Raw LLM Responses

Part 1. (The source article seems to have been pulled down. Here's a copy.) Every year, millions of students sit down for standardized tests that carry weighty consequences. National tests like the Graduate Record Examinations (GRE) serve as gatekeepers to higher education, while state assessments can determine everything from whether a student will graduate to federal funding for schools and teacher pay. Traditional paper-and-pencil tests have given way to computerized versions. And increasingly, the grading process—even for written essays—has also been turned over to algorithms. Natural language processing (NLP) artificial intelligence systems—often called automated essay scoring engines—are now either the primary or secondary grader on standardized tests in at least 21 states, according to a survey conducted by Motherboard. Three states didn’t respond to the questions. Of those 21 states, three said every essay is also graded by a human. But in the remaining 18 states, only a small percentage of students’ essays—it varies between 5 to 20 percent—will be randomly selected for a human grader to double check the machine’s work. But research from psychometricians—professionals who study testing—and AI experts, as well as documents obtained by Motherboard, show that these tools are susceptible to a flaw that has repeatedly sprung up in the AI world: bias against certain demographic groups. And as a Motherboard experiment demonstrated, some of the systems can be fooled by nonsense essays with sophisticated vocabulary. Essay-scoring engines don’t actually analyze the quality of writing. They’re trained on sets of hundreds of example essays to recognize patterns that correlate with higher or lower human-assigned grades. They then predict what score a human would assign an essay, based on those patterns. “The problem is that bias is another kind of pattern, and so these machine learning systems are also going to pick it up,” said Emily M. Bender, a professor of com