Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What is the ethical concern with using AI for 'predictive policing'?
⚠ Common exam trap
The trap here is that candidates may focus on practical limitations like cost or accuracy (options A, C, D) rather than recognizing that the core ethical concern in AI-900 is always about fairness, bias, and societal impact, not technical performance.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Potential to perpetuate racial bias, undermine due process, and create discriminatory self-fulfilling prophecies
Predictive policing AI systems often rely on historical crime data, which can contain inherent biases from over-policing in minority communities. This can lead to a feedback loop where the AI predicts more crime in those areas, prompting more police presence, which in turn generates more arrests and reinforces the original bias. Such systems also risk undermining due process by making decisions based on statistical correlations rather than individual evidence, and can create self-fulfilling prophecies where predicted crime hotspots become actual crime hotspots due to increased enforcement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Predictive policing AI is too expensive to implement at city scale
Why it's wrong here
The financial expense of implementing predictive policing at scale is an operational constraint, but it does not constitute the principal ethical objection. Ethical critiques focus on how machine learning models can encode historical biases, producing unfair targeting and violating civil rights. Therefore, cost is a budgetary consideration rather than a fundamental ethical concern.
- ✓
Potential to perpetuate racial bias, undermine due process, and create discriminatory self-fulfilling prophecies
Why this is correct
Predictive policing algorithms often train on historical arrest or crime data, which itself may be biased due to over-policing of certain neighborhoods. This leads the model to predict higher crime in those areas, prompting more police presence, which in turn generates more reported incidents—a self-fulfilling prophecy. Such feedback loops can perpetuate racial bias and undermine due process by subjecting individuals to suspicion based on location or demographic patterns rather than individual behavior.
- ✗
Predictive policing models are too slow to be useful for real-time decisions
Why it's wrong here
While response latency can hinder real-time deployment, the primary ethical controversy in predictive policing is not computational speed but the risk of algorithmic bias. Models trained on historical arrest data may reflect systemic discrimination, leading to over-policing of marginalized communities and eroding civil liberties. Thus, performance speed is a technical limitation, not the central ethical flaw.
- ✗
Predictive policing AI might predict crimes in the wrong ZIP code
Why it's wrong here
Predicting crimes in an incorrect ZIP code is an error of geographic precision, which is a technical performance metric rather than an ethical failing. The central ethical problems in predictive policing involve systemic bias and discrimination embedded in training data, not mere spatial inaccuracy. Thus, focusing on location errors obscures the deeper civil rights concerns that algorithmic systems can exacerbate.
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