Quiz: Detecting Bias and Preventing Outlier Over-Indexing

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Question 1 of 3

1. Question

What is a common source of algorithmic bias when using AI for market research?

Training the model on a perfectly representative and randomized sample.
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Using historical data that inherently reflects existing societal inequalities or skewed demographics.
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Ensuring all customer demographic groups are equally weighted in the dataset.
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Regularly auditing the algorithm's outputs for fairness and accuracy.
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Question 2 of 3

2. Question

In the context of AI market analysis, what does ‘outlier over-indexing’ refer to?

When an AI model disproportionately weighs extreme or highly vocal minority opinions as representative of the broader market.
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When an AI model correctly identifies and removes all statistical anomalies before generating a research report.
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The process of restructuring a database index to retrieve market research surveys at a faster speed.
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When an AI tool ignores negative feedback entirely to focus exclusively on positive market trends.
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Question 3 of 3

3. Question

Which of the following is an effective technique to prevent an AI model from over-indexing on outliers in customer feedback?

Relying solely on raw, unweighted data from public social media platforms.
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Decreasing the size of the training dataset to focus exclusively on 1-star and 5-star reviews.
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Applying statistical normalization techniques and setting appropriate confidence thresholds for sentiment scoring.
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Programming the AI to automatically delete any data points that do not exactly match the median average.
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Quiz: Detecting Bias and Preventing Outlier Over-Indexing

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