描述
By: Norma Tamaria Möllers | Series: Inside Technology
A rare look into the making of algorithmic surveillance—and how algorithmic harm happens.
How do computer scientists negotiate the thorny political issues their work is involved in—such as state violence, racial profiling, or the deskilling of labor—when they make surveillance technology? Examining how disciplinary conventions and larger social forces shape how computer scientists think, Against Bias by Norma Möllers diagnoses a deeply rooted culture of disengagement in computer science, one that discourages computer scientists from speaking and thinking politically about their work, or considering how their technologies aid larger systems of oppression or human destruction.
The author explores how and why, sometimes despite the best intentions, questions of justice, ethics, and democracy can disappear from the process of making algorithmic technologies, and what that has to do with the disciplinary cultures in which computer scientists are embedded. Speaking to broader debates on AI and social justice, this book makes the case for an “impure” computer science that doesn’t pretend to be neutral but teaches its students (how) to do no harm.
A rare look into the making of algorithmic surveillance—and how algorithmic harm happens.
How do computer scientists negotiate the thorny political issues their work is involved in—such as state violence, racial profiling, or the deskilling of labor—when they make surveillance technology? Examining how disciplinary conventions and larger social forces shape how computer scientists think, Against Bias by Norma Möllers diagnoses a deeply rooted culture of disengagement in computer science, one that discourages computer scientists from speaking and thinking politically about their work, or considering how their technologies aid larger systems of oppression or human destruction.
The author explores how and why, sometimes despite the best intentions, questions of justice, ethics, and democracy can disappear from the process of making algorithmic technologies, and what that has to do with the disciplinary cultures in which computer scientists are embedded. Speaking to broader debates on AI and social justice, this book makes the case for an “impure” computer science that doesn’t pretend to be neutral but teaches its students (how) to do no harm.