Right Discovery Staff Writer
Continuous Active Learning (CAL) is rewriting predictive review: models ingest senior-attorney judgments in real time—think recommendation engines, except the output is evidentiary instead of entertainment.
In this executive briefing, CEO Kevin M. Clark walks through CAL deployments spanning regulatory probes, complex litigation, and compliance monitors—showing how adaptive ranking trims spend while tightening statistical rigor.
Because CAL learns iteratively, it absorbs nuance—odd contract clauses, industry slang, and privilege tells—that brittle keyword stacks miss, which is why teams under investigatory pressure increasingly default to CAL-first workflows validated through transparent sampling.
The same dynamics apply outside litigation: diligence in M&A, proactive risk sensing, and operational audits all benefit from models that improve as humans teach them what "good" looks like for that matter.
Whether you are staring down trial timelines or enterprise risk programs, CAL is becoming table stakes for teams that would rather invest in defensible math than justify linear review sunk costs.
Topics: CAL, predictive coding, machine learning, document review, Right Discovery