Mitigation Approach for Certain Biases in Survey Scale Input Within AI Control and Decision Systems

For the realm of Artificial Intelligence (AI), Transparency, Explainability, and Accountability (TEA) have become a critical triumvirate. Central to TEA are reliability and validity. However, classical assessment instruments may fall short in this regard. The purpose of this paper was to formulate a prospective TEA-centric AI-facilitated assessment instrument that better contends with quantitative fallacy and human bias issues that are often not addressed. Various hybridizations of Semantic Difference Scales and Object Measures (OMs) were experimented with for the purposes of enhancing the involved Multi-Attribute Decision-Making/Multi-Objective Decision-Making Subjective Measure/OM counterpoisings, particularly on the Human-Informed Repertoire of Experience side of the AI Control and Decision System - which impacts the machine-centric Decision Engineering/Decision-Making side - thereby effectuating a more robust System TEA (STEA) paradigm.