whether a text belongs to a certain category.  These algorithms sometimes make reference to the position of a word within a text or its relative proximity to other words.  The weights are usually determined by machine learning methods (often described as “artificial intelligence").  In this procedure, which resembles an automated form of trial and error, a system is given a “training set” consisting of documents preclassified into two or more groups, along with a set of features that might be potentially useful in classifying the sets.  The system then “learns” rules that assign weights to those features according to how well they work in classification, and assigns each new document to a category with a certain probability.  Notwithstanding their “artificial intelligence” description, automated text classification systems are unable to grasp many distinctions between types of content that would be obvious to a human.  And of critical importance, no presently conceivable technology can make the judgments necessary to determine whether a visual depiction fits the legal definitions of obscenity, child pornography, or harmful to minors.  Finally, all the filtering software companies deposed in this case use some form of human review in their process of winnowing and categorizing Web pages, although one company admitted to categorizing some Web pages without any human review.  SmartFilter states that “the final categorization of every Web site is done by a human reviewer.”  Another filtering company asserts that of the 10,000 to 30,000 Web pages that enter the “work queue” to be categorized each day, two to three percent of those are automatically categorized by their PornByRef system (which only applies to materials classified in the pornography category), and the remainder are categorized by human review.  SurfControl also states that no URL is ever added to its database without human review.

Human review of Web pages has the advantage of allowing more nuanced, if not more accurate, interpretations than automated classification systems are capable of making, but suffers from its own sources of error.  The filtering software companies involved here have limited staff, of between eight and a few dozen people, available for hand reviewing Web pages.  The reviewers that are employed by these companies base their categorization decisions on both the text and the visual depictions that appear on the sites or pages they are assigned to review.  Human reviewers generally focus on English language Web sites, and are generally not required to be multi-lingual.  Given the speed at which human reviewers must work to keep up with even a fraction of the approximately 1.5 million pages added to the publicly indexable Web each day, human error is inevitable.  Errors are likely to result from boredom or lack of attentiveness, overzealousness, or a desire to “err on the side of caution” by screening out material that might be offensive to some customers, even if it does not fit within any of the company’s category definitions.  None of the filtering companies trains its reviewers in the legal definitions concerning what is obscene, child pornography, or harmful to minors, and none instructs reviewers to take community standards into account when making categorization decisions.