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Questions and Answers
What is the primary goal of transparency in AI?
What is the primary goal of transparency in AI?
- To eliminate human intervention in decision-making
- To provide stakeholders with a clear understanding of AI systems (correct)
- To ensure that AI systems are fully automated
- To maximize the efficiency of AI algorithms
Why is explainability critical in AI?
Why is explainability critical in AI?
- It helps improve user interface design
- It enhances the speed of AI algorithms
- It guarantees the security of user data
- It allows organizations to identify and remove biases (correct)
Which aspect of AI ethics is associated with ensuring users' data privacy?
Which aspect of AI ethics is associated with ensuring users' data privacy?
- Bias reduction
- Risk mitigation
- Transparency and explainability (correct)
- Regulatory compliance
What impact does a lack of transparency have on user trust in AI?
What impact does a lack of transparency have on user trust in AI?
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What requirement does GDPR impose regarding AI?
What requirement does GDPR impose regarding AI?
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In what way can transparency help stakeholders?
In what way can transparency help stakeholders?
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What was a major consequence faced by OpenAI related to transparency?
What was a major consequence faced by OpenAI related to transparency?
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How does explainability support better decision making in AI?
How does explainability support better decision making in AI?
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Study Notes
Defining Transparency and Explainability in AI
- Transparency ensures stakeholders understand AI systems and decision-making processes.
- Explainability focuses on describing how AI algorithms reach decisions in a way that is understandable to non-experts.
- AI adoption raises concerns regarding transparency and integrity for users.
User Trust and Accountability
- Transparency helps users understand AI decisions, fostering trust and adoption of the technology.
- Lack of understanding in AI decisions decreases user trust and adoption.
- GDPR mandates transparency in automated decision-making.
Ethical AI and Bias Reduction
- Explainability is crucial in detecting and correcting biases in AI models.
- Organizations can identify and remove biases in their models through explainability.
- AI used in hiring or loan approvals has faced scrutiny due to biased decisions.
Better Decision Making and Public Trust
- AI is increasingly used to make complex technical decisions.
- Transparency and explainability are crucial for building public trust in AI.
- Transparency ensures stakeholders understand AI systems and decisions.
- Explainability provides comprehensible descriptions of how AI algorithms reach decisions.
Case Study #1 - OpenAI
- OpenAI, the creators of ChatGPT, have been accused of lacking transparency in data usage for model building and training.
- A breach in 2023 led to scrutiny of OpenAI's security and privacy practices.
- The breach was not disclosed to law enforcement or the public.
- Information from an employee discussion forum on OpenAI's technology was stolen by a hacker.
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