Posts from April 2018 (17)

April 10, 2018

NRL Predictions for Round 6

Team Ratings for Round 6

The basic method is described on my Department home page. Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

  Current Rating Rating at Season Start Difference
Storm 12.57 16.73 -4.20
Panthers 4.33 2.64 1.70
Dragons 4.23 -0.45 4.70
Sharks 1.74 2.20 -0.50
Roosters 1.39 0.13 1.30
Cowboys 0.80 2.97 -2.20
Raiders 0.43 3.50 -3.10
Broncos 0.31 4.78 -4.50
Wests Tigers -0.22 -3.63 3.40
Sea Eagles -0.36 -1.07 0.70
Warriors -1.32 -6.97 5.70
Rabbitohs -2.07 -3.90 1.80
Eels -4.24 1.51 -5.80
Bulldogs -4.48 -3.43 -1.10
Knights -7.65 -8.43 0.80
Titans -7.78 -8.91 1.10

Performance So Far

So far there have been 40 matches played, 21 of which were correctly predicted, a success rate of 52.5%.
Here are the predictions for last week’s games.

  Game Date Score Prediction Correct
1 Raiders vs. Bulldogs Apr 05 26 – 10 6.60 TRUE
2 Sharks vs. Roosters Apr 06 10 – 28 6.80 FALSE
3 Dragons vs. Rabbitohs Apr 06 16 – 12 10.20 TRUE
4 Wests Tigers vs. Storm Apr 07 11 – 10 -15.00 FALSE
5 Warriors vs. Cowboys Apr 07 22 – 12 1.10 TRUE
6 Knights vs. Broncos Apr 07 15 – 10 -6.60 FALSE
7 Titans vs. Sea Eagles Apr 08 32 – 20 -7.10 FALSE
8 Eels vs. Panthers Apr 08 6 – 12 -5.50 TRUE

Predictions for Round 6

Here are the predictions for Round 6. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

  Game Date Winner Prediction
1 Roosters vs. Rabbitohs Apr 12 Roosters 6.50
2 Storm vs. Knights Apr 13 Storm 23.20
3 Dragons vs. Sharks Apr 13 Dragons 5.50
4 Warriors vs. Broncos Apr 14 Warriors 2.90
5 Cowboys vs. Bulldogs Apr 14 Cowboys 8.30
6 Raiders vs. Eels Apr 14 Raiders 7.70
7 Panthers vs. Titans Apr 15 Panthers 15.10
8 Sea Eagles vs. Wests Tigers Apr 15 Sea Eagles 2.90

 

Algorithmic Impact Assessments

There’s a new report from New York University’s AI Now Institute, giving recommendations for algorithmic impact assessments (PDF). Worth reading for anyone who is or should be interested in criteria for automated decision systems. As the researchers say:

AIAs will not solve all of the problems that automated decision systems might raise, but they do provide an important mechanism to inform the public and to engage policymakers and researchers in productive conversation. With this in mind, AIAs are designed to achieve four key policy goals:

  1. Respect the public’s right to know which systems impact their lives by publicly listing and describing automated decision systems that signi cantly a ect individuals and communities;
  2. Increase public agencies’ internal expertise and capacity to evaluate the systems they build or procure, so they can anticipate issues that might raise concerns, such as disparate impacts or due process violations;
  3. Ensure greater accountability of automated decision systems by providing a meaningful and ongoing opportunity for external researchers to review, audit, and assess these systems using methods that allow them to identify and detect problems; and
  4. Ensure that the public has a meaningful opportunity to respond to and, if necessary, dispute the use of a given system or an agency’s approach to algorithmic accountability.

(via Harkanwal Singh)

The Immigration NZ model: recap

Original post

To begin with: Yes, everyone being evaluated was already eligible for deportation.

