) and short papers (up to 4 pages + references).

Submissions should use the style files available here. Papers can be submitted via EasyChair at https://easychair.org/my/conference?conf=aia2024. Additional details are available on the symposium website.

Organizing Committee

Pulkit Verma, Arizona State University.
Rohan Chitnis, Meta AI.
Georgios Fainekos, Toyota Motor North America R&D.

Hazem Torfah, Chalmers University of Technology.

Siddharth Srivastava, Arizona State University.

Website

https://aair-lab.github.io/aia2024/index.html#program