Neurosymbolic AI for Safe and Trustworthy High-Stakes Applications

Artificial intelligence is increasingly deployed in high-stakes domains such as healthcare, public welfare, and autonomous transportation, where errors can cost lives or infringe on human rights. However, current AI approaches dominated by neural networks and generative models (e.g., large language models) have well-documented shortcomings: they can hallucinate false information, exhibit bias, and lack explainability. This paper argues that these limitations make purely neural AI insufficient for safety-critical applications like medical diagnostics (where misdiagnosis or unsafe advice can be deadly), public welfare decision-making (where biased algorithms have unfairly denied benefits or targeted vulnerable groups), and autonomous systems (where failures can result in fatal accidents). We then introduce neurosymbolic AI – a hybrid paradigm combining data-driven neural networks with rule-based symbolic reasoning – as a viable path toward trustworthy AI. By integrating neural perception with symbolic knowledge and logic, neurosymbolic systems can provide built-in safety guardrails, robust reasoning abilities, and transparent decision traces. We survey evidence that neurosymbolic architectures can mitigate hallucinations and bias by enforcing domain constraints (e.g. medical guidelines or legal rules), while also enhancing explainability and accountability through explicit reasoning steps. Through examples and literature (including the IEEE's “Neurosymbolic Artificial Intelligence: Why, What, and How”), we illustrate how neurosymbolic AI can bridge the gap between the accuracy of neural methods and the reliability required in life-critical environments. Diagrams comparing architectures and error mitigation strategies are provided to visualize how the neurosymbolic approach improves safety.