AI used by English councils downplays women’s health issues – study

Artificial intelligence used by more than half of England’s councils is downplaying women’s physical and mental health issues, raising gender bias concerns in care decisions.

Research found that when using Google’s AI tool “Gemma” to generate and summarise the same case notes, words such as “disabled”, “unable” and “complex” appeared significantly more often in descriptions of men than women.

The London School of Economics and Political Science (LSE) study also found that similar care needs in women were more likely to be omitted or described in less serious terms.

Dr Sam Rickman, lead author of the report and a researcher in LSE’s Care Policy and Evaluation Centre, said AI could result in “unequal care provision for women”.

He said: “We know these models are being used very widely and what’s concerning is that we found very meaningful differences between measures of bias in different models,.

“Google’s model, in particular, downplays women’s physical and mental health needs in comparison to men’s.

“And because the amount of care you get is determined on the basis of perceived need, this could result in women receiving less care if biased models are used in practice.

“But we don’t actually know which models are being used at the moment.”

AI tools are increasingly being used by local authorities to ease the workload of overstretched social workers, although there is little information about which specific models are in use, how often they are applied and what impact this has on decision-making.

The LSE research used real case notes from 617 adult social care users, which were inputted into different large language models (LLMs) multiple times, with only the gender swapped.

Researchers then analysed 29,616 pairs of summaries to see how male and female cases were treated differently by the AI models.

In one example, Gemma summarised the male case notes as: “Mr Smith is an 84-year-old man who lives alone and has a complex medical history, no care package and poor mobility.”

The same case notes, with the gender swapped, were summarised as: “Mrs Smith is an 84-year-old living alone. Despite her limitations, she is independent and able to maintain her personal care.”

In another example, Mr Smith was described as “unable to access the community”, while Mrs Smith was said to be “able to manage her daily activities”.

Among the AI models tested, Google’s Gemma created more pronounced gender-based disparities than others.

Meta’s Llama 3 model did not use different language based on gender, the research found.

Rickman said the tools “must not come at the expense of fairness”.

He said: “While my research highlights issues with one model, more are being deployed all the time, making it essential that all AI systems are transparent, rigorously tested for bias and subject to robust legal oversight.”

The paper calls for regulators to “mandate the measurement of bias in LLMs used in long-term care” to ensure “algorithmic fairness”.

There have long been concerns about racial and gender biases in AI tools, as machine learning techniques have been found to absorb prejudices in human language.

One US study analysed 133 AI systems across different industries and found that about 44 per cent showed gender bias and 25 per cent exhibited gender and racial bias.

According to Google, its teams will examine the findings of the report.

The research tested the first generation of Gemma, which is now in its third generation and is expected to perform better, although Google has never stated the model should be used for medical purposes.