This governmental report discusses the ethical and regulatory considerations of using artificial intelligence in mental healthcare in the UK.
Bias in AI tools (algorithmic bias) can stem from various places, including tools being trained on biased datasets and outputting discriminatory outcomes or developers making biased decisions in the design or training of such tools. For example, mental health Electronic health record (EHR) data is susceptible to cohort and label bias. This can occur because culture-bound presentations of mental disorders, combined with a lack of transcultural literacy among clinicians, often lead to both over- and under-diagnosis. People can also exhibit bias when using AI tools, such as over-relying on, or mistrusting AI outputs. All these biases can be conscious or unconscious.
Learn more about the report here: https://doi.org/10.58248/PN738
Reference
Gardiner, Hannah and Natasha Mutebi (2025), AI and Mental Healthcare – ethical and regulatory considerations, UK Parliament – POST, POSTnote 738, 31 January 2025