First ACM SIGEnergy Workshop on Fair, Accountable, Transparent, and Ethical (FATE) AI for Smart Environments and Energy Systems

A SenSys/BuildSys 2021 Workshop

About FATEsys 2021

With the advent of IoT, high performance computing, and ubiquitous and smart sensing, the SenSys/BuildSys community is noticing a big shift in the adoption of data-driven black-box modeling (also referred to as AI) to solve problems in the space of smart environments and energy systems. As a result, these AI-enabled systems for smart buildings, smart cities, smart grids, electric transportation, among others are attaining better accuracy and efficiency numbers every year. However, these data-driven black-box solutions are rarely held accountable for the impact of their actions on the human in the loop which significantly impacts their real-world adoption. To truly conceptualize the idea of smart systems for everyone, it is critical to study AI-enabled smart environments and energy systems to enforce energy equity and ensure not just clean and resilient energy systems, but also make them affordable and accessible for all. The first ACM SIGEnergy workshop on Fair, Accountable, Transparent, and Ethical AI for Smart Environments and Energy Systems intends to bring together researchers from diverse backgrounds and discuss key issues, challenges, breakthroughs, and socio-economic impact in developing fair, accountable, transparent and ethical AI techniques for smart environments and energy systems.

Call for Papers

FATEsys 2021 Call for Papers

The aim of this workshop is to create a platform for the SenSys/BuildSys community to discuss developing AI-enabled smart environments and energy systems that are not just accurate but also take responsibility for their actions. We invite submissions including, but not limited to:

  • - Studies exploring type of biases in energy-related data and their implications
  • - Challenges in collecting representative data for fair training of the AI models
  • - Studies on eXplainable AI (XAI) for smart environments and energy systems
  • - Interpretable and explainable ML/AI models
  • - Physics-informed ML for model interpretation
  • - Innovative ML/AI models and their key limitations pertaining to FATE
  • - Socio-economic impact analysis for energy equity
  • - Fair metrics for the evaluation of ML/AI methods
  • - Exploring visual analytics for bias evaluation in data and models

Submission Guidelines

Submitted papers must be unpublished and must not be currently under review for any other publication. Paper submissions must be at most 4 single-spaced US Letter (8.5"x11") pages, including figures, tables, and appendices (excluding references). All submissions must use the LaTeX (preferred) or Word styles found here. Authors must make a good faith effort to anonymize their submissions by (1) using the "anonymous" option for the class and (2) using "anonsuppress" section where appropriate. Papers that do not meet the size, formatting, and anonymization requirements will not be reviewed. Please note that ACM uses 9-pt fonts in all conference proceedings, and the style (both LaTeX and Word) implicitly define the font size to be 9-pt.

Submission link

All submissions must be in Adobe Portable Document Format (PDF) format through the HotCRP: https://fatesys21.hotcrp.com/

Important Dates

  • - Submission Deadline: September 3 October 7, 2021 (AOE)
  • - Notifications: September 24 October 12, 2021 (AOE)
  • - Camera-ready: October 1 October 16, 2021 (AOE)
  • - Workshop: TBD

Organization

General Co-Chairs

Milan Jain

Milan Jain Pacific Northwest National Laboratory, Richland, WA, USA

Pandarasamy Arjunan

Pandarasamy Arjunan Berkeley Education Alliance for Research in Singapore (BEARS)

Technical Program Committee Chair

Xueqing Sun

Xueqing Sun Pacific Northwest National Laboratory, Richland, WA, USA

Keynote & Panel Discussion

Sayak Mukherjee

Sayak Mukherjee Pacific Northwest National Laboratory, Richland, WA, USA

Publicity & Web Chair

Aowabin Rahman

Aowabin Rahman Pacific Northwest National Laboratory, Richland, WA, USA

Technical Program Committee

Akansha Bansal

Akansha Bansal University of Massachusetts Amherst, MA, USA

Mu Mu

Mu Mu University of Northampton, England

Nicole Sintov

Nicole Sintov Ohio State University, USA

Robert Yus

Robert Yus University of Maryland, Baltimore County, USA

Salma Elmalaki

Salma Elmalaki University of California, Irvine, USA

Sarah Newman

Sarah Newman Pacific Northwest National Laboratory, Richland, WA, USA

Sayak Mukherjee

Sayak Mukherjee Pacific Northwest National Laboratory, Richland, WA, USA

Vikas

Vikas Chandan Pacific Northwest National Laboratory, Richland, WA, USA

Xiaonan Wang

Xiaonan Wang Tsinghua University, China

Venue

The first ACM SIGEnergy Workshop on Fair, Accountable, Transparent, and Ethical (FATE) AI for Smart Energy Systems and Environments is part of (co-located with) SenSys/BuildSys 2021.

For venue details, visa information, etcetera please visit the BuildSys venue page.

Sponsor