AI Ethics for Researchers
1. Why is data provenance important when using AI in research?
Correct! Data provenance is crucial for verifying the origin, history and integrity of datasets, which directly impacts the reliability and reproducibility of AI research findings.
Not quite. Data provenance is primarily about tracking where data comes from, how it was generated, and verifying its reliability to ensure reproducibility and integrity in research findings.
2. What is a major risk of training AI on non-diverse datasets?
Correct! When AI systems are trained on non-diverse datasets, they can perpetuate or amplify existing biases, leading to skewed or misleading conclusions that fail to represent all populations accurately.
Not quite. While overfitting can be an issue, the primary ethical concern with non-diverse datasets is that they lead to bias and underrepresentation in the model's outputs, potentially causing harm when the AI makes real-world decisions based on incomplete or skewed data.
3. Which of the following is the most responsible approach to improving the sustainability of AI systems?
Correct! Sustainable AI practices involve using appropriately-sized models for the task at hand, avoiding unnecessary retraining, and maintaining transparency about environmental impacts. This balanced approach minimizes carbon footprint while maintaining effectiveness.
Not quite. The most responsible approach to AI sustainability involves matching model complexity to task requirements, avoiding unnecessary retraining, and being transparent about environmental impacts. Larger models and frequent retraining consume significantly more energy and resources than necessary for many tasks.
4. What is a constructive way for scientists to engage with AI, in particular Generative AI?
Correct! A constructive approach to AI in science is viewing it as a complementary tool that amplifies human creativity and scientific inquiry, rather than as a replacement for human expertise or as a black box system.
Not quite. We advocate for viewing AI as a tool that complements and enhances human creativity and scientific work, rather than restricting it to specific tasks or treating it as an opaque system where only the results matter.
5. What is a key benefit of using a network of specialised, task-specific AI models within a collaborative pipeline?
Correct! Using specialised AI models for specific tasks in a collaborative workflow reduces the risk of cascading errors, as each model is constrained to perform only what it's highly trained for, enhancing reliability and reducing the chance of a single error affecting the entire process.
Not quite. The main advantage of using specialised AI models in a pipeline is that it reduces cascading errors by limiting each model to a well-defined task it's specifically trained for, rather than relying on a single model to handle diverse functions it may not be equally capable of performing.
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