SC enlists lawyers for accurate translation of judicial records into English

Guwahati: The Supreme Court has enlisted practicing lawyers to assist in translating judicial records from Indian languages into English.

This initiative seeks to address the longstanding issue of poor and inaccurate translations that often distort meaning and hinder the fair administration of justice.

In a groundbreaking move, the Supreme Court Advocates-on-Record Association (SCAORA) has selected 69 lawyers to handle translations from 13 Indian languages into English.

For the first time, lawyers will now also serve as translators, assisting judges in ensuring the smooth administration of justice.

The decision follows several court orders highlighting the substandard quality of translated records, which have been problematic in maintaining the accuracy of legal proceedings.

Previously, the court relied on 60 to 70 official translators, whose workload had grown due to their involvement in translating Supreme Court judgments into regional languages.

As a result, the court allowed SCAORA to create an additional pool of translators to support the increasing demand.

SCAORA President Advocate Vipin Nair explained that the Supreme Court's order on March 18 prompted the initiative, requesting the Association’s assistance.

At that time, Chief Justice Sanjiv Khanna observed that official translators were overwhelmed with translating judgments and unavailable for other translation tasks.

Nair added that SCAORA responded by inviting applications from qualified lawyers, and the response was overwhelming, leading to the selection of nearly 70 lawyers proficient in various vernacular languages.

Justices Sanjay Karol and PK Mishra underscored the importance of accurate translation in a ruling on October 16, emphasizing the need to preserve the true meaning of the original language.

In a property dispute case involving a Muslim widow, Zoharbee, and her late husband’s property, the bench found that the translation of the civil court's judgment failed to accurately convey the original intent.

Justice Karol stressed that in legal matters, precision in language is crucial, noting how even a single word or comma can impact the overall understanding of the case.

Justice JK Maheshwari's bench highlighted this issue in a March 18 order, acknowledging that inaccurate translations posed a daily challenge.

The court had repeatedly faced cases where translations filed by advocates were flawed, thereby compromising the case's integrity.

In response, Nair informed the court in April that the then Chief Justice had approved the creation of a pool of lawyers to assist with translations.

On August 14, SCAORA Secretary Nikhil Jain issued a list of empanelled lawyers qualified to translate documents in languages such as Hindi, Marathi, Tamil, Malayalam, Bengali, Punjabi, Kannada, Telugu, Bhojpuri, Assamese, Odia, Gujarati, and Bengali.

However, SCAORA has clarified that it will not take responsibility for the quality of the translations.

Instead, each translator must provide an undertaking certifying the accuracy of their work and attach it to every translated document.

To streamline the process, the association has set a fee of Rs 100 per page (legal size), which lawyers will pay directly to the translators.

The real-time database of translators should help ensure that documents accompanying petitions meet the highest quality standards, enhancing the effectiveness of the Advocates-on-Record (AORs).

This initiative aligns with the Supreme Court's ongoing efforts to incorporate AI-based translation tools.

Since 2019, the court has been using the Supreme Court Vidhik Anuvaad Software (SUVAS) to translate judgments into regional languages, with a committee overseeing the process.

The software is also used by high courts for translating judgments.

As of December 2024, the Supreme Court had translated over 36,324 judgments into Hindi and 42,765 judgments into 17 regional languages.

By involving lawyers as translators, the Supreme Court aims to blend linguistic expertise with legal knowledge, hoping to resolve a gap that neither human labor nor machine learning has fully addressed.