Ethics for Data and AI

As more personal and sensitive data is collected for uses in decarbonisation, there is an increased risk to privacy and security. Data and AI methods also contain bias which can be perpetuated or worsened, causing harms to vulnerable groups and individuals, and may increase risks of discrimination. Below are some useful resources in this area.

Data Ethics and Bias in Smart Local Energy Sysmtes

Energy Systems Catapult wrote a short report, on Data Ethics and Bias: Practical steps to avoid discrimination in future Smart Local Energy Systems. The report focuses on three main principles:

• User Group Principles: Who are the users who may be effected by data bias.
• User Effect Principles: What effect data bias can have on users.
• Data Mitigation Principles: What approaches can reduce the harms from data bias.