India has 22 officially recognized languages and hundreds more spoken across its states, and a hiring process built around a single language — almost always English — inevitably interacts with that diversity in ways that shape who gets hired, often without anyone intending it to. A candidate who is genuinely excellent at a job, but less fluent or comfortable in English than a competing candidate, can be quietly disadvantaged by an interview format that inadvertently evaluates English fluency more than the actual skills the role requires.
This post looks at where language becomes a real barrier in Indian hiring, why it matters both ethically and for hiring quality, and practical steps that genuinely help — for both high-volume, standardized hiring and more senior, individualized roles.
Where language actually affects hiring outcomes
Sourcing and job postings. A job description written only in English, distributed only through English-language channels, structurally limits the candidate pool to those comfortable navigating in English from the very first touchpoint — before any actual skill evaluation has even begun.
Resume screening. Candidates whose resumes are written in a non-native language sometimes have less polished phrasing than native English speakers, even when their actual work experience is equally strong — a screening process (human or automated) that weighs phrasing and word choice heavily can inadvertently penalize this, mistaking language polish for competence.
Live interviews. This is where the effect is often most pronounced. A candidate answering complex questions in a non-native language may come across as less articulate, less confident, or slower to respond than they would in their native language — none of which necessarily reflects their actual job-relevant competence, but all of which can unconsciously shape an interviewer's overall impression.
AI-scored video interviews. If a scoring model wasn't built or tested with genuinely multilingual data, it can systematically score non-English or accented responses lower, or transcription errors on accented or non-native English speech can distort what's actually being evaluated — this is a real, documented risk category for automated interview tools specifically.
High-volume and blue-collar hiring. For roles where candidates may be more comfortable in a regional language than in English or even Hindi, an interview or assessment conducted exclusively in a language the candidate isn't fully fluent in can meaningfully undervalue genuinely qualified candidates, particularly in manufacturing, retail, and logistics roles common across diverse linguistic regions.
Why this matters beyond fairness alone
Beyond the ethical case — which is significant on its own — language-related hiring bias has a direct business cost: it narrows the effective candidate pool and risks losing genuinely qualified candidates to a competitor whose process evaluates skill more accurately, independent of language comfort. For roles where the actual job doesn't require English fluency specifically (a customer support role serving a regional market, for instance, might benefit more from strong regional-language communication than from English fluency at all), an English-centric hiring process is filtering for the wrong thing entirely relative to what the role actually needs.
For roles where the actual job doesn't require English fluency specifically, an English-centric hiring process is filtering for the wrong thing entirely relative to what the role actually needs.
Practical steps, by stage
Job descriptions and sourcing
Consider whether a role genuinely requires English proficiency, or whether that expectation has crept in as a default rather than a deliberate requirement. For roles serving regional markets or requiring interaction with predominantly regional-language customers or teams, explicitly welcoming candidates who are strong in the relevant regional language — rather than assuming English fluency as an unstated baseline — widens the effective candidate pool to people who are often better suited to the actual job.
Resume screening
Where possible, evaluate resumes based on described experience and demonstrated skills rather than penalizing phrasing or grammatical polish that reflects language background rather than job competence. If using automated screening, it's worth specifically asking how the scoring model handles resumes with non-native English phrasing, and whether it's been tested for this kind of bias.
Live interviews
For roles where English fluency isn't genuinely required for the job, consider offering interviews in a candidate's stronger language, either directly (if an interviewer is available who's fluent in that language) or with interpretation support for more senior or high-stakes conversations. Where an interview must be conducted in a shared but non-native language for one or both parties, structured interview formats — with pre-defined, clearly worded questions rather than free-flowing conversation — reduce the risk that language comfort gets conflated with genuine competence, since the evaluation rubric can be tied to substance rather than fluency or rhetorical polish.
AI-scored interviews
If using AI video interview tools, multilingual support isn't a nice-to-have feature — for a genuinely fair process across India's linguistic diversity, it's close to a baseline requirement. This means both language options for candidates to respond in, and a scoring/transcription system that's actually been validated across those languages rather than defaulting to English-optimized models applied indiscriminately. Recruitkar's AI video interviews, for instance, support 11+ languages specifically to address this gap, since a tool that only genuinely works well in English quietly reintroduces the same bias risk it might otherwise be marketed as solving.
Outreach and candidate communication
Consider whether outreach messaging, scheduling communication, and other candidate-facing touchpoints assume English proficiency by default, particularly for high-volume or regional hiring. A candidate who's a strong fit for the actual role but less comfortable navigating an entirely English-language process may disengage not because of disinterest in the role, but because of friction unrelated to job qualification. For channels like WhatsApp outreach, being willing to communicate in a candidate's preferred language where feasible, rather than defaulting rigidly to English, can meaningfully improve engagement for exactly this reason.
What "requiring English" should actually mean
It's worth being precise here rather than treating English proficiency as a blanket, unexamined requirement. For some roles, genuine English fluency is a legitimate, job-relevant requirement — a role involving regular communication with international clients or English-speaking leadership, for instance. For many other roles, what's actually needed is competent communication in whatever language the role's actual day-to-day work happens in, which may be a regional language, Hindi, English, or some combination depending on context. Being explicit and deliberate about which category a given role actually falls into — rather than defaulting to English as an unexamined baseline requirement across the board — is the foundational step that makes every other practice in this post meaningful rather than superficial.
Common mistakes worth naming directly
Assuming English fluency correlates with overall competence. This is a specific, well-documented bias pattern — confusing language fluency (in a language that may not even be a candidate's native tongue) with general intelligence or capability. The two are genuinely unrelated, and conflating them systematically disadvantages capable candidates for reasons unrelated to actual job performance.
Using informal, unvalidated interpretation during interviews. If interpretation is needed for a fair interview, using an untrained bystander or a rushed, informal translation risks distorting both the questions asked and the answers given, potentially undermining the fairness goal the interpretation was meant to serve in the first place.
Treating multilingual support as solved by simply "having someone who speaks the language" available inconsistently. Ad hoc availability of a multilingual interviewer, rather than a deliberate, consistent process, means some candidates get a fairer evaluation than others essentially by chance, depending on who happens to be available that day — this itself becomes a new, less visible form of inconsistency.
Ignoring language diversity within AI tool evaluation. As noted above, an AI interview or screening tool that hasn't been specifically tested across the languages your candidate pool actually uses can quietly encode the exact bias a well-intentioned multilingual hiring policy is trying to remove.
A realistic approach for teams getting started
For most teams, a full overhaul of every stage isn't necessary to start improving on this front. A reasonable starting point: identify which of your currently open roles genuinely require English fluency versus which don't (being honest rather than defaulting to "probably fine either way"), adjust job descriptions and sourcing for the roles where regional-language candidates are being unnecessarily filtered out, and for interview stages, prioritize structured question formats with clear scoring criteria over free-flowing conversation, since structure itself reduces the risk of language comfort being conflated with actual competence — a benefit that compounds with the language-specific practices above rather than requiring them as a prerequisite.
The bottom line
Language diversity in India isn't an edge case to accommodate occasionally — it's a structural reality that most hiring processes, built by default around English, interact with in ways that can quietly disadvantage genuinely qualified candidates. The fix isn't necessarily conducting every hiring process in every language — it's being deliberate about which roles genuinely require English fluency, structuring evaluation to reduce the conflation of language comfort with actual competence, and making sure that any technology used in the process (particularly AI-driven screening or interviewing) has genuinely been built and tested for the linguistic diversity of the candidate pool it's actually being used to evaluate.