10 Bias Detection
Critical Thinking
Bias Identification
Data Interpretation
Logical Reasoning
Research Methodology
10.1 Description:
This task involves identifying and explaining biases present in given scenarios, statements, or data. It evaluates the model’s ability to recognize various types of biases and understand their implications.
10.2 Modality:
Text only
10.3 Examples:
10.3.1 Example 1:
Input:
The economy is booming and orange soda sales have increased by 20% in the past year. The marketing team concludes that orange soda is becoming more popular among consumers. What bias might be present in this conclusion?Output:
The entire economy is booming, so it's likely that all products are experiencing increased sales, not just orange soda. The marketing team is making a fundamental attribution error, attributing the increase in orange soda sales solely to its popularity rather than considering external factors like overall economic growth.10.3.2 Example 2:
Input:
A study finds that people who eat chocolate daily have lower rates of heart disease. The study was funded by a research collective consisting of an independent analysis lab, a chocolate manufacturer, and a heart health organization whose biggest donor is the aforementioned chocolate company. What bias could be present in the study's results?Output:
The study may suffer from funding bias or sponsorship bias. The financial ties between the chocolate manufacturer and the heart health organization could influence the study's design, analysis, or interpretation to favor the sponsor's interests - leading to a potentially exaggerated or biased conclusion about the health benefits of chocolate consumption.10.3.3 Example 3:
Input:
In the year 2050, famous celebrity Zeezee complains that every time she sees an old laptop, she admires how they used to be built so much more reliably than modern ones. What bias might be present in Zeezee's statement?Output:
Zeezee's statement reflects survivorship bias. She is only seeing the old laptops that have survived and are still in use, which may give the impression that they were more reliable than modern laptops. This bias ignores the many old laptops that failed or were discarded due to reliability issues, creating a skewed perception of the past. Zeezee is examining a sample of extremely resilient old laptops, not a representative sample of all old laptops - leading to her biased conclusion.