Racial disparities in education have persisted for decades in the United States. From achievement gaps in test scores to inequities in school funding and resources, students of color frequently face systemic barriers to equal educational opportunities. Emerging technologies like artificial intelligence (AI) and machine learning may offer new ways to understand and start addressing these entrenched inequities. Our biggest fear is that our old racial biases will be programmed into the machines ( and decision makers ) of tomorrow.
What is Machine Learning?
Machine learning is a type of AI that gives computer systems the ability to learn and improve from experience without being explicitly programmed. Machine learning algorithms use statistical techniques to build models that can analyze huge datasets, recognize patterns and make predictions. Unlike traditional rule-based AI systems, machine learning algorithms actually “learn” by being fed training data and adjusting their models accordingly.
Machine learning has become revolutionary in fields like computer vision, natural language processing, robotics and more. Now, researchers and educators are exploring how these same techniques could be applied to education data to illuminate issues around racial disparities and inequality.
Detecting Racial Biases and Gaps
One way machine learning is being used is to analyze large datasets of academic records, test scores, school funding data and other education-related information. The algorithms can detect patterns and differences that may point to systemic biases or gaps between student groups.
For instance, a Stanford study used machine learning on a national dataset of over 200,000 students. The algorithms found that Black and Latinx students were consistently issued more discipline referrals than their White peers, even when controlling for factors like behavior, academic performance and disabilities. This suggests potential bias in disciplinary policies.
Other studies are using AI to analyze gaps in test scores, college admissions, gifted referrals, school funding allocations and more. Machine learning techniques are able to process huge amounts of data and identify discrepancies that may be early warning signs of racial inequity.
Predicting Educational Outcomes
Machine learning is also being applied to predict how certain students will perform on tests, in college or in their careers based on patterns in their prior education data. However, some critics argue these predictive models could perpetuate existing biases if the algorithms pick up on systemic disparities.
To address this, researchers at MIT are experimenting with AI models that take factors like race and gender into account in order to make predictions that avoid reproducing inherent biases. For example, their models predicted that talented Black students were being under-referred to gifted programs compared to White students. This demonstrates how AI could be used to flag and intervene against racial gaps.
Personalizing Education and Interventions
Machine learning has great potential for personalizing education based on each student’s strengths, weaknesses, interests and needs. AI tutoring systems can adapt to students’ learning progression in real-time, while machine learning programs could tailor college recruiting efforts to reach more students from underrepresented groups.
School districts are also using AI to identify early warning signs, like chronic absenteeism, so they can intervene before students fall behind. Targeted interventions enabled by machine learning data analysis could provide crucial support to students who might otherwise get overlooked by the system.
Improving Human Decision-Making
Ultimately, the role of AI is not to replace human judgment in education altogether. Rather, machine learning can enhance human decision-making by surfacing unseen patterns and providing insights that administrators, teachers, counselors and policymakers can use.
With the comprehensive data analysis enabled by machine learning algorithms, human experts can make more informed decisions to create policies, lessons, interventions and resource allocations that help dismantle racial inequities in education. AI provides a powerful set of tools, but humans must ensure they are being applied equitably.
The Path Ahead
Machine learning and AI still have significant limitations and potential pitfalls when it comes to addressing systemic racism in education. But used carefully and ethically, they may provide new opportunities to uncover inequities as well as solutions.
Racial justice in education requires a multifaceted effort, and technology is only one piece of that complex puzzle. However, machine learning does demonstrate promising capabilities that could aid in the fight to ensure educational equality for all students regardless of race or background. The key will be pairing emerging AI with a genuine commitment to equity from educators, administrators and education policy leaders.
If technological innovations like machine learning can be harnessed as part of a broader push for racial justice in education, these groundbreaking tools have immense potential to help eliminate disparities and create better, more equal opportunities for all students to learn and thrive.
