Why You Still Need Human Editing for AI Transcription Services?

Transcription has been the most helpful tool for easily accessing any spoken content. It has also been the most effective method for providing reliable, accurate, and not misleading content for medics, lawyers, researchers, and students. However, with the technological advancements of tools such as AI for transcribing spoken data, human transcribing efforts and power are neglected.

Is that true? Not at all. AI transcription editing services still need human edits. Why is that? Humans still in the loop contribute significantly to AI transcription services. Here is an article to inform you why you still need human editing for AI transcription services.

Best-Known Facts About AI and Human Transcripts?

Even if technology moves things so fast, the essence of human touch in every transcript is crucial. Why is it so? Programmed tools do make mistakes, and it's only transcribers who can help with this. Computer machines use strategized memory, and unless someone else intervenes, incorrect data with errors is misleading. That’s why a transcript ending with a human touch appears more reliable and natural than data in the original spoken version.

It may sound unconvincing. However, the characteristics of humans and machines differ in many forms. Transcription services need to adjust to these human edits for every AI-generated transcript. But come to think of it, humans are the personnel who have trained these machines to work the way they do.

Humans have innovated tech tools and software to assist them in transcription services, yet this knowledge should be neglected and taken away. No, this should not be the case. However, AI and humans should work together to complete a goal and refine the transcript to the required state. Doing so allows:

●        Streamlined data that enhances consistent and reliable information

●        Transcripts adhere to the needed structures, styles, and layouts

●         Plagiarism detection and grammar checks

●        Preliminary data assessment, especially in the research field

So, there is still the need for human-edited transcripts as there are more risks associated with over-reliance on AI transcription editing services. Here is why you need to understand why human revisions for your AI transcripts are essential and why you need to integrate them into the transcription process.  

Why Do You Still Need Human Edits for Your AI Transcripts?

Even though tech tools have streamlined the transcription process, saving time, cost, and energy, human transcribers' efforts are still needed in this industry. Transcription services might discard this, but transcripts with a human touch still have a safer place in transcription services. Though, in a wider view, AI has continued to be beneficial, human practice cannot be neglected. Here is why human edits are still required.

1) AI Lacks an Understanding of Complex Data

AI cannot reason through content like a transcriber can, especially for scientific research, technical languages, law, and medical jargon. The transcription process requires deep knowledge and understanding of specialized subjects, their complex terms, and data methodologies. AI fails to reason like a subject expert. All it does is over-rely on the tool’s capabilities.

This disadvantage leads to data inconsistency, altering the original version of the oral data and, to some extent, creating errors and data inadequacy in the produced transcripts.

2) AI Lacks Data Evaluation

One of the most important factors during the transcription process is evaluating how the spoken data is new, unique, and original compared to other familiar data that has earlier been spoken. Second, understanding how the data must be transcribed is another vital component during the process. As such, AI tools can miss all this. AI can copy data that is similar to other recordings.

AI may fail to acknowledge the importance of oral findings, the value of research, or the crucial evidence gathered for court cases. That’s why using human reviewers and editors with deep knowledge is essential. Humans are better equipped than machines.

3) AI Ethical Concerns: Data Privacy, Transparency and Accountability

For AI, producing quality data transcripts relies on the data the tech software has interacted with over time. It’s even more confusing if the audiovisual transcribed data has such disparities as the speaker’s gender, race, or geographical region. AI tools can advertently suggest these disparities to their liking, risking an issue in data transparency. Hence, leading to loss of data diversity. It's, therefore, crucial to include human edits to understand the kind of data inputs these tech tools exhibit to allow the production of reliable transcripts.

Data accountability occurs when AI tools provide transcripts with misleading information or faulty data. One cannot understand who made these errors. Managing these accountability issues requires transcripts to be clearly reviewed for correctness. Hence, transcription services must choose an appropriate human intervention approach to validate the transcribed data.

In addition, handling data via AI can lead to potential risks such as data security breaches. Data safety is usually a primary factor that attracts a client to seek transcription services from a credible company. If so, why do you need to fail your clients on this? Whether you are offering medical, academic, legal, or research transcription services, observe data safety for your clients.

With tech scammers and elites out there, your data might leak and be applied elsewhere. Secure the client's data with robust security measures like strong passwords and a three-step authentication process. Include encryption keys to effectively control data access. However, humans work best. With transcribers trained in matters of security and signing of NDA(s), the client's data becomes safer. Working with humans and using clear protocols ensures less data exposure to potential harm from outsiders.

4) Potential Biases in AI Algorithms

The recorded data may vary in form. Hence, the nature of the untranscribed text and its complexity should be clearly understood to avoid data bias. The AI algorithms used determine the prediction and evaluation of transcript outcomes. Algorithms are a set of rules that guide the AI tool and enable it to perform transcription tasks.

Some examples of AI algorithms include Wav2Vec 2.0 and speech recognition tools such as Connectionist Temporal Classification (CTC). Unlike a lengthy one, these tools can accurately convert a short recording to audio or video. Some algorithms may not fully transcribe large files of oral data. They pick the most presented content but fail to comprehensively capture other essential details in the recording.

Despite human interventions, crucial data details may get omitted, particularly personal opinions that might arise from a certain conversation. Somehow, this appears unfair and biased. Therefore, working with human editors provides insights into what to consider and include in the transcripts. Yet, more broadly, this practice creates data with grounded truth. Moreover, with this assessment, the data output from the AI algorithms is validated to determine whether they are correct.

Conclusion

Understanding all this ensures that AI is used as a supportive tool, not a tool that replaces human knowledge and expertise in the transcription industry. Even though this innovation has great potential, offering human edits to AI transcription services enhances work ethics and trusted transcription methodologies.

Therefore, human edits are needed in transcription services, as they always are at Verbalscripts. Verbalscripts' team comprises well-trained and experienced human transcriptionists, editors, reviewers, and transcribers. At the company, humans are the only professionals allowed to edit and review transcripts, making Verblascripts the recommended transcription service across the continent.

Verbalscripts transcripts are accurate, reliable, high-quality, and of a high standard. Clients here achieve what they want.