Mona Sloane, author of "Predicted: How AI Is Restructuring Social Life," argues that artificial intelligence fundamentally reshapes how societies understand reality and envision what comes next. Her work examines the mechanisms by which AI systems, increasingly embedded in everyday decision-making, alter collective imagination about the future.
Sloane characterizes this transformation as "a dangerous proposition." AI systems trained on historical data tend to encode existing biases and power structures into their outputs. When these systems inform hiring decisions, loan approvals, criminal sentencing recommendations, or content algorithms, they don't simply reflect past patterns. They actively reinforce and amplify them, creating feedback loops that make inequities appear inevitable rather than constructed.
The stakes extend beyond individual harms. Sloane contends that AI's role in shaping information environments affects how societies collectively imagine possibilities. Algorithmic systems determine what information reaches whom, which problems get framed as solvable, and which futures appear plausible. This narrows the range of imaginable futures. When algorithms consistently amplify certain narratives while suppressing others, they constrain not just what people know but what they believe is possible.
Her analysis draws on research showing how AI systems trained on internet text absorb cultural assumptions about gender, race, and capability. These latent biases don't disappear when systems are deployed. Instead, they operate at scale, influencing millions of decisions daily across healthcare, criminal justice, employment, and finance.
Sloane's framework suggests the problem isn't merely technical. It's structural. Building "fairer" algorithms without addressing the data they train on or the institutions deploying them offers only superficial solutions. Real change requires examining why particular groups are overrepresented in training data, why certain decisions were automated in the first place, and whose interests the technology serves.
Her work contributes to a growing body of scholarship questioning the assumption