MUSA 5080 Notes #2

Week 2: Algorithmic Decision Making & Census Data

Author

Fan Yang

Published

September 15, 2025

Note

Week 2: Algorithmic Decision Making & Census Data
Date: 09/15/2025

Part 1: Algorithmic Decision Making

What Is An Algorithm?

Definition: A set of rules or instructions for solving a problem or completing a task

Examples: - Recipe for cooking - Directions to get somewhere - Decision tree for hiring - Computer program that processes data to make predictions

Algorithmic Decision Making in Government

Systems used to assist or replace human decision-makers

Based on predictions from models that process historical data containing: - Inputs (“features”, “predictors”, “independent variables”, “x”) - Outputs (“labels”, “outcome”, “dependent variable”, “y”)

Real-World Examples

Criminal Justice Recidivism risk scores for bail and sentencing decisions

Housing & Finance Mortgage lending and tenant screening algorithms

Healthcare Patient care prioritization and resource allocation

Why Government Uses Algorithms

Governments have limited budgets and need to serve everyone

Algorithmic decision making promises: - Efficiency - process more cases faster - Consistency - same rules applied to everyone - Objectivity - removes human bias - Cost savings - fewer staff needed

When Algorithms Go Wrong

Remember: Data Analytics Is Subjective

Every step involves human choices: - Data cleaning decisions - Data coding or classification - Data collection - use of imperfect proxies - How I interpret results - What variables I put in the model

Important

These choices embed human values and biases

Case Study 1: Healthcare Algorithm Bias

The Problem: Algorithm used to identify high-risk patients for additional care systematically discriminated against Black patients

What Went Wrong: - Algorithm used healthcare costs as a proxy for need - Black patients typically incur lower costs due to systemic inequities in access - Result: Black patients under-prioritized despite equivalent levels of illness

Scale: Used by hospitals and insurers for over 200 million people annually

Case Study 2: Criminal Justice Algorithm Bias

COMPAS Recidivism Prediction:

The Problem: - Algorithm 2x as likely to falsely flag Black defendants as high risk - White defendants often rated low risk even when they do reoffend

Why This Happens: - Historical arrest data reflects biased policing patterns - Socioeconomic proxies correlate with race - “Objective” data contains subjective human decisions

Case Study 3: Dutch Welfare Fraud Detection

The Problem: - “Black box” system operated in secrecy - Impossible for individuals to understand or challenge decisions - Disproportionately targeted vulnerable populations

Court Ruling: - Breached privacy rights under European Convention on Human Rights - Highlighted unfair profiling and discrimination - System eventually shut down

Key Lesson: Designing Ethical Algorithms

Critical Questions to Ask

When designing algorithmic systems, consider:

  1. Proxy: What would I use to stand in for what I want?
  2. Blind spot: What data gap or historical bias could skew results?
  3. Harm + Guardrail: Who could be harmed, and what’s one simple safeguard?

Example: Emergency Response

  • Proxy: 911 call volume → stand-in for “need”
  • Blind spot: Under-calling where trust/connectivity is low
  • Harm + Guardrail: Wealthier areas over-prioritized → add a vulnerability boost (age/disability) and a minimum-service floor per zone

Potential Guardrails

  • Prioritize vulnerable groups
  • Cap disparities across areas (simple rule)
  • Human review + appeals for edge cases
  • Replace a bad proxy (collect the right thing)
  • Publish criteria & run a periodic bias check

Part 2: Census Data Foundations

Why Census Data Matters

Census data is the foundation for: - Understanding community demographics - Allocating government resources - Tracking neighborhood change - Designing fair algorithms (like those we just discussed)

Census vs. American Community Survey

Decennial Census (2020) - Everyone counted every 10 years - 9 basic questions: age, race, sex, housing - Constitutional requirement - Determines political representation

American Community Survey (ACS) - 3% of households surveyed annually - Detailed questions: income, education, employment, housing costs - Replaced the old “long form” in 2005 - A big source of data I’ll use this semester

ACS Estimates: What I Need to Know

1-Year Estimates (areas > 65,000 people) - Most current data, smallest sample

5-Year Estimates (all areas including census tracts) - Most reliable data, largest sample - What I’ll use most often

Tip

Key Point: All ACS data comes with margins of error - I need to learn to work with uncertainty

Census Geography Hierarchy

Nation
├── Regions  
├── States
│   ├── Counties
│   │   ├── Census Tracts (1,500-8,000 people)
│   │   │   ├── Block Groups (600-3,000 people)  
│   │   │   │   └── Blocks (≈85 people, Decennial only)

Most policy analysis happens at: - County level - state and regional planning - Census tract level - neighborhood analysis - Block group level - very local analysis (tempting, but big MOEs)

Part 3: Working with Census Data in R

Basic get_acs() Function

Most important function I’ll use:

library(tidycensus)

# Get state population data
get_acs(
  geography = "state",
  variables = "B01003_001",
  year = 2022,
  survey = "acs5"
)

Key parameters: geography, variables, year, survey

Understanding the Output

Every ACS result includes: - GEOID - Geographic identifier - NAME - Human-readable location name - variable - Census variable code - estimate - The actual value - moe - Margin of error

Working with Multiple Variables

# Get county data with multiple variables
county_data <- get_acs(
  geography = "county",
  variables = c(
    total_pop = "B01003_001",
    median_income = "B19013_001"
  ),
  state = "PA",
  year = 2022,
  survey = "acs5",
  output = "wide"
)

Data Cleaning Essentials

# Clean up messy geographic names
county_data <- county_data %>%
  mutate(
    county_name = str_remove(NAME, ", Pennsylvania"),
    county_name = str_remove(county_name, " County")
  )

Calculating Data Reliability

# Calculate MOE percentage and reliability categories
county_reliability <- county_data %>%
  mutate(
    moe_percentage = (median_incomeM / median_incomeE) * 100,
    reliability = case_when(
      moe_percentage < 5 ~ "High Confidence",
      moe_percentage >= 5 & moe_percentage <= 10 ~ "Moderate",
      moe_percentage > 10 ~ "Low Confidence"
    )
  )

Working with Margins of Error

Every ACS estimate comes with uncertainty

Rule of thumb: - Large MOE relative to estimate = less reliable - Small MOE relative to estimate = more reliable

In my analysis: - Always report MOE alongside estimates - Be cautious comparing estimates with overlapping error margins - Consider using 5-year estimates for greater reliability

Connecting the Dots

From Algorithms to Analysis

Today’s key connections:

  • Algorithmic Decision Making → Understanding why my analysis matters for real policy decisions
  • Data Subjectivity → Why I need to emphasize transparent, reproducible methods in my work
  • Census Data → The foundation for most urban planning and policy analysis
  • R Skills → The tools I need to do this work professionally and ethically

Questions for Reflection

As I work with data this semester, I should ask:

  1. What assumptions am I making in my data choices?
  2. Who might be excluded from my analysis?
  3. How could my findings be misused if taken out of context?
  4. What would I want policymakers to understand about my methods?
Tip

These questions will make me a more thoughtful analyst and better future policymaker

Summary

This week I learned about the critical intersection between technical skills and ethical responsibility in data analysis. My key takeaways:

  1. Algorithms are not neutral - they embed human choices and biases at every step
  2. Real-world consequences - algorithmic bias can systematically harm vulnerable populations
  3. Census data is foundational but comes with inherent uncertainties that I must acknowledge
  4. Technical competence must be paired with ethical awareness to create fair and effective policy tools
Important

Remember: Every data analysis decision I make has potential policy implications. I need to approach my work with both technical rigor and ethical consideration.