AI Ethics: Algorithms Go To College

AI systems can misinterpret student behavior, leading to false accusations.

Bias in AI reflects historical prejudices encoded in data.

Predictive analytics can help identify at-risk students but may alter their outcomes.

Anonymization of data is often ineffective in protecting privacy.

Differential privacy offers a way to share data while safeguarding individual identities.

Ethics should be a core component of algorithm design.

The impact of biased algorithms can accumulate over time.

Mathematicians must understand both technical and human aspects of AI.

Society must question the values embedded in AI systems.

Small changes in initial conditions can lead to vastly different outcomes.