Can AI be racist? Color‐evasiveness in the application of machine learning to science assessments
Bibliographic Data
| ID | 21392631 |
|---|---|
| Authors | Tina Cheuk (0000-0001-7841-2492, School of Education, College of Science and Mathematics California Polytechnic State University San Luis Obispo California USA, corresponding author) |
| Year | 2021 |
| Volume | 105 |
| Issue | 5 |
| Pages | 825-836 |
| Publication date | 2021-09-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Science Education (JOURNAL) |
| Journal identifiers | ISSN: 0036-8326 • E-ISSN: 1098-237X |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/sce.21671 |
| OpenAlex | W3191550339 |
| Language | EN |
| Citations received | 21 |
| References cited | 58 |
Assessment developers are increasingly using the developing technology of machine learning in transforming how to assess students in their science learning. I argue that these algorithmic models further embed the structures of inequality that are pervasive in the development of science assessments in how they legitimize certain language practices that protect the hierarchical standing of status quo interests. My argument is situated within the broader emerging ethical challenges around this new technology. I apply a raciolinguistic equity analysis framework in critiquing the “new black box” that reinforces structural forms of discrimination against the linguistic repertoires of racially marginalized student populations. The article ends with me sharing a set of tactical shifts that can be deployed to form a more equitable and socially‐just field of machine learning enhanced science assessments
Argument (complex analysis) · Computational sociology · Epistemology · Equity (law) · Field (mathematics) · Inequality · Pedagogy · Political science · Science education · Set (abstract data type) · Situated · Social science · Sociology · Status quo · Artificial Intelligence · Computer Science · Educational Theory and Curriculum Studies · Law · Second Language Learning and Teaching · Student Assessment and Feedback
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| Unique citing works | 21 |
|---|---|
| Citations per year | 5,25 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 21 |