1. Key Legal Problem

AI transcription errors create three main evidentiary risks:

(A) Distortion of Original Evidence

Meaning of statements may change due to incorrect transcription (e.g., “I will not come” vs “I will come”).

(B) Authenticity Challenge

Opposing party may argue:

• AI tool is unreliable
• No human verification
• Possibility of manipulation

(C) Chain of Custody Issues

Courts require proof that:

• original recording is intact
• transcription is faithful
• no tampering occurred

2. Legal Framework (India)

(i) Indian Evidence Act / Bharatiya Sakshya Adhiniyam (BSA 2023)

• Electronic records are admissible if authenticity is proven.
• Requires reliability of process used to produce evidence.

(ii) Section 65B (Old Act) / Equivalent Provisions in BSA 2023

• Certificate of authenticity for electronic records is critical.
• Applies to recordings, chats, AI-generated transcripts.

3. Leading Case Laws (at least 6)

1. Anvar P.V. v. P.K. Basheer (2014) 10 SCC 473

Principle:
Electronic evidence is admissible only if accompanied by proper certification under Section 65B.

Relevance to AI transcription:
AI-generated transcripts without certification or validation may be inadmissible.

2. Arjun Panditrao Khotkar v. Kailash Kushanrao Gorantyal (2020) 7 SCC 1

Principle:
Reaffirmed strict compliance with Section 65B requirements.

Relevance:
Courts emphasized reliability and procedural safeguards for electronic records, including digitally processed data like AI transcripts.

3. Tomaso Bruno v. State of Uttar Pradesh (2015) 7 SCC 178

Principle:
Electronic evidence is crucial in modern trials; courts should not ignore digital evidence.

Relevance:
Supports admissibility of AI-generated transcripts, but only when reliability is established.

4. State (NCT of Delhi) v. Navjot Sandhu (2005) 11 SCC 600

Principle:
Initially allowed secondary evidence of electronic records even without strict compliance (later overruled in part).

Relevance:
Shows historical flexibility, but later cases tightened rules for accuracy—important in AI disputes.

5. Shafhi Mohammad v. State of Himachal Pradesh (2018) 2 SCC 801

Principle:
Relaxed 65B requirement where certificate is not possible.

Relevance:
Could apply in AI transcription disputes when party does not control device or software generating transcript.

(Note: later limited by Arjun Panditrao case)

6. P. Gopalkrishnan @ Dileep v. State of Kerala (2019) 5 SCC 1

Principle:
Accused has right to access electronic evidence for fair trial.

Relevance:
Opposing party can demand original recording if AI transcription is disputed.

7. Ram Singh v. Col. Ram Singh (1985 Supp SCC 611)

Principle:
Audio recordings are admissible if authenticity and tamper-proof condition are proven.

Relevance:
Foundation case for evaluating reliability of recorded conversations later transcribed by AI.

8. K. Ramajayam v. Inspector of Police (2016) (Madras HC)

Principle:
Electronic evidence must be clear, authentic, and properly verified.

Relevance:
Supports rejection of distorted or unclear transcriptions.

4. Application to Marriage AI Transcription Disputes

In marriage litigation, AI transcription errors can affect:

(A) Cruelty Allegations

Incorrect transcription may exaggerate or soften abusive language.

(B) Consent in Marriage Disputes

Misinterpreted messages may falsely show coercion or agreement.

(C) Maintenance Cases

Financial statements or admissions may be wrongly recorded.

(D) Domestic Violence Evidence

Voice notes may lose tone/context after AI conversion.

5. How Courts Evaluate AI Transcriptions

Courts generally test:

1. Original Source Availability

Is the original audio/video available?

2. Integrity of AI Process

Was AI tool reliable and tested?

3. Human Verification

Was transcription reviewed by a competent person?

4. Possibility of Tampering

Any editing, compression, or reprocessing?

5. Consistency with Other Evidence

Do chats, witnesses, or documents match?

6. Common Dispute Scenarios

Scenario 1: AI mishears names or words

Leads to false attribution in marital accusations.

Scenario 2: Translation errors (regional language → English/Hindi)

Changes emotional or legal meaning.

Scenario 3: Automated speech-to-text in WhatsApp/recordings

Incorrect punctuation changes intent.

Scenario 4: Edited AI summaries used instead of raw transcript

Courts may reject summarized AI output.

7. Legal Position Summary

• AI-transcribed evidence is not automatically invalid
• But it is not automatically reliable either
• Courts require:
  • original electronic record
  • certification/verification
  • proof of accuracy of transcription process