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This study introduces a novel decision support system (DSS) approach. It ensures machine learning (ML) aligns with user decisions, promoting trust and informed choices.

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Decision support systems (DSS) often lack user-centricity.
  • Integrating machine learning (ML) into DSS requires addressing user trust and input validation.
  • Unassisted user decisions are crucial for personalized system outputs.

Purpose of the Study:

  • To propose a novel DSS approach prioritizing user input.
  • To develop an iterative ML-based system that collaborates with users.
  • To enhance user trust and decision consistency in AI-assisted processes.

Main Methods:

  • User provides an unassisted decision (αU) as initial input.
  • The system's ML-derived decision is compared against the user's αU.
  • An iterative process engages the user to reconcile discrepancies or refine inputs.

Main Results:

  • The proposed method facilitates convergence towards a shared decision.
  • It prompts users to reconsider inputs inconsistent with their initial judgment (αU).
  • Demonstrates a framework for human-AI decision alignment.

Conclusions:

  • This approach enhances user involvement in AI-driven decision-making.
  • It offers a pathway to more trustworthy and personalized decision support.
  • Potential benefits include improved user satisfaction and decision accuracy.