Explainable AI for Non-Experts: Energy Tariff Forecasting

Hongnan Ma; Kevin McAreavey; Ryan McConville; Weiru Liu

doi:10.1109/ICAC55051.2022.9911105

Explainable AI for Non-Experts: Energy Tariff Forecasting

Hongnan Ma, Kevin McAreavey, Ryan McConville, Weiru Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

7 Citations (Scopus)

250 Downloads (Pure)

Abstract

Non-expert users are increasingly affected by the decisions of systems that rely on machine learning (ML), yet it is often difficult for these users to understand the predictions of ML models. In this paper, we propose a web-based platform to evaluate explainable AI (XAI) for non-experts in the context of time series forecasting, focusing on energy price predictions as an exemplary use case. The XAI methods we consider include local feature importance and counterfactual explanations. The platform relies on gamification to encourage user engagement. Our research objective is to evaluate the effectiveness of these different approaches from the perspective of non-expert under- standing of machine learning models.

Original language	English
Title of host publication	2022 27th International Conference on Automation and Computing
Subtitle of host publication	Smart Systems and Manufacturing, ICAC 2022
Editors	Chenguang Yang, Yuchun Xu
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	6
ISBN (Electronic)	978-1-6654-9807-4
ISBN (Print)	978-1-6654-9808-1
DOIs	https://doi.org/10.1109/ICAC55051.2022.9911105
Publication status	Published - 10 Oct 2022

Publication series

Name	2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022

Bibliographical note

Funding Information:
This work is partially funded by the EPSRC CHAI project (EP/T026820/1).

Publisher Copyright:
© 2022 IEEE.

Access to Document

10.1109/ICAC55051.2022.9911105

Full-text PDF (accepted manuscript)
This is the accepted author manuscript (AAM). The final published version (version of record) is available online via IEEE at https://ieeexplore.ieee.org/document/9911105. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 410 KB

Handle.net

Persistent link

CHAI: Cyber Hygiene in AI enabled domestic life
Liu, W. (Principal Investigator)
1/12/20 → 28/02/24
Project: Research

Cite this

Ma, H., McAreavey, K., McConville, R., & Liu, W. (2022). Explainable AI for Non-Experts: Energy Tariff Forecasting. In C. Yang, & Y. Xu (Eds.), 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022 (2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICAC55051.2022.9911105

Ma, Hongnan ; McAreavey, Kevin ; McConville, Ryan et al. / Explainable AI for Non-Experts : Energy Tariff Forecasting. 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022. editor / Chenguang Yang ; Yuchun Xu. Institute of Electrical and Electronics Engineers (IEEE), 2022. (2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022).

@inproceedings{9e0c479c9ed24ae59d319827772ed01b,

title = "Explainable AI for Non-Experts: Energy Tariff Forecasting",

abstract = "Non-expert users are increasingly affected by the decisions of systems that rely on machine learning (ML), yet it is often difficult for these users to understand the predictions of ML models. In this paper, we propose a web-based platform to evaluate explainable AI (XAI) for non-experts in the context of time series forecasting, focusing on energy price predictions as an exemplary use case. The XAI methods we consider include local feature importance and counterfactual explanations. The platform relies on gamification to encourage user engagement. Our research objective is to evaluate the effectiveness of these different approaches from the perspective of non-expert under- standing of machine learning models.",

author = "Hongnan Ma and Kevin McAreavey and Ryan McConville and Weiru Liu",

note = "Funding Information: This work is partially funded by the EPSRC CHAI project (EP/T026820/1). Publisher Copyright: {\textcopyright} 2022 IEEE.",

year = "2022",

month = oct,

day = "10",

doi = "10.1109/ICAC55051.2022.9911105",

language = "English",

isbn = "978-1-6654-9808-1",

series = "2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

editor = "Chenguang Yang and Yuchun Xu",

booktitle = "2022 27th International Conference on Automation and Computing",

address = "United States",

}

Ma, H, McAreavey, K, McConville, R & Liu, W 2022, Explainable AI for Non-Experts: Energy Tariff Forecasting. in C Yang & Y Xu (eds), 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022. 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022, Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICAC55051.2022.9911105

Explainable AI for Non-Experts: Energy Tariff Forecasting. / Ma, Hongnan; McAreavey, Kevin; McConville, Ryan et al.
2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022. ed. / Chenguang Yang; Yuchun Xu. Institute of Electrical and Electronics Engineers (IEEE), 2022. (2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Explainable AI for Non-Experts

T2 - Energy Tariff Forecasting

AU - Ma, Hongnan

AU - McAreavey, Kevin

AU - McConville, Ryan

AU - Liu, Weiru

PY - 2022/10/10

Y1 - 2022/10/10

N2 - Non-expert users are increasingly affected by the decisions of systems that rely on machine learning (ML), yet it is often difficult for these users to understand the predictions of ML models. In this paper, we propose a web-based platform to evaluate explainable AI (XAI) for non-experts in the context of time series forecasting, focusing on energy price predictions as an exemplary use case. The XAI methods we consider include local feature importance and counterfactual explanations. The platform relies on gamification to encourage user engagement. Our research objective is to evaluate the effectiveness of these different approaches from the perspective of non-expert under- standing of machine learning models.

AB - Non-expert users are increasingly affected by the decisions of systems that rely on machine learning (ML), yet it is often difficult for these users to understand the predictions of ML models. In this paper, we propose a web-based platform to evaluate explainable AI (XAI) for non-experts in the context of time series forecasting, focusing on energy price predictions as an exemplary use case. The XAI methods we consider include local feature importance and counterfactual explanations. The platform relies on gamification to encourage user engagement. Our research objective is to evaluate the effectiveness of these different approaches from the perspective of non-expert under- standing of machine learning models.

U2 - 10.1109/ICAC55051.2022.9911105

DO - 10.1109/ICAC55051.2022.9911105

M3 - Conference Contribution (Conference Proceeding)

SN - 978-1-6654-9808-1

T3 - 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022

BT - 2022 27th International Conference on Automation and Computing

A2 - Yang, Chenguang

A2 - Xu, Yuchun

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Ma H, McAreavey K, McConville R , Liu W. Explainable AI for Non-Experts: Energy Tariff Forecasting. In Yang C, Xu Y, editors, 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022. Institute of Electrical and Electronics Engineers (IEEE). 2022. (2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022). doi: 10.1109/ICAC55051.2022.9911105

Explainable AI for Non-Experts: Energy Tariff Forecasting

Abstract

Publication series

Bibliographical note

Access to Document

Handle.net

Fingerprint

Projects

CHAI: Cyber Hygiene in AI enabled domestic life

Cite this