Which of the following best describes why explainability is a subject of ethical significance in "high-stakes" AI systems?

Question

BlackTom AI · Accepted Answer

Explainability enables affected people and regulators to understand and contest decisions.

1) Relevant knowledge points and concepts
- High-stakes AI systems: systems whose decisions substantially affect people’s rights, health, finances, safety, or legal status (e.g., loan denial, medical diagnosis, criminal risk scores).
- Explainability/interpretability: the ability to provide human-understandable reasons for a model’s outputs.
- Ethical principles: fairness, accountability, transparency, procedural justice, and respect for autonomy. Explainability supports these by making decision processes intelligible and contestable.

2) Step-by-step reasoning
- Step 1: High-stakes impacts create strong ethical demands for justification and recourse when decisions harm individuals.
- Step 2: If a model can produce explanations, stakeholders (affected individuals, auditors, regulators) can assess whether a decision was based on relevant, lawful, and non-discriminatory features.
- Step 3: With intelligible reasons, individuals can challenge, appeal, or seek remediation for incorrect or unfair outcomes, and regulators can audit for compliance.
- Step 4: Therefore, explainability serves as a mechanism for accountability, remedy, and trust in high-stakes contexts.

3) Calculations
- Not applicable; this is conceptual reasoning rather than numerical computation.

4) Why the correct answer is correct
- The selected option directly captures the ethical function of explainability: enabling understanding and contestation. That function ties to legal due process and practical accountability, making explainability ethically significant where decisions materially affect people.

5) Why the other options are incorrect
- “Ensures the model is accurate and reliable at all times”: Explainability does not guarantee accuracy or reliability; it only clarifies decision logic.
- “Prevents all forms of bias by default”: Explainability can help detect bias but does not automatically prevent it.
- “The lack of explainability … means they are actually less accurate”: Lack of interpretability does not imply lower accuracy; many accurate models (e.g., deep nets) are opaque.

6) Conclusion
The correct choice is: It helps affected people and regulators understand and contest decisions. This matches the provided answer.

Which of the following best describes why explainability is a subject of ethical significance in "high-stakes" AI systems?单项选择题

类似问题

An image-recognition model labels a medical image as suspicious and highlights the regions that most influenced the classification. What is the most appropriate description of how the highlighting functions as an explanation for patients and doctors?

A hospital deploys an AI sepsis early-warning system that gives emergency department physicians a risk score and brief rationale for each patient. Which design choice would most directly support appropriately calibrated trust by the intended users?

What does London mean wen he describes deep learning AI's reasoning as "atheoretical, associationist, and opaque"? (Check BOTH that apply)

Which of the following best describes a contrastive explanation in AI?

The words interpretable and explainable are closely related. However, in machine learning they refer to slightly different aspects of model behaviour. Choose the most appropriate definition below.

What is the purpose of explainable AI?

更多留学生实用工具

智能学习助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单