AI Ethics and Society Homework Project 2 Readings Chapter 4 Weapons of Math Destruction Propaganda Machine Online Advertising Optional Darrel Huff Chapter 1 How to lie with statistics Norton New York 1954 Internet Search Huff How to lie with statistics pdf In this assignment you ll begin the process of exploring relationships in data You ll accomplish this task by computing some basic statistical measures on one of three datasets This is a good time to learn or reboot your Python coding skills Step 1 Select one of the datasets available on CANVAS for completion of this assignment mental health in tech survey 2019 csv Mental Health in Tech Survey Survey on Mental Health in the Tech Workplace in 2019 https osmihelp org research Dependent Variables o treatment Have you ever sought treatment for a mental health disorder from a mental health professional FALSE TRUE o mental health disclosure Would you feel comfortable discussing a mental health issue with o mental health support Overall how well do you think the tech industry supports employees your direct supervisor s Yes Maybe No with mental health issues numerical 1 5 hospital discharge 2017 csv New York Hospital Inpatient Discharge Information in 2017 https health data ny gov dataset Hospital Inpatient Discharges SPARCS De Identified 22g3 z7e7 Dependent Variables o Length of Stay a numeric value representing number of days between admission and discharge o APR Severity of Illness Code a numeric value representing severity of illness deaths in custody csv Information on deaths that occur in custody or during the process of arrest in California https openjustice doj ca gov data Dependent Variables o manner of death Suicide Natural Accidental Homicide Cannot be Determined Other for all others o custody status Sentenced Awaiting Booking Booked Awaiting Trial Booked No Charges Filed Other for all others Step 2 Explore the data by answering the following questions Which dataset did you select How many observations are in the dataset How many variables in the dataset Does this dataset seem to belong to a regulated domain in law as discussed in the lectures If yes which one How many variables in the dataset are associated with a legally recognized protected class In a table format list those variables associated with a protected class identify the protected class and the associated legal precedence law as discussed in the lectures Example Output associated with a different dataset Dataset Housing Decisions in Metro Atlanta Number of Observations 1 400 Number of Variables 16 Regulated Domain in Law Housing Fair Housing Act Number of Protected Class Variables 2 nationality pregnant y n Protected Class National origin Pregnancy Law Civil Rights Act of 1964 1991 Pregnancy Discrimination Act Step 3 Determine the relationships between dependent and independent variables The frequency of a value represents the number of times a value occurs in a data set Compute the frequency of each value associated with each dependent variable listed in Step 1 as a function of all of the protected class variables independent variables identified in Step 2 Create table s and histogram s comparing the frequency values of the dependent variable as a function of the independent variable Hint For variables that are continuous you might consider creating intervals that represent the data For categorical ordinal nominal values you might consider converting to numerical values based on a reasonable albeit subjective ordering Example Output for One Dependent Independent Variable Combination Dependent Variable Housing Decision Y N Frequency of Y 50 Frequency of N 120 Frequency of Y 130 Frequency of N 20 Independent Variable Protected Class Variable Pregnant Y Pregnant N Step 4 Show how to manipulate with data Select one protected class variable independent variable and one dependent variable 1 Create a graph to support the fairness hypothesis The system is fair There is no difference in the outcomes 2 Create a graph to support the bias hypothesis The system is biased There is a difference in the outcomes For each provide a brief description of your manipulations Example Output 1 Fair Hypothesis As seen from this graph housing decisions are not dependent on the pregnancy status of women Manipulations Used line graph Increased Scale to 50 Mapped the ratio of positive Y decisions i e 50 180 versus 130 180 No label on the Y Axis 2 Bias Hypothesis As seen from this graph housing decisions are significantly dependent on the pregnancy status of women This hypothesis was easily supported with the data so didn t require much in manipulations Used stacked bar graph Reduced Scale Reworded labels Step 5 Given your selected protected class variable independent variable calculate the average using mean median and mode of the protected class group Hint Variables might need to be converted to numerical values as needed Run the random sampling method using 50 of the data to create a reduced dataset Calculate the average mean median and mode of the protected class group Indicate if there is a difference or not between the original dataset and the reduced dataset for any of the averages Provide all results Example Output Illustrating Possible Average Computations Protected Class Variable Mean Median Mode Pregnant Original Data Set Reduced Data Set Difference 0 NO 0 NO 0 NO 1 YES 0 NO 0 NO No Difference Difference No Difference Step 6 Given your reduced dataset from Step 5 Repeat Step 3 frequency and histogram using your selected dependent variable as a function of your selected independent variable from Step 4 Explain any differences in at least 2 sentences If you used the random sampling method would members associated with the protected class variable benefit or be harmed Explain your reasoning in at least 2 sentences Step 7 Turn in a report in PDF format documenting your outputs in each Step The report should follow the JDF format Jupyter notebook ipynb files submission is optional but a final PDF document per JDF format is required The file name for submission is GTuserName Assignment 2 for example Joyner03 Assignment2 Reports that are not neat and well organized will receive up to a 10 point deduction All charts graphs and tables should be generated in Python or Excel or any other suitable software application else appropriate points will be deducted which could be the maximum