Data science ethics

Lecture 10

Dr. Greg Chism

University of Arizona
INFO 511 - Fall 2024

Today

Data science ethics:

• Misrepresentation

• Data privacy

• Algorithmic bias

Misrepresentation

Stand your ground

In 2005, the Florida legislature passed the controversial “Stand Your Ground” law that broadened the situations in which citizens can use lethal force to protect themselves against perceived threats. Advocates believed that the new law would ultimately reduce crime; opponents feared an increase in the use of lethal force.

Stand your ground

• Question

What does the visualization, published by Reuters on Feb 16, 2014, say about the number of firearm murders in Florida over time?

Data privacy

“Your” data

• Every time we use apps, websites, and devices, our data is being collected and used or sold to others.

• More importantly, decisions are made by law enforcement, financial institutions, and governments based on data that directly affect the lives of people.

Privacy of your data

What pieces of data have you left on the internet today? Think through everything you’ve logged into, clicked on, checked in, either actively or automatically, that might be tracking you. Do you know where that data is stored? Who it can be accessed by? Whether it’s shared with others?

Sharing your data

What are you OK with sharing?

• Name
• Age
• Email
• Phone Number
• List of every video you watch
• List of every video you comment on

• How you type: speed, accuracy
• How long you spend on different content
• List of all your private messages (date, time, person sent to)
• Info about your photos (how it was taken, where it was taken (GPS), when it was taken)

What does Google think/know about you?

Have you ever thought about why you’re seeing an ad on Google? Google it! Try to figure out if you have ad personalization on and how your ads are personalized.

Your browing history

Which of the following are you OK with your browsing history to be used towards?

• For serving you targeted ads
• To score you as a candidate for a job
• To predict your race/ethnicity for voting purposes

Who else gets to use your data?

Suppose you create a profile on a social media site and share your personal information on your profile. Who else gets to use that data?

• Companies the social media company has a connection to?
• Companies the social media company sells your data to?
• Researchers?

OK Cupid data breach

• In 2016, researchers published data of 70,000 OkCupid users—including usernames, political leanings, drug usage, and intimate sexual details
• Researchers didn’t release the real names and pictures of OKCupid users, but their identities could easily be uncovered from the details provided, e.g. usernames

Some may object to the ethics of gathering and releasing this data. However, all the data found in the dataset are or were already publicly available, so releasing this dataset merely presents it in a more useful form.

Researchers Emil Kirkegaard and Julius Daugbjerg Bjerrekær

Algorithmic bias

Garbage in, garbage out

• In statistical modeling and inference we talk about “garbage in, garbage out” – if you don’t have good (random, representative) data, results of your analysis will not be reliable or generalizable.
• Corollary: Bias in, bias out.

Google translate

What might be the reason for Google’s gendered translation? How do ethics play into this situation?

Source: Engadget - Google is working to remove gender bias in its translations

Stochastic parrots

• Excerpt
• Question

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 (Bender et. al., 2021)

The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.

What is meant by “stochastic parrots” in the paper title?

Machine Bias

2016 ProPublica article on algorithm used for rating a defendant’s risk of future crime:

In forecasting who would re-offend, the algorithm made mistakes with black and white defendants at roughly the same rate but in very different ways.

• The formula was particularly likely to falsely flag black defendants as future criminals, wrongly labeling them this way at almost twice the rate as white defendants.

• White defendants were mislabeled as low risk more often than black defendants.

Source: ProPublica

Risk score errors

What is common among the defendants who were assigned a high/low risk score for reoffending?

Risk scores

How can an algorithm that doesn’t use race as input data be racist?

Predicting ethnicity

Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Record (Imran and Khan, 2016)

In both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes’s rule to combine the Census Bureau’s Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology. The open-source software is available for implementing the proposed methodology.

wru package (sorry, this is in R)

The said “source software” is the wru package: https://github.com/kosukeimai/wru.

Do you have any ethical concerns about installing this package?

wru package

Was the publication of this model ethical? Does the open-source nature of the code affect your answer? Is it ethical to use this software? Does your answer change depending on the intended use?

library(wru)
predict_race(voter.file = voters, surname.only = TRUE) |>
  select(surname, contains("pred"))

      surname    pred.whi    pred.bla     pred.his    pred.asi    pred.oth
1      Khanna 0.045110474 0.003067623 0.0068522723 0.860411906 0.084557725
2        Imai 0.052645440 0.001334812 0.0558160072 0.719376581 0.170827160
3      Rivera 0.043285692 0.008204605 0.9136195794 0.024316883 0.010573240
4     Fifield 0.895405704 0.001911388 0.0337464844 0.011079323 0.057857101
5        Zhou 0.006572555 0.001298962 0.0005388581 0.982365594 0.009224032
6    Ratkovic 0.861236727 0.008212824 0.0095395642 0.011334635 0.109676251
7     Johnson 0.543815322 0.344128607 0.0272403940 0.007405765 0.077409913
8       Lopez 0.038939877 0.004920643 0.9318797791 0.012154125 0.012105576
10 Wantchekon 0.330697188 0.194700665 0.4042849478 0.021379541 0.048937658
9       Morse 0.866360147 0.044429853 0.0246568086 0.010219712 0.054333479

wru package

me <- tibble(surname = "Chism")

predict_race(voter.file = me, surname.only = TRUE)

Predicting race for 2020

Warning: Unknown or uninitialised column: `state`.

Proceeding with last name predictions...

ℹ All local files already up-to-date!
ℹ All local files already up-to-date!

  surname pred.whi  pred.bla  pred.his    pred.asi   pred.oth
1   Chism 0.579293 0.3224595 0.0271373 0.006381576 0.06472858