A new project has developed a curriculum to help young students understand how algorithms can be affected by bias
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Spotted: Algorithms are everywhere, and they increasingly govern almost everything – from what we see on social media, to whether we are considered for a job or not. Now, Seattle-based responsible design company Artefact has created a project that helps students to learn more about how algorithms affect everyday life.
The Most Likely Machine project “puts students in the driver’s seat”, helping them to develop an algorithm that can predict Middle School yearbook award winners, such as the students most likely to go to a top university, most likely to go viral, and the biggest troublemakers. The project introduces young people to important ideas around digital literacy and helps students to see the impact of personal bias on algorithm design.
Artefact Founder Rob Girling told Springwise that the Most Likely Machine is designed for both self-guided exploration and for classroom use, and is currently being integrated into junior and middle school technology curriculums. Each section of the module includes a different interaction model, such as casting votes or sorting, that asks students to make their own decisions, followed by reflection and discussion.
According to Girling, “Teaching children algorithmic literacy is more relevant than ever. Algorithms underpin our lives and are only expanding in reach. Companies like Google and Facebook are grappling with the implications of algorithmic bias…From political misinformation to inequitable policing to the impact of social media on mental health, we are increasingly recognizing the impact of algorithms – and algorithmic bias – on our daily lives.”
The use of algorithms is growing. Of course, most algorithms exist to make processes and decisions easier. At Springwise, we have seen new uses for algorithms, which are often powered by AI, pop up all the time. Recent innovations include an AI-driven algorithm that can predict mechanical machine errors and an algorithm that allows insurance providers to identify underserved customers.
Written By: Lisa Magloff
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