There were two main categories of feedback on this point: the ‘Manus Island’ tendency, arguing they’re all guilty and so it doesn’t matter how you treat them, and the people pointing out that a model could perhaps make better decisions that an individual immigration officer.  The first group have, I think, missed an important issue: the arguments given by Immigration NZ for this model being a good thing would apply anywhere else in the immigration system or the justice system where there is currently discretion — eg, police discretion to prosecute.

The second group do have a good point (which is why it’s a point I made in my original post), but only if the model is constructed well and, ideally, audited.  As I said, it didn’t look like we had that sort of model. Today, we got more information about the model, thanks to Radio NZ’s Morning Report. Here’s a PDF of the spreadsheet and the briefing document (dated April 6, so potentially cleaned up after the initial publicity).  It’s a spreadsheet, simply adding up points for a bunch of categories, with minimal scaling for importance based on Immigration NZ’s expert knowledge or fitting to empirical data.

It’s not especially surprising that the harm model is a bit crap. What is surprising is that the Minister thinks this is a good thing

He said he was concerned about misconceptions around the pilot programme.

“Some people were talking about a sophisticated algorithm some people were talking about racial profiling, both of those are incorrect and I think it’s very important that the public know exactly what this is, and what it isn’t,” he said.

“This is not modelling or a predictive tool – this is a spreadsheet that they put some information into and they rank people based on that information.”

That’s not a defence; it’s an indictment.

April 9, 2018

Briefly

In a 2003 study, 19 percent of teens who claimed to be adopted actually weren’t, according to follow-up interviews with their parents. When you excluded these kids (who also gave extreme responses on other items), the study no longer found a significant difference between adopted children and those who weren’t on behaviors like drug use, drinking and skipping school

 

April 5, 2018

Immigration NZ and the harm model

Immigration NZ, by and large, has been good at transparency in the past– you may think some of their policies are inhumane or arbitrary, but you can easily find out what their policies are.  That’s a pleasant contrast to the other place I’ve lived as an immigrant. Even their operational manual is available online. So, when you hear in this morning’s Radio NZ story “Immigration NZ using data system to predict likely troublemakers”, you might want to give them the benefit of the doubt and assume they are just taking more steps to make their decision procedures explicit.

But then you get to the quotes

“We will model the data sets we have available to us and look at who or what’s the demographic here that we’re looking at around people who are likely to commit harm in the immigration system or to New Zealand,” he said.

“Things like who’s incurring all the hospital debt or the debt to this country in health care, they’re not entitled to free healthcare, they’re not paying for it.

“So then we might take that demographic and load that into our harm model and say even though person A is doing this is there any likelihood that someone else that is coming through the system is going to behave in the same way and then we’ll move to deport that person at the first available opportunity so they don’t have a chance to do that type of harm.

At the very least, they are saying that you can have two people with the same record of what they’ve done in New Zealand, in the same circumstances, and one of them will be deported and the other not deported based on, say, country of origin or age.  It’s true that to be deported you have to have done something that gives them a justification — but “at the first available opportunity” is fairly broad when you’re Immigration NZ. And if  they’re talking about people who are “not entitled to free health care”, then “immigrants” is the wrong term. [update: Radio NZ have now changed the first word of the story from “Immigrants” to “Overstayers”. Apart from that issue of terminology the same comments still apply]

So, how does this differ from, say, the IRD using statistical models to target people with higher probability of having committed tax fraud for auditing? There are two important differences in principle. The first is that the IRD is interested in auditing people who have already committed tax fraud, not people who might do so in the future. The second is that the consequences of being caught don’t depend on the predicted probability. Immigration NZ, on the other hand, seems to be interested in treating people differently based on things they haven’t done but might do in the future.

Now, Immigration NZ has to deport some people. It has to make decisions about who to let into the country in the first place, and who to give extensions of visas, or grant residency. That’s what it’s for. These decisions will have serious impacts on the lives of would-be immigrants — ranging from those who have an application for residency denied to those who don’t even bother applying because there’s no hope.

Since Immigration NZ does make these sorts of decisions, do we want them to do it based on a statistical model? That’s actually a serious question. It depends. There are at least three issues with the model: the ‘transparency‘ issue, the ‘audit‘ issue and the ‘allowable information‘ issue. All of these are also a problem with decisions made by humans.

