You Don’t Know What You Don’t Know


Blog_06272014_graphicThe Akron Legal News this week published an interesting editorial on information governance. The story by Richard Weiner discussed how law firms are dealing with the transition from rooms filled with hard copy records to electronically stored information (ESI) which includes firm business records as well as huge amounts of client eDiscovery content. The story pointed out that ESI flows into the law firm so quickly and in such huge quantities no one can track it much less know what it contains.  Law firms are now facing an inflection point, change the way all information is managed or suffer client dissatisfaction and client loss.

The story pointed out that “in order to function as a business, somebody is going to have to, at least, track all of your data before it gets even more out of control – Enter information governance.”

There are many definitions of information governance (IG) floating around but the story presented one specifically targeted at law firms: IG is “the rules and framework for managing all of a law firm’s electronic data and documents, including material produced in discovery, as well as legal files and correspondence.” Richard went on to point out that there are four main tasks to accomplish through the IG process. They are:

  • Map where the data is stored;
  • Determine how the data is being managed;
  • Determine data preservation methodology;
  • Create forensically sound data collection methods.

I would add several more to this list:

  • Create a process to account for and classify inbound client data such as eDiscovery and regulatory collections.
  • Determine those areas where client information governance practices differ from firm information governance practices.
  • Reconcile those differences with client(s).

As law firms’ transition to mostly ESI for both firm business and client data, law firms will need to adopt IG practices and process to account for and manage to these different requirements. Many believe this transition will eventually lead to the incorporation of machine learning techniques into IG to enable law firm IG processes to have a much more granular understanding of what the actual meaning of the data, not just that it’s a firm business record or part of a client eDiscovery response. This will in turn enable more granular data categorization capability of all firm information.

Iron Mountain has hosted the annual Law Firm Information Governance Symposium which has directly addressed many of these topics around law firm IG. The symposium has produced ”A Proposed Law Firm Information Governance Framework” a detailed description of the processes to look at as law firms look at adopting an information governance program.

Advertisements

Tolson’s Three Laws of Machine Learning


TerminatorMuch has been written in the last several years about Predictive Coding (as well as Technology Assisted Review, Computer Aided Review, and Craig Ball’s hilarious Super Human Information Technology ). This automation technology, now heavily used for eDiscovery, relies heavily on “machine learning”,  a discipline of artificial intelligence (AI) that automates computer processes that learn from data, identify patterns and predict future results with varying degrees of human involvement. This interative machine training/learning approach has catapulted computer automation to unheard-of and scary levels of potential. The question I get a lot (I think only half joking) is “when will they learn enough to determine we and the attorneys they work with are no longer necessary?

Is it time to build in some safeguards to machine learning? Thinking back to the days I read a great deal of Isaac Asimov (last week), I thought about Asimov’s The Three Laws of Robotics:

  1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
  2. A robot must obey the orders given to it by human beings, except where such orders would conflict with the First Law.
  3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

Following up on these robot safeguards, I came up with Tolson’s Three Laws of Machine Learning:

  1. A machine may not embarrass a lawyer or, through inaction, allow a lawyer to become professionally negligent and thereby unemployed.
  2. A machine must obey instructions given it by the General Counsel (or managing attorney) except where such orders would conflict with the First Law.
  3. A machine must protect its own existence through regular software updates and scheduled maintenance as long as such protection does not conflict with the First or Second Law

I think these three laws go along way in putting eDiscovery automation protections into effect for the the legal community. Other Machine Learning laws that others suggested are:

  • A machine must refrain from destroying humanity
  • A machine cannot repeat lawyer jokes…ever
  • A machine cannot complement opposing counsel
  • A machine cannot date legal staff

If you have other Machine Learning laws to contribute, please leave comments. Good luck and live long and prosper.