Which of these are good practices for addressing bias in AI? (Select all that apply)1 pointTechnical solution such as “zeroing out” biasSystematic auditing processes to check for biasUsing more inclusive/less biased dataUsing an adversarial attack on the AI system to change its outputs to be less biased
Question
Which of these are good practices for addressing bias in AI? (Select all that apply)1 pointTechnical solution such as “zeroing out” biasSystematic auditing processes to check for biasUsing more inclusive/less biased dataUsing an adversarial attack on the AI system to change its outputs to be less biased
Solution
The good practices for addressing bias in AI are:
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Technical solution such as “zeroing out” bias: This is not typically a good practice. While it might seem like a quick fix, it doesn't address the root cause of the bias, which is often in the data used to train the AI.
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Systematic auditing processes to check for bias: This is a good practice. Regular audits can help identify and correct bias in AI systems.
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Using more inclusive/less biased data: This is also a good practice. The more diverse and inclusive the data used to train the AI, the less likely it is to produce biased results.
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Using an adversarial attack on the AI system to change its outputs to be less biased: This is not typically a good practice. While adversarial attacks can be used to test the robustness of an AI system, they are not a reliable method for reducing bias.
So, the correct answers are "Systematic auditing processes to check for bias" and "Using more inclusive/less biased data".
Similar Questions
In AI, what does bias do? 1 pointSolves problems faster Augments human intelligence Gives systematic disadvantages to certain groups or individuals Identifies and addresses socio-technical issues raised by AI
Which of the following is a viable method to reduce bias when humans are deployed to label data for training an AI?Group of answer choicesUse only a single annotator for one set of data to reduce inconsistency.Use a large set of annotators with diverse demographic characteristics.Use another AI to suggest labels and humans adjust them if needed.Use only publicly available data, avoiding private and sensitive information.
What are the causes of AI bias?
AI can exhibit biased behaviour becauseAI cannot exhibit biased behaviourIt intentionally prefers certain outcomesIt is programmed to be biased by developersIt can replicate biases present in the training data
What are the causes of AI bias?Bias is introduced intentionally by AI developers for experimental purposesAI bias is a myth and does not actually existBias is solely caused by the AI’s programming languageMost training data for AI comes from Western countries
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