From criminal sentencing to credit scores, algorithms and artificial intelligence increasingly make high-stakes decisions that have big implications for people’s freedom, privacy, and access to opportunity.
Despite the almost-blind faith people can put in such “artificial agents,” it’s no secret that they are often biased, according to a report from the RAND Corp. that has implications for education.
More than ever, RAND researchers Osonde Osoba and Bill Welser said in an interview, it’s important to raise awareness about the role that algorithms play and to push for a public accounting of their impact—particularly in areas that involve the public interest, including K-12 education.
“For the longest time, any time questions of bias came up, hard-core researchers in artificial intelligence and algorithms dismissed them because they were not ‘engineering concerns,’ ” Osoba said. “That was OK for commercial toys, but the moment the switch was made to applying algorithms to public-policy systems, the issue of bias no longer became a triviality.”
The new RAND report, “,” does not focus on education. Instead, the authors lay out examples such as the algorithmic bias in criminal sentencing and the problems with , a chatbot developed by Microsoft that was supposed to learn the art of conversation by interacting with Twitter users—and quickly began spewing racist and vulgar hate speech.
Artificial agents can process the immense streams of data now running through society in ways that humans can’t, making them a necessary tool for modern society, the RAND researchers write. But too often, they say, the public ascribes objectivity and neutrality to algorithms and artificial intelligence, even though most function as a “black box” and some have been shown to result in different outcomes for different groups of people.
Origins of Digital Bias
Where does such bias come from?
The individual humans who program the artificial agents, who may have biases they are not even aware of; a pool of computer and data scientists that is far less diverse than the populations their products eventually affect; and biases in the data that are used to train the artificial agents to “learn” by finding patterns, RAND concluded.
All those issues are present in the ed-tech field.
One example:—educational software programs that rely on algorithms to choose what types of instructional content and learning experiences students have each day in the classroom. Algorithm-driven tools are also use by some districts to provide and to .
What if such tools are biased against students of color, or students with special needs? How would educators, parents, and students even know?
Such questions are both realistic and important for the field to be asking, Osoba and Welser said.
“Educators need to not cede complete control to the computer,” Welser said. That means being aware which products used in the classroom, school, or district rely on algorithms and artificial intelligence to make decisions; understanding what decisions they are making; and paying attention to how different groups of students are experiencing the products.
Maribeth Luftglass, the assistant superintendent for information technology for the Fairfax County, Va., schools, said it is the district’s responsibility to make sure digital tools driven by algorithms remain bias-free. When it comes to assessing students, she said, adaptive algorithms will never replace a human teacher’s evaluation.
“It’s not to say that you can’t use artificial agents to help you identify particular potential gaps in instruction and learning,” Luftglass said.
The ed-tech product-development process provides opportunities to detect bias that are unavailable to companies in other industries, said Bridget Foster, the senior vice president for the Education Technology Industry Network, a division of the Software & Information Industry Association. “In education, developers are right there in the school, in the classroom, working with educators,” she said.
Foster’s organization recommends that ed-tech companies figure out how to mitigate bias from the early planning and development stages onward.
Welser said it’s too early to try to regulate the field or mandate bias testing across the board. But, he argued, it is time for a conversation to begin in K-12 about how to address the potential biases in algorithms.
A version of this article appeared in the April 19, 2017 edition of Education Week as Algorithmic Bias a Rising Concern for K-12 Ed-Tech Field