India is deploying facial recognition and algorithmic policing tools at scale without a governing framework, where unaddressed opacity and discriminatory error risk making efficiency come at the cost of justice Artificial intelligence and biometric tools have moved from proposal to practice in Indian law enforcement, a shift that has been underway since at least 2017-2018 and has only gained urgency over time. After the 2020 Northeast Delhi riots, investigators turned to facial recognition technology to identify those involved. The results were uneven. In one case, a man spent four and a half years in custody before securing bail. His detention rested substantially on an 80 percent similarity score generated by Facial Recognition Technology (FRT) from a CCTV frame. Delhi Police deployed FRT in more than 750 riot investigations, yet over 80 percent of those that reached a verdict ended in acquittal or discharge. The state has structural reasons to lean on automation. The India Justice Report records a police-to-population ratio stalled at 155 per 100,000, well short of the United Nations benchmark of 222. Bihar manages roughly 81 per 100,000, and 22 percent of sanctioned posts remain vacant nationally. In an environment of chronic under-resourcing, algorithmic assistance is easily presented as an administrative remedy. These methods are far from foolproof; over-dependence risks blind policing. Algorithmic policing in India has expanded without an anchoring statute. The Project Panoptic tracker documents 170 facial recognition systems commissioned across agencies, though only around 20 are operational, at a cumulative outlay of ₹1,513 crore. Punjab’s PAIS searches over 390,000 records and 84,000 voice samples, Uttar Pradesh’s Trinetr holds more than 900,000 records, and Telangana issues TSCOP units to officers for real-time biometric matching. In 2018, under the Delhi High Court’s direction in Sadhan Haldar v. NCT of Delhi, the police acquired FRT for a single