Machine learning systems are not inherently fair or just by default

Evidence

Levendowski’s pioneering article (and subsequent book-length scholarship) on the relationship between copyright law and the implicit-bias problem in AI training data is foundational for understanding the equity dimensions of contemporary AI-IP debate. Levendowski argued that the chilling effects of copyright on AI training datasets contributes to bias, since developers default to public-domain corpora skewed toward older, whiter, more male material. — , The Braided Reading EPUB, 2242 (#expands, via linked claim)

Categorization

• corroborated: 3 independent passages support this claim
• XREF: connects to: Machine-learning bias often stems from unrepresentative training data; Even massive, comprehensive text datasets reflect biases present in the real world; AI researchers aim to align general-purpose systems with hard-to-specify human intentions (+6 more)

Related

• Copyright’s chilling effect on AI training data worsens algorithmic bias (similarity 0.94)
• Even massive, comprehensive text datasets reflect biases present in the real world (similarity 0.91)
• Machine-learning bias often stems from unrepresentative training data (similarity 0.82)
• Machine learning increasingly intertwines technical problems with human and social questions (similarity 0.80)
• AI researchers aim to align general-purpose systems with hard-to-specify human intentions (similarity 0.79)

Library Connections

• Machine-learning bias often stems from unrepresentative training data #exemplifies (0.85)
• Even massive, comprehensive text datasets reflect biases present in the real world #exemplifies (0.82)

Broader Connections

• Copyright’s chilling effect on AI training data worsens algorithmic bias #expands (0.60) — , The Braided Reading EPUB, 2242