Empowering Users: How Explainability, Transparency, and Control Can Help Designers Create Accessible AI Systems

Artificial intelligence has emerged as a crucial tool for aiding users in making decisions across diverse domains, from healthcare to finance and education. Yet, users often hesitate to trust AI systems for making critical decisions, partly due to their opacity and complexity. Designers can develop AI systems that are more trustworthy and user-friendly by focusing on three fundamental principles: explainability, transparency, and control.

Explainability pertains to an AI system’s ability to provide clear and comprehensible explanations about its decision-making process. This becomes particularly crucial when the system’s decisions have significant implications on the users’ lives, such as determining their medical diagnosis or loan approval. By providing a transparent understanding of how the AI system arrived at its decision, users can better assess its accuracy and credibility.

Transparency denotes how an AI system’s operations and decisions are open and visible to users. This principle ensures that users can scrutinize the system’s decision-making process and assess its performance. When an AI system is transparent, users can trust it and understand its limitations and strengths.

Finally, control refers to the degree to which users can intervene and modify the AI system’s decisions. Users should be able to adjust the AI system’s settings and preferences to align with their needs and preferences.

Designers can develop trust and empower users to make well-informed decisions by building AI systems that are transparent, open for inspection, and allow users to have greater control over the decision-making process. Overall, by following these three fundamental principles, designers can create AI systems that are more reliable, transparent, and trustworthy for users.