Corpus ID: 15501840

The Challenge and Promise of Predictive Coding for Privilege

@inproceedings{Gabriel2013TheCA,
  title={The Challenge and Promise of Predictive Coding for Privilege},
  author={Manfred Gabriel and Chris Paskach and D. Sharpe},
  year={2013}
}
  • Manfred Gabriel, Chris Paskach, D. Sharpe
  • Published 2013
  • Manfred Gabriel Chris Paskach David Sharpe Principal Partner Manager Forensic Technology Forensic Technology E-Discovery Services KPMG LLP (US) KPMG LLP (US) KPMG LLP (Canada) 345 Park Avenue 6032 Katella Avenue 333 Bay Street, Suite 4600 New York, NY 10154 Cypress, CA 90630 Toronto, ON M5H 2S1 USA USA Canada +1 (212) 954-3656 +1 (714) 934-5442 +1 (416) 777-3738 mjgabriel@kpmg.com cpaskach@kpmg.com davidsharpe@kpmg.ca 
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    References

    SHOWING 1-3 OF 3 REFERENCES