AI Interview Analysis FAQ Page

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TSCinc’s FAQ section is dedicated to addressing client concerns about potential AI errors in generated artifacts.  There is a lot of concern generated by isolated examples of AI usage gone wrong.  We understand the importance of accuracy, on how AI-based Validation and Verification process serves as the ultimate solution to these concerns. Let's explore these concerns about potential AI errors FAQs in detail.

Answer:  No! Not at all, there are errors in the speech recognition and in the speaker change detection within ALL AI Generated content.   AI-generated Summaries Reports and Transcription artifacts, while advanced, contain errors due to complexities in interview audio quality, language and context. TSCinc has implemented mitigation mechanisms. 

This process involves rigorous cross-referencing and thorough analysis to enhance accuracy in the final output.  

Final transcript formal validation requires todays court reporting transcription cycle where a professional transcription is made by a human and the Interviewer receives this document and reviews the transcript to the original audio to ensure accuracy before signing a legal document.  Our reporting artifacts do not change that requirement, we just seed the human transcript process with a well developed and editable first draft to reduce labor efforts. 

Bottom Line:   Investigators are 100% responsible for the final accuracy of the reports they communicate with or submit as evidence.   AI is a tool to reduce the workload of the investigator and provide assistance to a human task of analysis and reporting.  AI is not a replacement for the Human investigator, or the human transcriptionist  

Answer: Yes, AI is adept at accurately summarizing complex interviews, though minor inaccuracies exist. TSCinc's commitment to quality is fortified by AI-based Validation and Verification. This involves a multi-step process where the AI-generated analysis undergoes automated cross-validation to ensure precision and reliability.

Answer:  Yes, a simple, non-structured question presented to AI can omit details.   Recent media coverage has rightly raised concerns about risks from false or omitted information in AI-generated content. As creators of AI tools, we recognize the risks and the need for rigorous quality control to validate AI output.

TSCinc’s AI-generated summaries and transcriptions strive for concise, comprehensive accuracy, and we understand omission of crucial data elements is a risk. TSCinc uses a phased data alignment approach with hybrid AI models employing iterative cross artifact analysis to ensure this risk is mitigated.   

Our process uses multiple AI engines and models to create draft content artifacts. We then employ an interactive content audit review harnessing multiple AI model responses to flag content anomalies. 

Ensuring AI Content Integrity through Verification and Validation reduces the chances of false or hallucinated content, as well as catching data omissions. 

Bottom Line: Humans are the final reviewers, and this is critical, as AI has limitations in reasoning and common sense. Humans excel at error checking and holistic assessment in a way machines cannot yet match.

All AI-generated content must receive final human approval before release. This Human review validates the content meets the organizations quality standards for accuracy, completeness, and logical coherence. 

Answer: AI-generated content can carry biases from training data. TSCinc addresses this through AI-based Validation and Verification.  With our focused process we leverage basic contained analysis, Bias is mitigated by the focused and artifact driven use case which reduces the bias anomalies, minimizing their impact and enhancing the overall neutrality and objectivity of the analysis. In some regards we actually use content “Bias” as a feature. 

Answer:  This AI Analysis process is designed to process recordings from formal interview room settings with professional recording equipment.  Field interview analysis results will depend on the quality of the audio artifact.  Data that includes noise from competing audio sources, or speech from the crowd as well as subject, or rambling interview questions with multiple people will produce transcripts