Beyond privacy and security: Exploring ethical issues of smart metering and non-intrusive load monitoring

Abstract

Artificial intelligence is believed to facilitate cost-effective and clean energy by optimizing consumption, reducing emissions, and enhancing grid reliability. Approaches such as non-intrusive load monitoring (NILM) offer energy efficiency insights but raise ethical concerns. In this paper, we identify most prominent ethical and societal issues by surveying relevant literature on smart metering and NILM. We combine these findings with empirical insights gained from qualitative workshops conducted with an electricity supplier piloting the use of AI for power load disaggregation. Utilizing the requirements for trustworthy AI, we show that while issues related to privacy and security are the most widely discussed, there are many other equally important ethical and societal issues that need to be addressed, such as algorithmic bias, uneven access to infrastructure, or loss of human control and autonomy. In total, we identify 19 such overarching themes and explore how they align with practitioners' perspectives and how they embody the seven core requirements for trustworthy AI systems defined by the Ethics Guidelines for Trustworthy AI.