Businesses gain trust through scores evaluating GenAI outputs
- A May 2025 study found that 34% of Americans trust ChatGPT more than human experts.
- Trust scores assess the alignment between FLM outputs and their trained contexts.
- Businesses must leverage FLMs and trust scores to minimize risks in GenAI applications.
In May 2025, a study by Express Legal Funding revealed that 34% of American respondents expressed greater trust in ChatGPT over human experts. This growing reliance on generative AI (GenAI) prompts questions regarding the accuracy and reliability of such outputs. The development of trust scores is put forward as a solution to ascertain the validity of outputs from focused language models (FLMs). These trust scores aim to measure the alignment of user queries with the trained knowledge of FLMs, helping mitigate risks associated with inaccuracies. Focused language models have been identified as critical tools for a variety of business applications, including customer interactions and fraud management. By employing a secondary analytic model to assign trust scores on a scale from 1 to 999, businesses can gauge whether a FLM's response reflects the training context and knowledge anchors from which it derives answers. A high trust score typically indicates correct and contextual responses, while a low score suggests misalignment and potentially inaccurate outputs. As commercial language models strive to provide satisfying responses to users, they may produce misleading information due to hallucination or lack of knowledge in specific areas. Companies are urged to adopt FLMs in conjunction with risk management strategies to ensure responsible GenAI deployment. A well-implemented trust scoring system can help shift the control of GenAI outputs from random statistical success to accountable corporate governance, reducing associated business risks. Ultimately, the integration of trust scores is crucial for maintaining a high standard of accuracy and user trust in the outputs generated by FLMs. Such measures may empower chief risk officers and decision-makers to confidently utilize GenAI technology while aligning with regulatory requirements and ethical standards. This framework not only enhances GenAI’s reliability but also ensures better alignment between technology outputs and business objectives.