Calibrating Trust in AI-Assisted Product Portfolio Decisions: A Human-in-the-Loop Governance Framework
DOI:
https://doi.org/10.22399/ijcesen.5557Keywords:
Artificial intelligence, Trust calibration, Human-in-the-loop governance, Product portfolio decisions, AI reliability, Risk-adaptive oversightAbstract
The use of artificial intelligence (AI) is becoming common practice in product portfolio management via such processes as market analysis, demand forecast, product evaluation and resource management. However, erroneous recommendations or improper user trust might compromise the quality of decision-making. This research develops a Risk-Adaptive Human-in-the-Loop Governance Model for proper calibration of user trust regarding AI-assisted product portfolio decisions. Such a model would incorporate AI reliability test, risk categorization, human verification procedure, decision authorization and monitoring of decisions' results. The level of decision-making governance will be based on the uncertainty of AI recommendations and its impact on the business. This research design will also differentiate between AI recommendation acceptance and human decision-making authority. The following conceptual research design includes comparison between human-only decision making, AI-assisted decision making with no human verification, and AI-assisted decision-making with human oversight. The numerical examples that have been provided in the current paper are purely illustrative and are not based on any completed empirical results. Thus, the applicability of the proposed framework needs to be proven further. In order to do so, future research needs to empirically test the model under controlled conditions and in various settings of portfolios.
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