Ethical AI Practices & Data Governance Flashcards

Why is transparency important in AI systems?

Answer: It helps users trust AI decisions

Transparency in AI systems is crucial because it allows users to understand how and why an AI model arrived at a particular decision or prediction. When the decision-making process is clear and interpretable, it builds trust and confidence in the AI's outputs. This is especially important in sensitive applications where accountability and user acceptance are paramount.

What is the goal of data governance?

Answer: To manage data integrity and compliance

The goal of data governance is to establish and enforce policies, procedures, and standards for managing an organization's data assets. This ensures data integrity, quality, security, and compliance with relevant regulations and ethical guidelines. Effective data governance is essential for reliable AI systems, as it guarantees that the data used for training and operation is trustworthy and legally sound.

What does 'algorithmic bias' refer to?

Answer: Unfair outcomes produced by models

Algorithmic bias refers to systematic and repeatable errors in a computer system that create unfair or discriminatory outcomes. This bias often arises from biased training data, flawed assumptions in the algorithm's design, or unrepresentative samples. It can lead to models making prejudiced decisions that disproportionately affect certain groups, undermining fairness and equity.

Which principle is essential for ethical AI?

Answer: Fairness

Fairness is an essential principle for ethical AI because it dictates that AI systems should treat all individuals and groups equitably, without discrimination or prejudice. This means ensuring that AI decisions do not perpetuate or amplify existing societal biases, especially concerning protected characteristics. Upholding fairness helps build trust, promotes social good, and prevents harmful impacts from AI deployment.

Why is informed consent important in data collection?

Answer: To respect user rights and transparency

Informed consent is vital in data collection as it upholds individuals' rights to privacy and autonomy. It ensures that users are fully aware of what data is being collected, how it will be used, and for what purpose, before they agree to its collection. This transparency builds trust and is a fundamental ethical and legal requirement for responsible data handling.

What role does anonymization play in data governance?

Answer: It removes identifying information from data

Anonymization plays a critical role in data governance by transforming data so that individual subjects cannot be identified, directly or indirectly. By removing or scrambling personally identifiable information (PII), it helps protect privacy while still allowing the data to be used for analysis, research, or AI training. This process is crucial for balancing data utility with privacy protection and regulatory compliance.

Which regulation focuses on data protection in the EU?

Answer: GDPR

The General Data Protection Regulation (GDPR) is a comprehensive data protection law enacted by the European Union. It focuses on giving individuals control over their personal data and imposes strict rules on how organizations collect, process, and store personal information. GDPR has significantly influenced global data privacy standards, emphasizing consent, transparency, and accountability for data handling.

What is the risk of using poor quality data in AI?

Answer: Incorrect and potentially harmful outcomes

Using poor quality data in AI systems poses a significant risk because the model learns directly from the data it's trained on. If the data is inaccurate, incomplete, or biased, the AI model will inevitably produce incorrect, unreliable, or even harmful outcomes. This can lead to flawed predictions, unfair decisions, and a loss of trust in the AI system's capabilities.

What does accountability in AI mean?

Answer: Ensuring responsibility for AI impacts

Accountability in AI means establishing clear responsibility for the decisions, actions, and impacts of AI systems, especially when things go wrong. It involves identifying who is answerable for the design, deployment, and oversight of AI, ensuring mechanisms are in place to address errors, biases, or harms. This principle is crucial for building trustworthy AI and fostering responsible innovation.