Biases in AI-powered automatic transcription: what to consider before using it as evidence

Updated: June 2026 · By Álvaro ArrescurrenagaCEO of Voicit

Automatic transcription using artificial intelligence has become commonplace: a meeting, a job interview, a client call, or a statement can be converted into text in a matter of minutes. We use it to avoid missing details, to write minutes, and to keep a record of what was said. The problem arises when that text ceases to be a helpful tool and begins to be used for... make serious decisions —a dismissal, an agreement with contractual value, a claim, or a legal proceeding— without recalling an uncomfortable fact: No AI transcribes without errors..

And those mistakes aren't random. Many are systematic biasesThey fail more with some voices, accents, languages, or contexts than with others. The same system can transcribe one person perfectly and consistently penalize another. When the text is going to be used as evidence, understanding these biases isn't a technical detail: it's the difference between solid evidence and evidence that falls apart as soon as someone compares it with the audio.

In this guide you will see what types of biases exist, how reliable automatic transcription really is, in what cases a single error changes the result and, above all, What to review before basing an important decision on an AI-generated transcript.

What is bias in AI-powered automatic transcription?

Precision error vs systematic bias

It is important to separate two concepts that are often confused:

• Accuracy error: The tool makes a mistake with a single word. It happens to any system, and if it's distributed randomly, it's acceptable.
• Bias: those errors are concentrated systematically in certain speakers or situations (an accent, a language, deep voices, distant audio). It's more dangerous as evidence because it's predictable and directional: tends to always harm the same person.

For internal meeting minutes, a minor error is easily corrected. But for evidence that affects someone's rights, a directional bias is a serious problem: it can tip the scales unfairly without anyone noticing.

How AI works (and why it makes mistakes) when transcribing

Understanding the "how" helps anticipate errors. Modern machine transcription typically combines two parts:

• Voice recognition (ASR, Automatic Speech Recognition): It converts the audio signal into text. Language, accent, noise, and microphone quality all play a role here.
• Language models (generative AI): They "clean" and format the text, correct punctuation, and sometimes summarize. This layer improves readability... but it's also the one that can invent text that nobody said.
• Daily log: It separates and identifies the speakers (who said what). It is one of the weakest points when there is overlap.

The practical consequence: AI doesn't "understand" conversation like a person; it predicts the most likely sequence of words given the signal. When the signal is ambiguous (noise, accent, an uncommon proper name), it fills in with what statistically fits, not necessarily with what was actually said. That's why a weird name or a figure They are fertile ground for error, and that is why the same model that You use ChatGPT to transcribe It may sound perfect and yet be wrong about what's important.

Why it matters when the transcript is used as evidence

A transcription ceases to be a simple note and begins to have consequences in many everyday contexts:

• Human Resources: selection interviews, disciplinary meetings, or conversations that support a decision about a person.
• Minutes and agreements: un AI-generated meeting minutes It can collect commitments with contractual value.
• Legal scope: statements, recorded conversations or claims submitted in a proceeding.
• Journalism and investigation: direct quotes attributed to a source, where one word changes the headline.
• Sales and support: verbal commitments to clients during a call.

In all these cases, A single misspelled word can change the meaning: a disappearing "no," a confused name, an altered number, or a lost negation. The correct question, therefore, is not "Does AI transcribe well?", but "What mistakes does it make, where, and how do I detect them before using the text?".

The 8 most common biases and errors in machine transcription

1. Accent and dialect bias

The models are trained with more data on some accents than others. The same system can transcribe neutral Spanish with great accuracy and fail with regional accents or Latin American variants. A study from Stanford University (Koenecke et al., 2020, PNAS) measured this bias in the main speech recognition engines: the error rate was almost duplicated for some groups of speakers compared to others. Result: the quality of the test depends on who talk, not just about what you say.

2. Language switching and mixing (code-switching)

When languages are alternated—or when English technical terms creep in—many tools get stuck in a single language and mistranscribe the rest. This is common in professional and bilingual environments, and in areas with two co-official languages.

3. Overlapping voices and diary entry

Diarying attributes each sentence to the person who said it. When two people speak at the same time or interrupt each other, the system mixes up interventions or assigns a sentence to the wrong speaker: critical if the evidence depends on "who said what."

4. Noise, distant audio, and poor quality

A distant microphone, room echo, or background noise degrades the transcription. In face-to-face meetings recorded with a mobile phone on the table, voices from further away are lost or fabricated.

5. Technical vocabulary and proper nouns

Names of people, brands, legal or medical terms, and acronyms are where AI makes the most mistakes, and these are precisely the most identifying data of evidence.

6. Numbers, dates and negations

Amounts, dates, percentages, ID numbers and, above all, the negations ("no", "never", "without") are fragile. Losing a "no" completely reverses the meaning of a sentence.

7. Gender and voice bias

Some systems perform worse with certain tones or voice registers. This is another form of systematic bias: the same content is transcribed with varying reliability depending on the speaker's voice.

8. "Hallucinations": text invented by AI

Modern models don't just make mistakes: sometimes They generate text that nobody said, especially with low-quality audio, silences, or inaudible fragments. Researchers who audited Whisper (OpenAI's transcription model) in 2024 (report reported by APThey found fabricated fragments in around 1% of the transcripts, sometimes containing damaging phrases that were never uttered. For evidence, this is the most serious risk, because the text sounds coherent but is false.

