A BiD Whist PaRty Brought to you By Necie and Nellie

 In The Lancet Digital Health, McCradden and colleagues say that the true potential of AI lies in revealing existing biases to motivate societal change and correct disparities in health care. For example, the disparity in pain management between Black and White patients is systemic, driven by biases with no scientific basis; however, race and ethnicity are still used as a proxy for indicators of health. Pierson and colleagues developed an AI model to measure osteoarthritis severity linked to knee pain and found that AI predictions correlated more closely with patient-reported pain than with radiologists' diagnosis, particularly in Black patients. The study, published in Nature Medicine, highlighted the algorithm's ability to reduce unexplained disparities entrenched in the racial and socioeconomic diversity of the data. The authors state that the AI may better capture under-served patients' pain and potentially redress disparities in access to further treatments.  

Increasing the diversity of our workforce is essential to treating health disparities with AI, stating that “more diverse perspectives will ensure that the right questions are asked”.

 The recent dismissal of a prominent Google AI and ethics researcher and co-founder of Black in AI, Timnit Gebru, has sent shockwaves through the AI community in the USA. It has been widely reported that the cause of her departure may be linked to Gebru's published research findings, which are critical of Google's AI technology. More than 2600 Google employees and 4300 academic, industry, and civil society supporters have signed a public letter that says Gebru's departure “heralds danger for people working for ethical and just AI—especially Black people and People of Color—across Google”. As a leader in the field of AI and health care, Google has a responsibility to set standards for AI research. All research must meet global ethical standards of integrity and AI researchers must understand the diverse needs of those reliant on their algorithms. Likewise, within the research community, Knight and colleagues describe the essential role of data scientists in tackling racism and discrimination in health care, and emphasise that generalisability, transparency, and reproducibility are essential for ethical and race-sensitive data-driven insights.