A preference for native file formats in eDiscovery – the electronic exchange of information in the discovery process in litigation – is now nearly the norm, despite some resistance in years past. When electronically stored information (ESI) is preserved throughout the discovery process in its native format, all potentially relevant information – including searchable text, annotations, and metadata – is preserved as well, and is readily available to sharp legal eyes and to the growing array of eDiscovery analysis tools.
Of those tools, one of the most talked-about today is predictive coding, the application of artificial intelligence and workflow processes to keyword search, filter, and sample eDiscovery content to identify what’s most relevant to the case, in order to make the process quicker and less labor-intensive. Critics say predictive coding amounts to replacing a lawyer’s experienced eyes and judgment with inferior programming, while proponents argue that it’s not only faster (hence cheaper), but also less likely to miss something small and important, when applied smartly. Although the jury is still out and challenges remain, recent word from federal and state benches points to growing acceptance of predictive coding, especially in cases involving thousands or millions of pages of discovery.
Read more to learn about predictive coding.