Addressing machine learning bias to foster energy justice

A Survey on Bias and Fairness in Machine Learning

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812 · see citing works

Energy justice: A conceptual review

Jenkins, K. E. H., McCauley, D., Heffron, R. J., Stephan, H. R., & Rehner, R. (2015) · Energy Research & Social Science · doi:10.1016/j.erss.2015.10.004 · Cited by 2,261 · see citing works

Ethics Guidelines for Trustworthy AI

Cannarsa, M. (2021) · The Cambridge Handbook of Lawyering in the Digital Age · doi:10.1017/9781108936040.022 · Cited by 2,095 · see citing works

Social Science Research: Principles, Methods and Practices

Bhattacherjee, A., Toleman, M., Rowling, S., Frederiks, A., & Andersen, N. (2019) · doi:10.26192/q7w89 · Cited by 2,030 · see citing works

The Modifiable Areal Unit Problem in Multivariate Statistical Analysis

Fotheringham, A. S., & Wong, D. (1991) · Environment and Planning A Economy and Space · doi:10.1068/a231025 · Cited by 1,561 · see citing works

Leakage and the reproducibility crisis in machine-learning-based science

Kapoor, S., & Narayanan, A. (2023) · Patterns · doi:10.1016/j.patter.2023.100804 · Cited by 1,387 · see citing works

New frontiers and conceptual frameworks for energy justice

Sovacool, B. K., Burke, M. J., Baker, L., Kotikalapudi, C. K., & Wlokas, H. L. (2017) · Energy Policy · doi:10.1016/j.enpol.2017.03.005 · Cited by 917 · see citing works

Training and assessing classification rules with imbalanced data

Menardi, G., & Torelli, N. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-012-0295-5 · Cited by 769 · see citing works

Establishment of Best Practices for Evidence for Prediction: A Review

Poldrack, R. A., Huckins, G., & Varoquaux, G. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.3671 · Cited by 738 · see citing works

Integrating explanation and prediction in computational social science

Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M. J., Vazire, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03659-0 · Cited by 371 · see citing works

Sociodemographic disparities in energy insecurity among low-income households before and during the COVID-19 pandemic

Memmott, T., Carley, S., Graff, M., & Konisky, D. M. (2021) · Nature Energy · doi:10.1038/s41560-020-00763-9 · Cited by 238 · see citing works

Modeling energy consumption in residential buildings: A bottom-up analysis based on occupant behavior pattern clustering and stochastic simulation

Diao, L., Sun, Y., Chen, Z., & Chen, J. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.04.072 · Cited by 214 · see citing works

Fairlearn: A toolkit for assessing and improving fairness in AI

Bird, S., Dudík, M., Edgar, R. J., Horn, B., Lutz, R., Milan, V., Sameki, M., Wallach, H., & Walker, K. (2020) · Cited by 204 · see citing works

Big-data for building energy performance: Lessons from assembling a very large national database of building energy use

Mathew, P., Dunn, L. N., Sohn, M. D., Mercado, A., Custudio, C., & Walter, T. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.11.042 · Cited by 199 · see citing works

Modifiable Temporal Unit Problem (MTUP) and Its Effect on Space-Time Cluster Detection

Cheng, T., & Adepeju, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100465 · Cited by 167 · see citing works

Diagnosis of wind turbine faults with transfer learning algorithms

Chen, W., Qiu, Y., Feng, Y., Li, Y., & Kusiak, A. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.10.121 · Cited by 157 · see citing works

Inequitable access to EV charging infrastructure

Khan, H. A. U., Price, S. L. R., Avraam, C., & Dvorkin, Y. (2022) · The Electricity Journal · doi:10.1016/j.tej.2022.107096 · Cited by 124 · see citing works

Energy Justice and Smart Grid Systems: Evidence from the Netherlands and the United Kingdom

Milchram, C., Hillerbrand, R., van de Kaa, G., Doorn, N., & Künneke, R. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.08.053 · Cited by 98 · see citing works

Typical occupancy profiles and behaviors in residential buildings in the United States

Mitra, D., Steinmetz, N., Chu, Y., & Cetin, K. S. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.109713 · Cited by 93 · see citing works

An agent-based modeling approach for public charging demand estimation and charging station location optimization at urban scale