The ‘allowable information‘ issue is ‘racial profiling’. As a society, we’ve decided that some information just should not be used to make certain types of decisions — regardless of whether it’s genuinely predictive. For anyone other than Immigration NZ, country of origin would be in that category. Invoking a statistical model — essentially, writing it down in a flowchart — wouldn’t be a justification. To some extent Immigration NZ is required to treat prospective immigrants differently based on their country of origin; the question is how far they can go. The Human Rights Commission is likely to have an opinion here, and it’s quite possible they’ll say Immigration NZ has gone too far.

The ‘transparency‘ issue is that the model should be public.  Voters should be able to find out their government’s policy on deportations; people trying to immigrate should know their chances. The tax office have an argument for keeping their model secret; they don’t want people to be able to tweak their accounts to escape detection. The immigration office don’t.

The ‘audit‘ issue is related but more complicated.  Immigration NZ need to know (and should have independent verification, and should tell us) how accurate the model is and what inputs it’s sensitive to, and how reliable the data are. How many of the deported people does the model say would have committed serious crimes? How much unnecessary government expenditure does it predict they will require? How well do these predictions match up to reality? Are there relevant groups of people for whom the model is importantly less accurate — people from particular countries, people with or without family in NZ, etc — so that the costs of automated decision making aren’t justified by benefits.  And to what extent do the inputs to the model suffer from self-reinforcing bias?

The classic problem of self-reinforcing bias comes from a different context, predictions of future offences by convicted criminals. We don’t have data on who commits crimes, only on who is arrested, charged, or convicted.  To the extent that people from particular demographic groups are more likely to attract the notice of the justice system, it will look as if they are more likely to commit crime, and this will lead to more targeted enforcement. And so on, round and round.

In the immigration setting, we’d be concerned about any of the criteria that can be affected by current immigration enforcement practice — if people are currently more likely to be deported or more likely to have applications refused based subjectively on country of origin, this will tend to show up in the new models.  Healthcare costs, on the other hand, aren’t directly affected by Immigration NZ decisions and so don’t have the same self-reinforcing vicious circle — though failing to pay the bills might.

Having a statistical model isn’t necessarily a bad thing, just like having a formal flowchart or points system isn’t necessarily a bad thing.  The model can have various sorts of bias, but so can actual human immigration officers.  In contrast to some of the social policy models, this model isn’t being used to make new distinctions in a setting where everyone used to be treated uniformly — the immigration system has always made individual decisions about visas and deportations.

In principle, a model  could be developed with care to include only the right sorts of inputs, to predict outputs that aren’t subject to vicious circles,  to have clear and reliably estimated costs and benefits associated with decisions, and to be open to independent audit. Such a model would be more accountable to the Minister, Parliament, and the nation than the decisions of individual immigration officers.

The fact that we, and the incoming Minister, only found out about the system this morning doesn’t suggest we’ve got that sort of model. Neither does the disappearance of data from their website, where they’ve just discovered privacy problems (without all that much effect, since the data are still up at archive.org). Nor the explicit use of country of origin. Nor the spokesperson’s complete lack of reference to safeguards in the modelling process, or the argument that they can’t be doing racial profiling because they also use gender, age and type of visa in the model.

April 3, 2018

Super 15 Predictions for Round 8

Team Ratings for Round 8

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

 

Current Rating Rating at Season Start Difference
Hurricanes 17.10 16.18 0.90
Crusaders 14.86 15.23 -0.40
Chiefs 10.23 9.29 0.90
Highlanders 10.17 10.29 -0.10
Lions 8.22 13.81 -5.60
Stormers 0.35 1.48 -1.10
Sharks 0.25 1.02 -0.80
Blues -1.24 -0.24 -1.00
Waratahs -1.85 -3.92 2.10
Brumbies -2.06 1.75 -3.80
Bulls -3.07 -4.79 1.70
Jaguares -4.16 -4.64 0.50
Reds -7.95 -9.47 1.50
Rebels -9.23 -14.96 5.70
Sunwolves -19.04 -18.42 -0.60

 

Performance So Far

So far there have been 42 matches played, 27 of which were correctly predicted, a success rate of 64.3%.
Here are the predictions for last week’s games.

