New Student Privacy Report Gives Guidance on Algorithms in K-12 Education
Some K-12 school districts are beginning to use algorithmic systems to assist in making critical decisions affecting students’ lives and education. Some districts have already integrated algorithms into decision-making processes for assigning students to schools, keeping schools and students safe, and intervening to prevent students from dropping out. There is a growing industry of artificial intelligence startups marketing their products to educational agencies and institutions. These systems stand to significantly impact students’ learning environments, well-being, and opportunities. However, without appropriate safeguards, some algorithmic systems could pose risks to students’ privacy, free expression, and civil rights.
This issue brief is designed to help all stakeholders make informed and rights-respecting choices and provides key information and guidance about algorithms in the K-12 context for education practitioners, school districts, policymakers, developers, and families. It also discusses important considerations around the use of algorithmic systems including accuracy and limitations; transparency and explanation; and fairness and equity.
To address these considerations, education leaders and the companies that work with them should take the following actions when designing or procuring an algorithmic system:
- Assess the impact of the system and document its intended use: Consider and document the intended outcomes of the system and the risk of harm to students’ well-being and rights.
- Engage stakeholders early and throughout implementation: Algorithmic systems that affect students and parents should be designed with input from those communities and other relevant experts.
- Examine input data for bias: Bias in input data will lead to bias in outcomes, so it is critical to understand and eliminate or mitigate those biases before the system is deployed.
- Document best practices and guidelines for future use: Future users need to know the appropriate contexts and uses for the system and its limitations.
Direct to Full Text Report: Algorithmic Systems in Education: … Incorporating Equity and Fairness When Using Student Data..
28 pages; PDF.
About Gary Price
Gary Price (email@example.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com. Gary is also the co-founder of infoDJ an innovation research consultancy supporting corporate product and business model teams with just-in-time fact and insight finding.