From Features Engineering to Scenarios Engineering for Trustworthy AI: I&I, C&C, and V&V

TLDR

AI’s rapid progress has produced state‑of‑the‑art models that depend on network architectures and feature engineering, yet high accuracy often comes at the cost of interpretability and reliability, undermining trust and posing systemic risks. The article proposes a theoretical framework of scenarios engineering to develop trustworthy AI. It introduces six key dimensions—intelligence and index, calibration and certification, and verification and validation—to build robust, trustworthy AI and outline future research directions.

Abstract

Artificial intelligence (AI)’s rapid development has produced a variety of state-of-the-art models and methods that rely on network architectures and features engineering. However, some AI approaches achieve high accurate results only at the expense of interpretability and reliability. These problems may easily lead to bad experiences, lower trust levels, and systematic or even catastrophic risks. This article introduces the theoretical framework of scenarios engineering for building trustworthy AI techniques. We propose six key dimensions, including intelligence and index, calibration and certification, and verification and validation to achieve more robust and trusting AI, and address issues for future research directions and applications along this direction.