pp<-tibble( year =c(2006, 2006, 2013, 2013), service =c("Abortion", "Cancer", "Abortion", "Cancer"), n =c(289750, 2007371, 327000, 935573))ggplot(pp, aes(x =year, y =n, color =service))+geom_point(size =2)+geom_line(linewidth =1)+geom_text(aes(label =n), nudge_y =100000)+geom_text(aes(label =year), nudge_y =200000, color ="darkgray")+labs( title ="Services provided by Planned Parenthood", caption ="Source: Planned Parenthood", x =NULL, y =NULL)+scale_x_continuous(breaks =c(2006, 2013))+scale_y_continuous(labels =label_number(big.mark =","))+scale_color_manual(values =c("red", "purple"))+annotate( geom ="text", label ="Abortions", x =2009.5, y =400000, color ="red")+annotate( geom ="text", label ="Cancer screening\nand prevention services", x =2010.5, y =1600000, color ="purple")+theme_minimal()+theme(legend.position ="none")
Uncertainty - Catalan independence
On December 19, 2014, the front page of Spanish national newspaper El País read “Catalan public opinion swings toward ‘no’ for independence, says survey”.
Democratic Party, Governor Gavin Newson, League of Women Voters of California, California Medical Association, Democracy for America (progressive PAC), etc.
NO: keep the cash bail system.
Republican Party, American Bail Coalition, ACLU of Southern California, NAACP, California Asian Pacific Chamber of Commerce, etc.
If passed, each county would be empowered to develop a tool that predicts the risk of a suspect reoffending before trial.
Judges would consult this prediction to make bail decisions.
What might “risk assessment” look like?
Something we will study in two weeks:
Let’s say above the line means high risk, which means no bail. Is this progress?
What happens when we try “predictive policing”?
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.
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.
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?
reasonable people can debate if this outcome is good or bad;
every Californian was invited to decide whether statistics and data science should be deployed to make decisions with major social consequences. They opted out;
This vote was held in the pre-ChatGPT era. What would the outcome be today? Is the case for YES stronger or weaker?
Another algorithmic decision…
Armies of stats PhDs go to work on these models. They have no training in the ethics of what they’re doing.
A success story?
. . .
Data + Model to predict timing of menstrual cycle:
. . .
A perfect microcosm of the themes of our course, and maybe one of the real triumphs of data and modeling improving modern life.
. . .
Some good news?
Faster, more accurate cancer screening?
Augmenting doctors’ diagnostic capacity so that they make fewer mistakes, treat more people, and focus on other aspects of care:
Recent Nobel Prize
AlphaFold2: “predicting 3D structures [of proteins] (\(y\)) directly from the primary amino acid sequence (\(x\)).”
“researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.”
At some point during your data science learning journey you will learn tools that can be used unethically
You might also be tempted to use your knowledge in a way that is ethically questionable either because of business goals or for the pursuit of further knowledge (or because your boss told you to do so)
How do you train yourself to make the right decisions (or reduce the likelihood of accidentally making the wrong decisions) at those points?
A Brief History of Statistics (Buckle Up)
Origins
Compared to other sciences, statistics is relatively young: most major contributions happened in the 1900s -Three main waves
Franics Galton and Karl Pearson evolving the field from math
Sir Ronald Fisher and The Design of Experiments
Jerzy Neyman and Egon Pearson
Galton and Pearson
Galton introduced standard deviation, correlation, regression
Together with Karl Pearson founded mathematical statistics
Other contributions: hypothesis testing, p-values
First statistics department in University College London
Fisher and The Design of Experiments
Many contributions fundamental to experimental design
Analysis of variance or ANOVA
Agriculture! A very popular data source for statistics
Besides statistics, what did these founding father of statistics have in common?
RACISM!
Why I bring this up?
Statistics was used to advance eugenics “research”
Acknowledging that statistics is not merely numbers but rather a powerful tool that caused real harm is a starting point toward ethical and thoughtful usage
The midterm tomorrow features the popular (and seemingly innocuous) dataset “Iris”: a very famous dataset on flowers
Unfortunately, one of the original uses of that dataset was by Fisher in the Annals of Eugenics
We will use it tomorrow with the understanding that while these people involved in eugenics created many of the methods today, we hold the power to use them ethically and responsibly
Armies of stats PhDs go to work on these models. They have no training in the ethics of what they’re doing.
