What is a potential consequence of bias in data used to train an AI system?
Understand the Problem
The question asks about the potential negative effects of bias in data used to train AI systems. It presents four options, and the task is to identify the one that accurately describes a consequence of such bias.
Answer
The AI's outputs will reflect and amplify the data biases, leading to unfair or inaccurate outcomes.
A potential consequence of bias in data used to train an AI system is that the AI's outputs will reflect and potentially amplify those biases, leading to unfair, discriminatory, or inaccurate outcomes.
Answer for screen readers
A potential consequence of bias in data used to train an AI system is that the AI's outputs will reflect and potentially amplify those biases, leading to unfair, discriminatory, or inaccurate outcomes.
More Information
Biased AI outcomes can perpetuate societal inequalities, damage reputation, and even result in legal liabilities.
Tips
A common mistake is assuming that more data automatically leads to a better outcome; however, if the data is biased, more of it will simply amplify the bias. To avoid this, carefully examine and clean the training data to ensure diversity and representation.
Sources
- AI Bias Examples | IBM - ibm.com
- What Do We Do About the Biases in AI? - Harvard Business Review - hbr.org
- Ethical Use of Training Data: Ensuring Fairness & Data Protection in AI - lamarr-institute.org
AI-generated content may contain errors. Please verify critical information
