FATEsys 2022: the second ACM SIGEnergy workshop on fair, accountable, transparent, and ethical (FATE) AI for smart environments and energy systems
Pages 307 - 309
Abstract
The advent of IoT, ubiquitous and smart sensing and high-performance computing has resulted in a big shift in the adoption of data-driven black-box modelling (also referred to as machine/deep learning) to make our environments smarter and energy systems efficient. Several studies have shown that these AI-enabled solutions for smart buildings, smart cities, smart grids, and electric transportation, among others, are much more accurate and efficient. 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 these AI-enabled smart environments and energy systems for not just efficiency, but also for affordability and accessibility 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 socioeconomic impact in developing fair, accountable, transparent and ethical AI techniques for smart environments and energy systems.
References
[1]
David Danks and Alex John London. 2017. Algorithmic Bias in Autonomous Systems. In IJCAI, Vol. 17. 4691--4697.
[2]
R Gopinath, Mukesh Kumar, and Kota Srinivas. 2020. Feature Mapping based Deep Neural Networks for Non-intrusive Load Monitoring of Similar Appliances in Buildings. In Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. 262--265.
[3]
Anna Lauren Hoffmann. 2019. Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse. Information, Communication & Society 22, 7 (2019), 900--915.
[4]
Milan Jain, Khushboo Gupta, Arun Sathanur, Vikas Chandan, and Mahantesh M Halappanavar. 2021. Transfer-Learnt Models for Predicting Electricity Consumption in Buildings with Limited and Sparse Field Data. In 2021 American Control Conference (ACC). IEEE, 2887--2894.
[5]
Milan Jain, Soumya Kundu, Arnab Bhattacharya, Sen Huang, Vikas Chandan, Nikitha Radhakrishnan, Veronica Adetola, and Draguna Vrabie. 2021. Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads. arXiv preprint arXiv:2103.11485 (2021).
[6]
Lee V White and Nicole D Sintov. 2020. Health and financial impacts of demand-side response measures differ across sociodemographic groups. Nature Energy 5, 1 (2020), 50--60.
Index Terms
- FATEsys 2022: the second ACM SIGEnergy workshop on fair, accountable, transparent, and ethical (FATE) AI for smart environments and energy systems
Recommendations
FATEsys 2021: the first ACM SIGEnergy workshop on fair, accountable, transparent, and ethical (FATE) AI for smart environments and energy systems
BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and TransportationThe advent of IoT, ubiquitous and smart sensing, and high performance computing has resulted in a big shift in the adoption of data-driven black-box modeling (also referred to as machine/deep learning) to make our environments smarter and energy systems ...
FATREC Workshop on Responsible Recommendation
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender SystemsThe first Workshop on Responsible Recommendation (FATREC) was held in conjunction with the 11th ACM Conference on Recommender Systems in August, 2017 in Como, Italy. This full-day workshop brought together researchers and practitioners to discuss ...
Towards Responsible Spatial Data Science and Geo-AI
IC3-2023: Proceedings of the 2023 Fifteenth International Conference on Contemporary ComputingResponsible Geo-AI encourages the design and development of spatial methods, processes, algorithms, and systems to discover spatial patterns (e.g., hotspots, colocations) that reduce adverse impacts on the communities that use them. We propose a vision ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2022 ACM.
© 2022 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 December 2022
Check for updates
Author Tags
Qualifiers
- Short-paper
Conference
BuildSys '22
Sponsor:
BuildSys '22: The 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
November 9 - 10, 2022
Massachusetts, Boston
Acceptance Rates
Overall Acceptance Rate 148 of 500 submissions, 30%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 51Total Downloads
- Downloads (Last 12 months)26
- Downloads (Last 6 weeks)2
Reflects downloads up to 21 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in