Predictive analytics is one of the most exciting and influential technological developments in modern legal practice, but like any other emerging technological trend, lawyers must be as aware of its limitations as they are of its strengths. Lawyers who properly utilize predictive analytics can obtain key insights based on vast amounts of information, such as historical case data, judicial behavior, and litigation patterns. This can aid the savvy practitioner( and their clients) in forecasting outcomes, estimating timelines, and advising clients with greater precision. It is crucial, however, for the lawyer or firm to ensure they do not embrace data-driven decisionprediCtive anaLytiCs in LegaL praCtiCe: innovation with intentionaLity
Technology Section SectionCo-Chairs: KurtSanger – BuchananIngersoll & Rooney & CarolineSpradlin – PhelpsDunbar
it is incumbent on the lawyer to understand these technologies well enough to communicate when they are reliable, when they are not, and the reasons behind those distinctions.
making at the expense of critical thinking and real-world experience.
The Risks: 1) Biased or Incomplete Data
When dealing with predictive models of any kind in any setting, if you remember nothing else, remember this: Garbage In, Garbage Out. Any predictive model can only provide answers based on the data it is trained on. How does the model you are using account for disparities in historical rulings based on geography, litigant characteristics, or judicial tendencies? If patterns of discrimination— based on race, gender, national origin, sexual orientation, or otherwise— are embedded in the model, the predicted outcomes are inherently going to reinforce inequities. For lawyers, unwittingly trusting such a model raises ethical concerns and compromises strategic decision-making.
2) Blind Outputs The natural corollary to the Garbage In, Garbage Out rule is“??? In,??? Out.” Most predictive models don’ t share how the sausage is made( so to speak). If the lawyer doesn’ t know what variables are considered, how they are weighted, or the process by which a model draws its conclusions, how can
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