Research Methodology

Research methodology

An explainable, evidence-based approach to AI-supported decision-making. The material on this page is conceptual and describes the intended workflow.

Conceptual
  1. 1
    Problem definition
    State the decision, its objective, scope, constraints, and time horizon.
  2. 2
    Evidence & data quality
    Collect sources, separate verified facts from assumptions, and flag gaps.
  3. 3
    Alternative identification
    List the feasible options under consideration, noting who proposed each.
  4. 4
    Multi-criteria evaluation
    Score alternatives against weighted benefit and cost criteria.
  5. 5
    Risk & uncertainty
    Register risks with likelihood, impact, evidence, and mitigation.
  6. 6
    Sensitivity & robustness
    Test how rankings change when weights or assumptions shift.
  7. 7
    Explainable recommendation
    Present a rationale that traces back to inputs, methods, and evidence.
  8. 8
    Human review & decision
    A qualified reviewer approves, rejects, or requests changes — and it is recorded.
Candidate evaluation techniques

AHP (Analytic Hierarchy Process) derives criterion weights from pairwise comparisons.

TOPSIS ranks alternatives by their distance to an ideal and anti-ideal solution.

These are candidate techniques; their suitability depends on the decision context. Neither is integrated into this platform yet.

Research article

Placeholder — to be added by the research team.

References

Placeholder — to be added by the research team.

System architecture diagram

Placeholder — to be added by the research team.