MeitY

MeitY Presses Meta to Curb Algorithmic Bias in Content Reach

MeitY Presses Meta to Curb Algorithmic Bias in Content Reach

The government has formally told Meta to examine whether its recommendation algorithms are skewing what Indian users see during moments of civic tension. An advisory issued by the Ministry of Electronics and Information Technology on October 10 asks the company to ensure its systems do not disproportionately push a narrow set of narratives, and to tighten controls on material that could inflame public disorder or incite unlawful conduct.

What triggered the advisory

The ministry has not pointed to a specific event as the cause, but the timing is notable. The advisory follows protests in Delhi organised by the Cockroach Janta Party and Opposition groups demanding the removal of Chief Election Commissioner Gyanesh Kumar over the Special Intensive Revision of electoral rolls. According to IT Ministry sources, agencies had flagged that Meta's platforms appeared to favour certain kinds of content over others, with videos, images, statements and graphics tied to public-order situations being prominently surfaced and recommended to users, sometimes without sufficient context.

How recommendation systems shape what people see

Platforms like Instagram and Facebook do not show users a neutral stream of posts. Ranking algorithms decide what appears, in what order, and how often, based on engagement signals such as shares, comments and watch time. This design tends to reward content that provokes strong emotional reactions, which can mean that dramatic, alarming or polarising material is amplified far beyond what its accuracy or relevance would justify. The ministry's concern is that during sensitive periods, this dynamic can turn an algorithm optimised for attention into a vector for confusion or unrest, particularly when old footage, unrelated visuals or selectively edited clips are presented as live coverage of unfolding events.

The legal basis and its limits

MeitY has grounded its advisory in the Information Technology Act, 2000, and the IT Rules, 2021, which require intermediaries to exercise due diligence and to inform users about prohibited categories of content, including material likely to provoke a cognisable offence against public order. The advisory lists six specific asks, among them a review of ranking systems to prevent manipulated or out-of-context material from being repeatedly recommended, stronger escalation mechanisms for fast-spreading content, and timely cooperation with law enforcement under prescribed timelines.

  • Ensure diversity of credible information rather than narrow narrative exposure
  • Provide context for content tied to ongoing public events
  • Flag old or unrelated visuals passed off as current
  • Strengthen review of content with rapid, large-scale reach
  • Cooperate with authorities within IT Rules timelines
  • Report back to the ministry on actions taken

Digital rights advocates have pushed back on the advisory's legal standing. Apar Gupta of the Internet Freedom Foundation described it as a continuing censorship directive that is not tethered to any specific statutory provision, noting that terms such as "without adequate context" and "heightened public-order sensitivity" are undefined in law. He has called for the advisory's withdrawal and urged Meta to disclose publicly any takedowns or downranking carried out in response.

The broader tension

Advisories of this kind occupy an uneasy space: they are not binding regulations, yet platforms rarely ignore them, given the backdrop of licensing, liability protections and the government's broader power to issue directions under the IT Act. That leaves companies like Meta navigating a gap between informal government pressure and formal legal obligation, while users are left to trust that content moderation decisions made under such pressure do not quietly narrow the range of political speech they encounter. The episode reflects a wider, ongoing argument in India and elsewhere about who gets to decide what counts as harmful amplification, and how that judgment is checked.