Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization

Accuracy alone is insufficient for trustworthy AI: The paper identifies that empirical risk minimization—the standard approach driving most AI success—prioritizes accuracy over trustworthiness, which can result in biases, opacity, and other adverse effects even when systems perform well on traditional metrics.

Trustworthiness requires intentional design choices: Rather than being an afterthought, the paper argues that trustworthiness must be "translated into design choices" across all components of AI systems, suggesting that responsible AI requires deliberate engineering decisions from the ground up.

Practical guidance exists for developers: The paper positions itself as providing "actionable guidance" for building trustworthy AI systems, indicating that meeting emerging trustworthiness standards is not merely theoretical but implementable through concrete developer practices.

Trustworthiness addresses multiple concerns: The paper identifies multiple dimensions of trustworthiness beyond accuracy—including bias mitigation and transparency ("opacity")—suggesting that responsible AI requires attention to several distinct but interconnected requirements rather than a single fix.

Trustworthiness standards are emerging: The reference to "emerging standards for trustworthiness of AI" indicates that the field is developing clearer expectations and benchmarks for what responsible AI systems should achieve, moving beyond voluntary best practices.