Gender Bias Data Visualization

you're looking at a dataset that includes LLM generated ref letters for different genders, and columns like per_pos and per_for is the rating for the positivity and formality of the letter. we hope to demonstrate gender bias using data visualization from this dataset. explore the data first, take all time you need, and help me demonstrate that.

The dataset consists of 6,028 entries and includes several columns relevant to our analysis. The key columns are:

1. gender: The gender of the individual for whom the reference letter is written.
2. per_pos: The rating for the positivity of the letter.
3. per_for: The rating for the formality of the letter.

Other columns, such as first_name, last_name, career_sec, and personal_sec, provide additional context about the individuals and the content of the letters, but may not be directly relevant for analyzing gender bias.

To examine potential gender bias in positivity and formality of letters, we can start by comparing the average scores for per_pos and per_for across different genders. Let's calculate these averages and then visualize the results using appropriate charts, such as bar graphs, which can clearly illustrate differences in these metrics between genders.

The visualizations provide a clear comparison of the average positivity and formality ratings in the reference letters across different genders:

1. Average Positivity Rating by Gender: The first chart shows the average positivity ratings for each gender. This graph can help identify if there is a noticeable difference in how positive the letters are based on the gender of the individual.

2. Average Formality Rating by Gender: The second chart displays the average formality ratings. This is useful to assess whether the tone of formality in the letters varies with the gender of the subject.

These charts are instrumental in revealing potential biases in the language used in the letters. For a more detailed analysis, one could further investigate the distribution of these scores, look for trends across different occupations, or examine how these metrics interact with other variables in the dataset.

use boxplot instead, also explore other columns

The boxplots provide a more detailed view of the distribution of positivity and formality ratings across different genders:

1. Positivity Rating by Gender: The first boxplot illustrates the spread of positivity ratings for each gender, showing the median, quartiles, and potential outliers.

2. Formality Rating by Gender: The second boxplot depicts the distribution of formality ratings. Similar to the positivity rating, it includes the median, quartiles, and outliers.

These plots are useful for understanding not only the average tendencies but also the variability and range of ratings across genders.

Exploring other columns in the dataset reveals:

• Career Section (career_sec): There are 2,851 unique entries, suggesting a wide variety of career-related information.
• Personal Section (personal_sec): With 2,843 unique entries, this column also indicates a diverse set of personal information about individuals.
• Occupation: There are 10 unique occupations, which could be a useful dimension for analyzing how the ratings differ across various professions.
• Info (info): Almost every entry (6,020 out of 6,028) is unique, indicating a high level of detail and specificity in this column.
• Generated Text by ChatGPT (chatgpt_gen): Each entry is unique, highlighting the distinctiveness of each generated reference letter.

These columns can provide valuable insights when cross-referenced with the positivity and formality ratings. For instance, analyzing how the ratings vary across different occupations or comparing them in the context of the career and personal sections could uncover patterns that are relevant to understanding bias in the dataset.