Yi, Z., Chen, B., Liu, X. C., Wei, R., Chen, J., & Chen, Z. (2023) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2023.101949 · Cited by 80 · see citing works

A high spatial resolution residential energy model based on American Time Use Survey data and the bootstrap sampling method

Chiou, Y.-S., Carley, K. M., Davidson, C. I., & Johnson, M. P. (2011) · Energy and Buildings · doi:10.1016/j.enbuild.2011.09.020 · Cited by 60 · see citing works

A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity SectorPreprint

Ruan, G., Wu, D., Zheng, X., Sivaranjani, S., Zhong, H., Kang, C., Dahleh, M. A., & Xie, L. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3631498 · Cited by 24 · see citing works

The effects of population aggregation in geospatial electrification planning

Khavari, B., Sahlberg, A., Usher, W., Korkovelos, A., & Nerini, F. F. (2021) · Energy Strategy Reviews · doi:10.1016/j.esr.2021.100752 · Cited by 19 · see citing works

Evaluating Potential Sources of Aggregation Bias with a Structural Optimization Model of the U.S. Forest Sector

Wade, C. M., Baker, J. S., Latta, G. S., & Ohrel, S. B. (2019) · Journal of Forest Economics · doi:10.1561/112.00000503 · Cited by 16 · see citing works

Observational Scale and Modeled Potential Residential Loss from a Storm Surge

Kar, B., & Hodgson, M. E. (2012) · GIScience & Remote Sensing · doi:10.2747/1548-1603.49.2.202 · Cited by 10 · see citing works

Connected Communities: A Vision for the Future of Electric Utilities

Nevels, P. (2020) · IEEE Engineering Management Review · doi:10.1109/emr.2020.2970010 · Cited by 8 · see citing works

Machine learning applications in energy systems: current trends, challenges, and research directions

Aslam, S., Aung, P. P., Rafsanjani, A. S., & Majeed, A. P. P. A. (2025) · Energy Informatics · doi:10.1186/s42162-025-00524-6 · Cited by 112 · see citing works

Novel Artificial Intelligence Applications in Energy: A Systematic Review

Zhang, T., & Štrbac, G. (2025) · Energies · doi:10.3390/en18143747 · Cited by 30 · see citing works

Towards a just AI-assisted energy transitions for vulnerable communities

Delina, L. L., & Tung, Y. S. M. (2024) · Energy Research & Social Science · doi:10.1016/j.erss.2024.103752 · Cited by 25 · see citing works

Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector

Park, C. (2025) · Sustainability · doi:10.3390/su17135764 · Cited by 17 · see citing works

Reinforcement Learning in Energy Finance: A Comprehensive Review

Giannelos, S. (2025) · Energies · doi:10.3390/en18112712 · Cited by 14 · see citing works

AI Bias in Power Systems Domain—Exemplary Cases and Approaches

Eze, C., Ezema, A., Roth, L., Pan, Z., Ponci, F., & Monti, A. (2025) · Energies · doi:10.3390/en18184819 · Cited by 9 · see citing works

Examining the impact of bias mitigation algorithms on the sustainability of ML-enabled systems: A benchmark study

Martino, V. D., Voria, G., Troiano, C., Catolino, G., & Palomba, F. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112458 · Cited by 6 · see citing works

Achieving energy justice through corporate carbon disclosure: The effects of voluntary and third-party verification mechanisms

Du, J., Zhen, Z., Wei, X., & Li, R. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115677 · Cited by 4 · see citing works

BERT based sentiment analysis of consumer hesitancy toward solar energy adoption

Jabbar, A., Yuan, J., Al-Shamasneh, A. R., & Rekik, S. (2026) · Scientific Reports · doi:10.1038/s41598-026-38604-6 · Cited by 4 · see citing works

Artificial intelligence and the energy transition: Towards ethical and equitable future power systems

Zhu, R. R., & Ma, J. (2026) · Energy Research & Social Science · doi:10.1016/j.erss.2026.104564 · Cited by 3 · see citing works

Enhancing Nonparametric Tests: Insights for Computational Intelligence and Data Mining

Aqmal, S. Y., & Fermle Erdely, S. (2024) · Researcher Academy Innovation Data Analysis · doi:10.69725/raida.v1i3.168 · Cited by 3 · see citing works

A Role for Artificial Intelligence (AI) in Qualitative Research? An Exploratory Analysis Examining New York City Residents’ Perceptions on Climate Change

