Page 164 - Legal Leaders 2018 Master Copy - 9
P. 164
programming is undoubtedly required to command machine-learning algorithms.
Your firm will need to choose software that can carry out the analyses. Those algorithms
exceed the capabilities of Excel, but many other choices exist. This author relies on the open-
source R programming language, which has been optimized for statistical analyses and data
visualization. Another open-source choice would be Python. Many commercial packages jostle
in the market, including SPSS, Tableau, SAS, and Mathematica.
As with most change initiatives, your firm should start with a pilot study and learn from it
before you roll out a more ambitious project. A practice group that wants to be able to predict
results, costs, or duration of matters from a subset of its past matters would be a good choice.
The HR group might also apply multiple regressions on data to reduce attrition or understand
better who makes partner.
3. Subject Matter Expert.
Your firm will need a lawyer who not only supports the initiative but also qualifies as a
“subject matter expert.” A SME can look at the data set and understand the relative importance
of pieces of it, what’s missing or odd, and what the firm might learn from it. A SME can
translate in-the-trenches reality to the champion and programmer. For instance, looking at a set
of information about certain kinds of cases, a subject matter expert could point out that the tenure
of the judge — senior, mid-career, newly appointed — seems likely to correlate with the
decision. An SME might also say that the duration of a case is not particularly useful because
there are long stretches where neither party takes any actions. Even more usefully, an SME could
classify matters as successful, unclear, or unsuccessful so that the software can tease out patterns
and influential variables.
Appendix – Source articles
Portions of this chapter came from the articles cited below, albeit with significant re-arrangement
and revisions.
Rees W. Morrison, Mind the Machines: Time to Explore the Potential of Machine Learning,
INSIDECOUNSEL (Oct. 21, 2016).
Rees W. Morrison, The Math Behind AI, as Explained to Lawyers, INSIDECOUNSEL (Dec. 26,
2016).
Rees W. Morrison, Drawing ACES, LEGALTECH NEWS, L12 (Feb. 2017).
Rees W. Morrison, Making the Machine-Learning Switch, 25 MET. CORP. COUNSEL 31 (Feb. 29,
2017).
Rees W. Morrison, With Data Analytics, It's Not Always ‘Follow the Money!’, LEGALTECH
NEWS (March 2017).
Rees W. Morrison, Fairness Calculations: Letting the Gini out of the Lamp, LEGALTECH NEWS
(Sept. 28, 2017).
150
Your firm will need to choose software that can carry out the analyses. Those algorithms
exceed the capabilities of Excel, but many other choices exist. This author relies on the open-
source R programming language, which has been optimized for statistical analyses and data
visualization. Another open-source choice would be Python. Many commercial packages jostle
in the market, including SPSS, Tableau, SAS, and Mathematica.
As with most change initiatives, your firm should start with a pilot study and learn from it
before you roll out a more ambitious project. A practice group that wants to be able to predict
results, costs, or duration of matters from a subset of its past matters would be a good choice.
The HR group might also apply multiple regressions on data to reduce attrition or understand
better who makes partner.
3. Subject Matter Expert.
Your firm will need a lawyer who not only supports the initiative but also qualifies as a
“subject matter expert.” A SME can look at the data set and understand the relative importance
of pieces of it, what’s missing or odd, and what the firm might learn from it. A SME can
translate in-the-trenches reality to the champion and programmer. For instance, looking at a set
of information about certain kinds of cases, a subject matter expert could point out that the tenure
of the judge — senior, mid-career, newly appointed — seems likely to correlate with the
decision. An SME might also say that the duration of a case is not particularly useful because
there are long stretches where neither party takes any actions. Even more usefully, an SME could
classify matters as successful, unclear, or unsuccessful so that the software can tease out patterns
and influential variables.
Appendix – Source articles
Portions of this chapter came from the articles cited below, albeit with significant re-arrangement
and revisions.
Rees W. Morrison, Mind the Machines: Time to Explore the Potential of Machine Learning,
INSIDECOUNSEL (Oct. 21, 2016).
Rees W. Morrison, The Math Behind AI, as Explained to Lawyers, INSIDECOUNSEL (Dec. 26,
2016).
Rees W. Morrison, Drawing ACES, LEGALTECH NEWS, L12 (Feb. 2017).
Rees W. Morrison, Making the Machine-Learning Switch, 25 MET. CORP. COUNSEL 31 (Feb. 29,
2017).
Rees W. Morrison, With Data Analytics, It's Not Always ‘Follow the Money!’, LEGALTECH
NEWS (March 2017).
Rees W. Morrison, Fairness Calculations: Letting the Gini out of the Lamp, LEGALTECH NEWS
(Sept. 28, 2017).
150
