By Prasad Ramasubramanian
The integration of artificial intelligence into marketing and PR has transformed how brands communicate. From audience targeting to content personalisation, algorithms now play a central role in shaping brand messaging. But this reliance on data-driven systems brings a critical challenge: algorithmic bias.
In a country as diverse as India, the implications are particularly significant. With its multitude of languages, cultures, and socio-economic segments, any skew in data or algorithm design can lead to exclusion or misrepresentation.
One of the most visible forms is linguistic bias. Campaigns optimised for English-speaking audiences routinely overlook vernacular users, despite regional language speakers driving a large share of India’s internet growth. Brands that ignore this gap don’t just miss reach, they signal to hundreds of millions of users that they weren’t considered.
Urban bias compounds the problem. Datasets tend to overrepresent metro consumers, producing messaging that doesn’t land with rural or semi-urban audiences. This is especially costly in FMCG, fintech, and edtech, where the next wave of growth sits firmly outside the top eight cities.
Brands are beginning to acknowledge these gaps. Companies like MiQ emphasise the importance of diverse data inputs to build more inclusive campaigns. But data diversity alone doesn’t solve it. Algorithms can identify patterns; they can’t read cultural context. That’s where PR professionals remain indispensable, ensuring messaging is appropriate, not just optimised.
The cost of getting it wrong is real. boAt’s Republic Day campaign drew backlash for being perceived as insensitive, a reminder that automated or AI-assisted content generation doesn’t come with built-in cultural judgment. Reputational damage from these missteps tends to outrun any efficiency gains.
Mitigation requires more than good intentions. Brands need to audit data sources for demographic representation, test campaigns across diverse audience segments before full deployment, and build inclusive storytelling into the brief rather than treating it as a final review checkbox. Featuring diverse voices, languages, and lived experiences doesn’t just reduce bias, it makes campaigns more likely to actually resonate.
Generative AI is adding new complexity to this. As brands use large language models to draft copy, scripts, and social content at scale, the biases embedded in training data get reproduced at speed. A model trained predominantly on urban, English-language content will reflect those assumptions unless teams actively intervene. Prompt design, output review, and regular audits of AI-generated content are becoming core skills for PR practitioners, not optional additions.
Gender and caste dynamics add another layer that algorithms frequently flatten. India’s social fabric is too textured for a targeting model built on broad demographic buckets to capture accurately. A campaign that performs well among women in Bengaluru may read very differently in a Tier 3 town in Uttar Pradesh, even if the platform classifies both as the same audience segment. Brands increasingly need ethnographic insight sitting alongside their analytics, people who understand what the data can’t say.
Transparency matters too. Consumers are increasingly aware of how their data is used, and brands that are open about their practices and actively working to address bias build more durable trust than those that stay quiet until something goes wrong.
Technology is evolving to help, AI tools designed to detect and correct dataset bias are improving. But their effectiveness depends entirely on how they’re implemented and who’s watching them.
In India, where diversity is simultaneously the market’s defining feature and its greatest operational challenge, equitable messaging isn’t a values statement. It’s a commercial requirement. Brands that treat bias as a compliance issue rather than a strategic one will keep making the same mistakes at increasing scale.
Efficiency and empathy aren’t competing priorities in AI-driven PR. The ones that last will need both.
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