 

Game Date Score Prediction Correct
1 Chiefs vs. Highlanders Mar 30 27 – 22 3.40 TRUE
2 Rebels vs. Hurricanes Mar 30 19 – 50 -21.10 TRUE
3 Blues vs. Sharks Mar 31 40 – 63 6.00 FALSE
4 Brumbies vs. Waratahs Mar 31 17 – 24 4.70 FALSE
5 Bulls vs. Stormers Mar 31 33 – 23 -1.30 FALSE
6 Lions vs. Crusaders Apr 01 8 – 14 -2.20 TRUE

 

Predictions for Round 8

Here are the predictions for Round 8. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

 

Game Date Winner Prediction
1 Hurricanes vs. Sharks Apr 06 Hurricanes 20.80
2 Sunwolves vs. Waratahs Apr 07 Waratahs -13.20
3 Chiefs vs. Blues Apr 07 Chiefs 15.00
4 Brumbies vs. Reds Apr 07 Brumbies 9.40
5 Lions vs. Stormers Apr 07 Lions 11.40
6 Jaguares vs. Crusaders Apr 07 Crusaders -15.00

 

NRL Predictions for Round 5

Team Ratings for Round 5

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

 

Current Rating Rating at Season Start Difference
Storm 13.70 16.73 -3.00
Dragons 4.66 -0.45 5.10
Panthers 4.29 2.64 1.70
Sharks 3.48 2.20 1.30
Cowboys 1.42 2.97 -1.50
Broncos 1.12 4.78 -3.70
Sea Eagles 0.98 -1.07 2.00
Raiders -0.23 3.50 -3.70
Roosters -0.35 0.13 -0.50
Wests Tigers -1.35 -3.63 2.30
Warriors -1.94 -6.97 5.00
Rabbitohs -2.50 -3.90 1.40
Bulldogs -3.82 -3.43 -0.40
Eels -4.21 1.51 -5.70
Knights -8.46 -8.43 -0.00
Titans -9.12 -8.91 -0.20

 

Performance So Far

So far there have been 32 matches played, 17 of which were correctly predicted, a success rate of 53.1%.
Here are the predictions for last week’s games.

 

Game Date Score Prediction Correct
1 Cowboys vs. Panthers Mar 29 14 – 33 3.20 FALSE
2 Rabbitohs vs. Bulldogs Mar 30 20 – 16 4.40 TRUE
3 Sharks vs. Storm Mar 30 14 – 4 -10.00 FALSE
4 Roosters vs. Warriors Mar 31 6 – 30 11.00 FALSE
5 Sea Eagles vs. Raiders Mar 31 32 – 16 2.30 TRUE
6 Dragons vs. Knights Apr 01 30 – 12 15.80 TRUE
7 Broncos vs. Titans Apr 01 14 – 26 17.30 FALSE
8 Wests Tigers vs. Eels Apr 02 30 – 20 5.20 TRUE

 

Predictions for Round 5

Here are the predictions for Round 5. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

 

Game Date Winner Prediction
1 Raiders vs. Bulldogs Apr 05 Raiders 6.60
2 Sharks vs. Roosters Apr 06 Sharks 6.80
3 Dragons vs. Rabbitohs Apr 06 Dragons 10.20
4 Wests Tigers vs. Storm Apr 07 Storm -15.00
5 Warriors vs. Cowboys Apr 07 Warriors 1.10
6 Knights vs. Broncos Apr 07 Broncos -6.60
7 Titans vs. Sea Eagles Apr 08 Sea Eagles -7.10
8 Eels vs. Panthers Apr 08 Panthers -5.50