Announcements

Midterm exam released on Monday!
- Take home available: Monday noon - Weds 10am
- Oral exams: Thurs / Fri

Goals for Today

Modeling & Ethics: Algorithmic bias
Quantifying uncertainty

Sampling variability
Sampling distributions

Data Ethics: Algorithmic Bias

Return to the Americian Statistical Association’s “Ethical Guidelines for Statistical Practice”

Integrity of Data and Methods

“The ethical statistical practitioner seeks to understand and mitigate known or suspected limitations, defects, or biases in the data or methods and communicates potential impacts on the interpretation, conclusions, recommendations, decisions, or other results of statistical practices.”

“For models and algorithms designed to inform or implement decisions repeatedly, develops and/or implements plans to validate assumptions and assess performance over time, as needed. Considers criteria and mitigation plans for model or algorithm failure and retirement.”

Algorithmic Bias

Algorithmic bias: when the model systematically creates unfair outcomes, such as privileging one group over another.

Example: The Coded Gaze

Facial recognition software struggles to see faces of color.
Algorithms built on a non-diverse, biased dataset.

Algorithmic Bias

Algorithmic bias: when the model systematically creates unfair outcomes, such as privileging one group over another.

Example: COMPAS model used throughout the country to predict recidivism

Differences in predictions across race and gender

Cynthia Rudin and collaborators wrote The Age of Secrecy and Unfairness in Recidivism Prediction
- Argue for the need for transparency in models that make such important decisions.

Shift Gears: Statistical Inference

The ❤️ of statistical inference is quantifying uncertainty

library(tidyverse)
ce <- read_csv("data/fmli.csv")
summarize(ce, meanFINCBTAX = mean(FINCBTAX))

# A tibble: 1 × 1
  meanFINCBTAX
         <dbl>
1       62480.

The ❤️ of statistical inference is quantifying uncertainty

library(tidyverse)
ce <- read_csv("data/fmli.csv")
summarize(ce, meanFINCBTAX = mean(FINCBTAX))

# A tibble: 1 × 1
  meanFINCBTAX
         <dbl>
1       62480.

Like with regression, need to distinguish between the population and the sample

Parameters:
- Based on the population
- Unknown then if don’t have data on the whole population
- EX: \(\beta_o\) and \(\beta_1\)
- EX: \(\mu\) = population mean

Statistics:
- Based on the sample data
- Known
- Usually estimate a population parameter
- EX: \(\hat{\beta}_o\) and \(\hat{\beta}_1\)
- EX: \(\bar{x}\) = sample mean

Quantifying Our Uncertainty

R has been giving us uncertainty estimates:

library(pdxTrees)
kenilworth <- get_pdxTrees_parks() %>%
  filter(Park %in% c("Kenilworth Park")) %>% 
  drop_na(Functional_Type, Native)

ggplot(kenilworth, aes(x = Tree_Height,
                       y = DBH, color = Native)) +
  geom_point() +
  stat_smooth(method = "lm", se = TRUE) +
  scale_color_manual(values = c("plum3", "goldenrod")) + 
  theme(legend.position = "bottom")

Quantifying Our Uncertainty

R has been giving us uncertainty estimates:

modTrees <- lm(Tree_Height ~ DBH*Native, data = kenilworth)
library(moderndive)
get_regression_table(modTrees)

# A tibble: 4 × 7
  term          estimate std_error statistic p_value lower_ci upper_ci
  <chr>            <dbl>     <dbl>     <dbl>   <dbl>    <dbl>    <dbl>
1 intercept        7.39      3.65       2.03   0.044    0.201   14.6  
2 DBH              2.25      0.17      13.3    0        1.92     2.59 
3 Native: Yes     11.1       5.59       1.98   0.049    0.067   22.1  
4 DBH:NativeYes    0.315     0.215      1.47   0.144   -0.108    0.739