How each bias affects evidentiary value (and how to mitigate it)

How reliable is automatic transcription?

The standard metric for measuring a voice recognition system is the WER (Word Error Rate or error rate per word)WER: the percentage of words that the system inserts, deletes, or replaces compared to what was actually said. A WER of 5% means that, out of every 100 words, 5 are incorrect.

The key is that WER is not a fixed numberIt depends heavily on the conditions. As an industry benchmark, it is considered good a transcript below a 5-10% of WERAbove 15-20%, relying on it as evidence without a thorough review is very risky.

• Clean audio, single speaker, native language: The error can be low and the result, very usable.
• Accents, noise, multiple people, or language change: The WER spikes and the transcription loses reliability precisely in the most relevant sections.
• Spanish vs. English: Many tools were originally designed in English and perform worse in Spanish. Those designed for Spanish have a head start; VoicitFor example, it reaches a 95% accuracy in Spanish (that is, around a 5% error rate under good conditions).

Human, automatic, or hybrid transcription: when to use each one?

Not everything needs the same level of warranty. This comparison helps you choose based on what's at stake:

For one piece of evidence, the approach hybrid This is usually the optimal point: AI does 95% of the work in seconds and a person validates the critical sections against the audio.

Real-life cases: when a mistake changes the outcome

These scenarios illustrate why review matters so much:

• HR — a lost denial. In a disciplinary meeting, "I did not accept those conditions" is transcribed as "I accepted those conditions." The meaning is completely reversed.
• Deed with contractual value — a figure. A "14,000" that the AI turns into "40,000" changes the commitment recorded in the minutes.
• Journalism — a quote. Attributing a word to a source that it did not say can be a legal and reputational problem.
• Diarying — who said so. In a multi-voice conversation, assigning a compromising statement to the wrong person invalidates the evidence.
• Hallucination — a phantom phrase. In a noisy section, the model "completes" a coherent sentence that was never uttered.

None of these errors are detected by reading only the transcript: the text appears correct. They are only discovered... back to the audio.

What to consider before using an AI-generated transcript as evidence

Before basing a decision on an automated transcript, review this list:

How to reduce bias: best practices

It's not about giving up on AI—it saves a lot of time—but about using it wisely:

• Start with good audio: Use a nearby microphone, ensure the room is echo-free, and avoid everyone speaking at once. Input quality is paramount.
• Choose a tool that's accurate in your language. and a variant, not a generic adaptation. We compared several in the guide to AI apps for transcribing meetings.
• Activate timestamps and speaker separation from the beginning.
• Review and correct Before accepting the text as valid, treat the transcription as a draft, not as an original.
• Document the process (what tool, what version, who reviewed it and when): reinforces traceability and credibility.
• Define an internal policy which decisions can be supported by a transcript and which require listening to the audio.

And, above all, remember the ethical dimension: a transcript affects real people. We elaborate on this in Ethics and technology in HR.

Legal framework and GDPR: recording and using transcripts

Beyond accuracy, there is a legal layer that should be clear (and which varies depending on the country and context):

• Consent and information: As a general rule, you must inform participants that the recording is taking place and for what purpose. Recording surreptitiously may invalidate the evidence and result in penalties.
• GDPR / LOPD: A voice recording is personal data. It must be handled legally, minimizing data, and ensuring its retention period and security. Using tools with [specific tools/methods/etc.] is helpful. servers in the EU and encryption.
• Evidentiary value: A transcript is usually accepted as supporting evidence, but the strongest element is the original audio and its authenticity. The transcript accompanies; it does not replace.
• Chain of custody: It preserves the original unaltered, records who accesses it and how it was obtained. An edited transcript without the underlying audio loses its power.
• EU AI Regulation (AI Act): using AI to transcribe and evaluate selection interviews It falls into a category of high riskwith obligations of human oversight and transparency. More details in the text of the AI Act.

You can find more details in the specific guide for GDPR and recording of conversationsFor decisions with legal consequences, always consult a professional.

What to look for when choosing a transcription tool

Beyond the brand, these are the characteristics that truly reduce bias and reinforce the value of a transcript:

• Real accuracy in your language. Tools designed for Spanish outperform Anglo-Saxon adaptations. Voicit It was born with Castilian as its main language and reaches a 95% accuracy in Spanish.
• Detection of multiple languages, including switching within the same recording (Voicit recognizes up to 8 languages), key to bilingual conversations.
• Original audio + timestamps that link each phrase to its exact minute, so that it can be verified.
• Editable transcript to correct errors before using it.
• Speaker identification reliable.
• GDPR compliance and servers in the EUwith encrypted data.
• It works on Meet, Teams and Zoom and in face-to-face meetingswithout adding bots to the conversation.

Actionable summary

If you only stick with one idea: Automatic transcription is an excellent draft, not an original.To use it with confidence:

• ✅ Always keep the original audio with its date and metadata.
• ✅ Check with a person the key sections against the audio.
• ✅ Watch names, numbers, and negations.
• ✅ Use a tool Precise in your language, with timestamps and speaker separation.
• ✅ Meets the GDPR and reports the registration.
• ✅ Don't make serious decisions only with the automatic summary.

Frequently Asked Questions

Álvaro Arrescurrenaga
CEO and co-founder of Voicit. He has years of experience working with HR teams, consultancies, and professionals who document meetings and interviews with AI.

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