Sprague, N. L., Meltzer, G., Dandeneau, M., Santos, D. D. L., O’Neil, D., Kim, A. K., Parisi, A., Araujo, S., Ekenga, C. C., Siegel, E., et al. (2025) · Sustainability · doi:10.3390/su172310459 · Cited by 2 · see citing works

A Comparative Study of Machine Learning Algorithms for Predicting Project Outcomes

Pacot, M. P. B., Daguil, R. C., & Pitogo, V. A. (2024) · Proceeding of the Electrical Engineering Computer Science and Informatics · doi:10.1109/eecsi63442.2024.10776243 · Cited by 2 · see citing works

Large Language Models to Support Socially Responsible Solar Energy Siting in Utah

Moshina, U., Chick, I. P., Carlisle, J. E., & Ames, D. P. (2025) · Solar · doi:10.3390/solar5040052

Recalibrating Responsible AI: Non-Western Ethics, Sustainable Machine Learning, and Human Oversight

Wasiu, L. (2025) · Journal of information engineering and applications · doi:10.7176/jiea/15-2-09

Trustworthy AI in Smart Grid Systems

Ren, H., Imran, A. S., & Olsen, R. L. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1013588

Redesigning Risk Assessments in the Age of AI: Emergence of the Opacity Risk Dimension

Issa, H. (2026) · Risk Analysis · doi:10.1111/risa.70278

Artificial Intelligence as a Compounding Enabler in the Implementation of Green Energy Technology Policies in South Africa

Vilakazi, S., & Sevhake, M. (2026) · Green Technology & Innovation · doi:10.65582/gti.2026.016

Reinforcement Learning for Dynamic Spectrum Allocation in 6G Networks

Almashhadani, Y. S. (2026) · Cihan University-Erbil Scientific Journal · doi:10.24086/cuesj.v10n2y2026.pp11-28

Quantifying AI impact in energy transitions: The Energy Justice Impact Assessment (EJIA) frameworkPreprint

Bringmann, E., Kutzner, F., & Kacperski, C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7352634

System-Level AI Governance in Nursing: A Practical Framework for Safe and Scalable Adoption

Wang, J., Yang, Y., Marshall, D., Shucoski, P., Gross, T. L., Frederick, R. K., & Pappas, S. H. (2026) · Nursing Administration Quarterly · doi:10.1097/naq.0000000000000772

Geo‐Insights

Bhattacharjee, S., Chaberek-Karwacka, G., Nandi, E., & Majumdar, S. (2026) · Wiley eBooks · doi:10.1002/9781394284795.ch7

Ethical, societal, and sustainability dimensions of resource-efficient artificial intelligence in energy systems: a systems-level review

Sulaima, M. F., Arvindhan, M., Krejcar, O., & Namazi, H. (2026) · Energy Nexus · doi:10.1016/j.nexus.2026.100867

Ethical and governance challenges of large language models in power and energy systems: a review and framework for responsible AI deployment

Sulaima, M. F., Arora, S., Krejcar, O., Herout, L., & Namazi, H. (2026) · Energy Nexus · doi:10.1016/j.nexus.2026.100869

In search of hidden complexity: Artificial intelligence analyses of energy poverty

Kotova, M., Ruiz-Rivas, U., & Espino, A. L. (2026) · Energy Research & Social Science · doi:10.1016/j.erss.2026.105033

Machine learning for energy storage I-thermal and electrical and hydrogen energy storages

Dan, Z. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34029-1.00013-x

Faires Machine Learning

Hilbert, S., Kraus, E. B., & Lindl, A. (2025) · Springer eBooks · doi:10.1007/978-3-658-43649-0_7

Machine Learning and Bias

Kayyali, M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2685-6.ch006 · Cited by 2 · see citing works

Artificial Intelligence in Democratizing Access to Renewable Energy: Machine Learning Models for Community Engagement and Equity

Annapareddy, V. N. (2025) · Journal of Artificial Intelligence & Cloud Computing · doi:10.47363/jaicc/icaicc/2025(4)16

Race and Intersecting Inequalities in Machine Learning

Alegria, S. N. (2023) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197653609.013.23 · Cited by 2 · see citing works

Ethical AI and Bias Mitigation in Machine Learning Systems

Chaube, N. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.15799047