When Fairness Meets Consistency in AHP Pairwise Comparisons

We propose introducing fairness constraints to one of the most famous multi-criteria decision-making methods, analytic hierarchy process (AHP). offer a solution that guarantees consistency while respecting legally binding in AHP pairwise comparison matrices. Through synthetic experiment, we generate matrices different sizes and ranges/levels initial parameters (i.e., ratio disparate impact). optimize impact for various combinations these observed matrix an acceptable level minimizing deviations (or their upper triangles) before after optimization. use metaheuristic genetic algorithm set dually motivating problem operate discrete optimization procedure (in connection with Saaty’s 9-point scale). The results confirm hypothesis (with 99.5% validity concerning 2800 runs) achieving fair ranking (when comparing alternatives regarding given criterium) is possible, thus meeting two challenging goals simultaneously. This research contributes initiatives directed toward unbiased decision-making, either automated or algorithm-assisted (which case covered by this research).