Sophisticated software can help lawyers, not replace them

admin — Tue, 22 Mar 2011 13:42:03 +0000

On March 5^th, the New York Times published an article entitled “Armies of Expensive Lawyers, Replaced by Cheaper Software” which discussed the “new e-discovery software that can analyze millions of documents in a fraction of the time, and at a fraction of the cost consumed by human lawyers, even deducing patterns of behaviour”. It discussed the explosion of electronically stored information, the technology used to analyze that data and how all this has disrupted the legal job market.

The article provided a clear and concise overview of the new systems available to assist legal teams. However, the conclusion that these technologies will replace “expensive lawyers” misses the mark. All of the technologies mentioned in the article require a combination of machine and human interaction in order to operate. Humans have to “teach” the computer to identify relevant information. As with any educational process, the more highly skilled the teacher, the better the lesson will be. What the technologies will replace are lawyers working at very basic levels without a strong understanding of the case.

Although the new technologies will force lawyers to learn new ways to approach document discovery, the technologies are just one cog in the legal machine. Lawyers still need to understand the content of the documents to build their case. The new systems will help lawyers to zero in on the documents containing the relevant content.

No matter how sophisticated a computer system gets, it can’t make subjective evaluations. The legal process is not black and white – it’s ultimately based on judgement and inference. As a result, it will always require highly skilled talent.

Beyond keyword searching…

admin — Wed, 19 May 2010 19:15:55 +0000

When advising clients on their document collection, one of our key goals is to reduce the volume of records before the manual part of the review begins. The rationale is to reduce the collection of irrelevant data, which in turn reduces the labour intensive and costly human review of those records. While filtering by custodian, date, document type and keywords are the typical starting points, there are many new and advanced filtering methods available to us today, which are useful for organizing and reducing large and complex document collections.

Take, for instance, probability tracking. Probability tracking places a value on words based on relationships, proximities and frequency, in order to map out or create relationships with unknown data. Once the relationships between the words are identified, irrelevant data is more readily identified and culled from the collection.

Clustering tools analyze the content of documents, comparing the number of times different words appear. These tools then place the documents into a specified number of clusters. Concept learning technologies are a step up in complexity from probability tracking. It identifies related words and analyzes their relationship in a document that may not share the same words but nevertheless can be identified as having similar topics. These techniques become even more powerful when combined with thesauri, taxonomies and ontologies.

Complex tools such as these, based on mathematical probability and statistics, can be partnered with programs that present the information in a meaningful way for human examination and review. Collections of data represented in tables, trees, clusters and threads, allow us to understand the relationships of the records, thereby speeding up the review process.

For more information on probability tracking and clustering tools, contact Wortzman Nickle.

E-Discovery Blog Canada » software and tools

Sophisticated software can help lawyers, not replace them

Beyond keyword searching…