TAR + Advanced AI: The Future is Now

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WHITE PAPER: TAR + ADVANCED AI – THE FUTURE IS NOW | 10 allows case teams to quickly pivot based on government negotiations and thus meet notoriously tight HSR Second Request deadlines. In fact, within the last year we have seen the government accept this type of technology (a TAR 1.0 workflow enhanced by advanced AI) during HSR Second Request negotiations. In accepting TAR enhanced by advanced AI, it is clear that government regulators are also beginning to understand the power of this technology to simplify and speed up the HSR Second Request process. As such, we expect to see more and more regulators approving advanced AI in HSR Second Request discovery. Regulator knowledge and understanding around the capabilities of advanced AI can also be a double-edged sword for those unwilling or unable to evolve with technology. Protecting sensitive information like protected health information (PHI) or personally identifiable information (PII) amongst millions of documents under short HSR Second Request timeframes used to be a herculean task. As such, regulators were more willing to allow for mistakes and inadvertent disclosures with little to no penalties or repercussions. However, as more and more states enact stricter data privacy laws while at the same time regulators see how accurately and efficiently advanced AI can pinpoint such data within large datasets, the time is coming when parties will be expected to use that technology to protect personal information from disclosure. Conclusion In the long and storied history of the legal profession, ediscovery is still new. But this doesn't mean attorneys and ediscovery practitioners can become complacent with the TAR technology of the last two decades. While in comparison to the history of litigation, a decade can seem like the blink of an eye. But for technology outside of the legal space, a decade is a lifetime. While the introduction of TAR in the mid-to-late 2000s was revolutionary to the legal industry, analytics leveraging the same basic machine learning technology had been assisting decision making in nonlegal industries since the 1960s. 38 And likewise, the technologies now being introduced to ediscovery have been used for years in other industries. Thus, there are few concerns in stability, and best practices for development and use are well known. The challenges that modern data poses to traditional ediscovery workflows are substantial and will only continue to grow. To survive and meet those challenges (as well as comply with discovery obligations), it is necessary to evolve with technology rather than fight it – just as the legal industry has done in the past with TAR. Advanced AI technology is (and will continue to be) key to this evolution – allowing law firms and case teams to rise to the challenge and manage the increasing volumes and variety of modern datasets of today and tomorrow. Leaders of corporate legal teams can no longer afford to use technology that is 40 years behind their colleagues. The rise of modern data and use of business intelligence across entire organizations means that it is time to join nonlegal disciplines in adopting new technologies built for big data to maximize budgets, optimize resources, and make strategic business decisions, all backed by data.

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