No matter, how effective predictive algorithms may be, computers are still quite dense, and need a bit of tinkering from humans to discern between subtle concepts. The Times has a nice piece documenting this human-computer interaction:
And so, while programming experts still write the step-by-step instructions of computer code, additional people are needed to make more subtle contributions as the work the computers do has become more involved. People evaluate, edit or correct an algorithm’s work. Or they assemble online databases of knowledge and check and verify them — creating, essentially, a crib sheet the computer can call on for a quick answer. Humans can interpret and tweak information in ways that are understandable to both computers and other humans.
With respect to Assisted Decision Making and quantitative legal prediction, no matter how effective Natural Langauge Processing gets, ultimately, a human being needs to import some knowledge of the law. Figuring how to scale these two processes effectively will hold the key for moving to law’s information revolution.
Wastson, Google, and Twitter are all moving towards “human curation”:
Question-answering technologies like Apple’s Siri and I.B.M.’s Watson rely particularly on the emerging machine-man collaboration. Algorithms alone are not enough.
Twitter uses a far-flung army of contract workers, whom it calls judges, to interpret the meaning and context of search terms that suddenly spike in frequency on the service.
When Google’s algorithm detects a search term for which this distilled information is available, the search engine is trained to go fetch it rather than merely present links to Web pages.
“There has been a shift in our thinking,” said Scott Huffman, an engineering director in charge of search quality at Google. “A part of our resources are now more human curated.”
Other human helpers, known as evaluators or raters, help Google develop tweaks to its search algorithm, a powerhouse of automation, fielding 100 billion queries a month. “Our engineers evolve the algorithm, and humans help us see if a suggested change is really an improvement,” Mr. Huffman said.
For the time being, the human mind is still superior:
“You need judgment, and to be able to intuitively recognize the smaller sets of data that are most important,” Mr. Taylor said. “To do that, you need some level of human involvement.”