Improving fairness in aging-related AI: a conceptual model for mitigating biases.

Area of Science:

• Gerontology and Artificial Intelligence (AI)
• Healthcare Equity
• Algorithmic Bias Mitigation

Background:

• AI offers significant potential to enhance accuracy, efficiency, and personalized care in gerontology by analyzing complex patient data.
• Existing healthcare disparities related to race, gender, and socioeconomic status may be amplified by biases embedded within AI systems.
• Addressing these biases is crucial for equitable AI implementation in elder care.

Purpose of the Study:

• To present actionable strategies for enhancing fairness and equity in aging-related AI.
• To introduce a conceptual model for de-biasing AI from a gerontology perspective.
• To highlight the real-world implications of AI potential and bias in gerontology.

Main Methods:

• Analysis of AI potential and biases in gerontology through three representative scenarios: disease diagnosis, chronic condition management, and geriatric rehabilitation.
• Identification of major bias sources throughout the AI lifecycle, including data, model selection, and user feedback.
• Development of an innovative conceptual model for de-biasing AI in aging.

Main Results:

• AI's transformative potential in gerontology is illustrated, alongside prevalent biases.
• Key sources of bias identified include unrepresentative training data, unsuitable AI model choices, and lack of diverse user feedback.
• The study outlines aging- and older adult-focused de-biasing approaches.

Conclusions:

• Actionable strategies and practical frameworks are provided to mitigate bias in AI for gerontology.
• The proposed model aims to foster an equitable, effective, and inclusive socio-technological environment for older adults.
• Implementing these de-biasing approaches is essential for realizing AI's full potential in elder care without exacerbating disparities.