See - Does machine-learning-powered software make good research decisions? Lawyers can't know for sure
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A primer on algorithm-powered legal search
An algorithm is a set of rules that a machine will follow. When lawyers perform e-research, they input information into a search field. Algorithms shape how computers interpret that information, which alters the results of the search. They might change how many cases are selected, which cases they are, and in what order.
Algorithms play a crucial role in natural language searching. They change the substance of the search itself, perhaps placing greater weight on certain words or supplementing the search with synonymous or logically related terms. Indeed, the bleeding edge of search innovation seeks to incorporate the semantic or conceptual relations between words and phrases.
For the natural language search “duties of truck drivers”, the algorithm might add to the search terms like “responsibility” or “commercial motor vehicle operator,” or it might favor cases that use “truck driver” frequently over cases that use “duty” frequently. These algorithmic functions happen behind the curtain, and they are subject to the strategic decisions of the product designer—decisions that need to be made even before the search even happens.
Compare this to good old terms-and-connectors searching. The search
dut! /s ‘truck driver”
will retrieve all cases within the selected database containing words beginning with the root “dut” that are in the same sentences as the phrase “truck driver.” Nothing more. Nothing less.
Algorithm-powered searches can improve a search, but they can make important search decisions automatically and without the searcher’s knowledge. As a result, they lack transparency, particularly compared to terms-and-connectors searches.
But does transparency even matter? Lawyers have been performing natural language searches for a long time without issue.
It might matter.
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