A caution against customized AI in healthcare

Area of Science:

• Artificial Intelligence
• Human-Computer Interaction
• Information Science

Background:

• The increasing integration of artificial intelligence (AI) into healthcare and other high-stakes domains.
• The growing trend towards personalized AI systems tailored to individual user preferences and biases.

Purpose of the Study:

• To critique the uncritical adoption of personalized AI in critical decision-making contexts.
• To highlight the potential risks associated with customized AI systems compromising knowledge diversity.
• To advocate for cautious and intentional application of personalized AI.

Main Methods:

• Conceptual analysis of current trends in AI personalization.
• Ethical and epistemological examination of AI's impact on knowledge dissemination.
• Review of potential consequences for informed decision-making.

Main Results:

• Personalized AI can inadvertently create information silos, limiting exposure to diverse viewpoints.
• User preferences and biases, when amplified by AI, can restrict access to critical or conflicting information.
• The reach and diversity of human knowledge may be compromised by unchecked AI customization.

Conclusions:

• Customized AI systems require careful deliberation to mitigate risks to knowledge diversity.
• Application of personalized AI must prioritize access to a wide range of perspectives for impartial decision-making.
• Intentional design and deployment are essential for leveraging AI responsibly in high-stakes domains.