The identification of responsibilities within the FAIR model is about making sure everyone, regardless of who they are or where they come from, receives fair and equal treatment. This principle of equity is crucial when we use AI in social care because algorithms can unintentionally reinforce existing inequalities.
AI must not reinforce inequalities but instead promote fairness in care delivery. The systems we implement must be designed with fairness at their core, ensuring that no group—whether by race, disability, or socioeconomic status—is disadvantaged by the technology.
Case Example:
In a pilot project using AI to streamline care visits, the system initially favoured care workers in urban settings, where travel times were shorter and easier to predict. Rural workers and clients, however, were disadvantaged because the system didn’t account for the complexity of rural travel. By identifying this inequality and adjusting the AI to better reflect the realities of rural care, the service ensured that workers and clients in all areas received fair treatment. This is equity in action—making sure that no one is left behind, especially those who are already marginalised by geography, disability, or other factors.