Critical Evaluation of AI Systems
Apply critical analysis frameworks to AI systems, examining their design choices, limitations, and societal implications.
Every AI system reflects the values, assumptions, and priorities of its designers and the data it was trained on. Critical evaluation of AI systems requires examining these embedded values.
Design Choices in AI Systems
AI systems are not neutral tools — they are designed artifacts that reflect choices about what to optimize for, what data to train on, what outputs to reward, and what harms to accept. These choices are made by people with particular interests, values, and blind spots.
- Objective function: What the system is optimized to maximize — engagement, accuracy, profit?
- Training data: What data was used, and whose experiences are represented?
- Reward model: What behaviors are reinforced during training?
- Safety constraints: What outputs are filtered or prevented?
- Deployment context: Who uses the system, for what purposes, with what oversight?
Social media recommendation algorithms optimized for engagement maximize time on platform. Research suggests this optimization leads to amplification of emotionally provocative content, including misinformation and divisive content — because these generate more engagement than calm, accurate content.
Questions for Critical AI Evaluation
- What is this system optimized for? Who benefits from that optimization?
- What data was it trained on? Whose experiences are represented or excluded?
- What are its known failure modes and biases?
- Who is accountable when it causes harm?
- What alternatives exist, and why was this approach chosen?
Approach AI systems as you would any designed artifact — ask who made it, for what purpose, with what assumptions, and whose interests it serves. This critical stance is more productive than either uncritical acceptance or blanket rejection.
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3 questions · Grade 11 level