Archive for Pittsburgh

algorithm for predicting when kids are in danger [guest post]

Posted in Books, Kids, Statistics with tags , , , , , , , , , , , , , , , , , on January 23, 2018 by xi'an

[Last week, I read this article in The New York Times about child abuse prediction software and approached Kristian Lum, of HRDAG, for her opinion on the approach, possibly for a guest post which she kindly and quickly provided!]

A week or so ago, an article about the use of statistical models to predict child abuse was published in the New York Times. The article recounts a heart-breaking story of two young boys who died in a fire due to parental neglect. Despite the fact that social services had received “numerous calls” to report the family, human screeners had not regarded the reports as meeting the criteria to warrant a full investigation. Offered as a solution to imperfect and potentially biased human screeners is the use of computer models that compile data from a variety of sources (jails, alcohol and drug treatment centers, etc.) to output a predicted risk score. The implication here is that had the human screeners had access to such technology, the software might issued a warning that the case was high risk and, based on this warning, the screener might have sent out investigators to intervene, thus saving the children.

These types of models bring up all sorts of interesting questions regarding fairness, equity, transparency, and accountability (which, by the way, are an exciting area of statistical research that I hope some readers here will take up!). For example, most risk assessment models that I have seen are just logistic regressions of [characteristics] on [indicator of undesirable outcome]. In this case, the outcome is likely an indicator of whether child abuse had been determined to take place in the home or not. This raises the issue of whether past determinations of abuse– which make up  the training data that is used to make the risk assessment tool–  are objective, or whether they encode systemic bias against certain groups that will be passed through the tool to result in systematically biased predictions. To quote the article, “All of the data on which the algorithm is based is biased. Black children are, relatively speaking, over-surveilled in our systems, and white children are under-surveilled.” And one need not look further than the same news outlet to find cases in which there have been egregiously unfair determinations of abuse, which disproportionately impact poor and minority communities.  Child abuse isn’t my immediate area of expertise, and so I can’t responsibly comment on whether these types of cases are prevalent enough that the bias they introduce will swamp the utility of the tool.

At the end of the day, we obviously want to prevent all instances of child abuse, and this tool seems to get a lot of things right in terms of transparency and responsible use. And according to the original article, it (at least on the surface) seems to be effective at more efficiently allocating scarce resources to investigate reports of child abuse. As these types of models become used more and more for a wider variety of prediction types, we need to be cognizant that (to quote my brilliant colleague, Josh Norkin) we don’t “lose sight of the fact that because this system is so broken all we are doing is finding new ways to sort our country’s poorest citizens. What we should be finding are new ways to lift people out of poverty.”

Unusual timing shows how random mass murder can be (or even less)

Posted in Books, R, Statistics, Travel with tags , , , , , , , , on November 29, 2013 by xi'an

This post follows the original one on the headline of the USA Today I read during my flight to Toronto last month. I remind you that the unusual pattern was about observing four U.S. mass murders happening within four days, “for the first time in at least seven years”. Which means that the difference between the four dates is at most 3, not 4!

I asked my friend Anirban Das Gupta from Purdue University are the exact value of this probability and the first thing he pointed out was that I used a different meaning of “within 4”. He then went into an elaborate calculation to find an upper bound on this probability, upper bound that was way above my Monte Carlo approximation and my rough calculation of last post. I rechecked my R code and found it was not achieving the right approximation since one date was within 3 days of three other days, at least… I thus rewrote the following R code

T=10^6
four=rep(0,T)
for (t in 1:T){
  day=sort(sample(1:365,30,rep=TRUE)) #30 random days
  day=c(day,day[day>363]-365) #account for toric difference
  tem=outer(day,day,"-")
  four[t]=(max(apply(((tem>-1)&(tem<4)),1,sum)>3))
  }
mean(four)

[checked it was ok for two dates within 1 day, resulting in the birthday problem probability] and found 0.070214, which is much larger than the earlier value and shows it takes an average 14 years for the “unlikely” event to happen! And the chances that it happens within seven years is 40%.

Another coincidence relates to this evaluation, namely the fact that two elderly couples in France committed couple suicide within three days, last week. I however could not find the figures for the number of couple suicides per year. Maybe because it is extremely rare. Or undetected…

Pittsburgh snapshot

Posted in pictures, Running, Travel, University life with tags , , , , on November 8, 2013 by xi'an

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Unusual timing shows how random mass murder can be (or not)

Posted in Books, R, Statistics, Travel with tags , , , , , , , , on November 4, 2013 by xi'an

This was one headline in the USA Today I picked from the hotel lobby on my way to Pittsburgh airport and then Toronto this morning. The unusual pattern was about observing four U.S. mass murders happening within four days, “for the first time in at least seven years”. The article did not explain why this was unusual. And reported one mass murder expert’s opinion instead of a statistician’s…

Now, there are about 30 mass murders in the U.S. each year (!), so the probability of finding at least four of those 30 events within 4 days of one another should be related to von Mises‘ birthday problem. For instance, Abramson and Moser derived in 1970 that the probability that at least two people (among n) have birthday within k days of one another (for an m days year) is

p(n,k,m) = 1 - \dfrac{(m-nk-1)!}{m^{n-1}(m-nk-n)!}

but I did not find an extension to the case of the four (to borrow from Conan Doyle!)… A quick approximation would be to turn the problem into a birthday problem with 364/4=91 days and count the probability that four share the same birthday

{30 \choose 4} \frac{90^{26}}{91^{29}}=0.0273

which is surprisingly large. So I checked with a R code in the plane:

T=10^5
four=rep(0,T)
for (t in 1:T){
  day=sample(1:365,30,rep=TRUE)
  four[t]=(max(apply((abs(outer(day,day,"-"))<4),1,sum))>4)}
mean(four)

and found 0.0278, which means the above approximation is far from terrible! I think it may actually be “exact” in the sense that observing exactly four murders within four days of one another is given by this probability. The cases of five, six, &tc. murders are omitted but they are also highly negligible. And from this number, we can see that there is a 18% probability that the case of the four occurs within seven years. Not so unlikely, then.

a talk with Jay

Posted in Books, Running, Statistics, Travel, University life with tags , , , , , , , , , , on November 1, 2013 by xi'an

IMG_1900I had a wonderful time in CMU, talking with a lot of faculty about their research (and mine), like reminiscing of things past and expanding on things to come with Larry (not to mention exchanging blogging impressions), giving my seminar talk, having a great risotto at Casbah, and a nice dinner at Legume, going for morning runs in the nearby park… One particularly memorable moment was the discussion I had with Jay as/since he went back to our diverging views about objective Bayes and improper priors, as expressed in the last chapter of his book and my review of it. While we kept disagreeing on their relevance and on whether or not they should be used, I had to concede that one primary reason for using reference priors is one of laziness in not seeking expert opinions. Even though there always is a limit to the information provided by such experts that means a default input at one level or the next (of a hierarchical model). Jay also told me of his proposal (as reported in his 1996 Bayesian methods and ethics in a clinical trial design book) for conducting clinical trials with several experts (with different priors) and sequentially weighting them by their predictive success. Proposal which made me think of a sequential way to compare models by their predictive abilities and still use improper priors…

colors of the Fall

Posted in pictures, Running, Travel with tags , , , on October 30, 2013 by xi'an

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