[bvod]

This is not only much needed, but also will help shape how our legal system can adapt to new technologies.

I had the privilege of sitting in on an Election Law class last year at YLS. The topic was gerrymandering, with a discussion of the legal arguments presented in Vieth v. Jubelirer.

For non-lawyers, the plaintiffs arguments for what should constitute illegal gerrymandering is technically complex, using statistic concepts, graphs (computer science), and even np-completeness. In essence, the argument was to use computers to draw all possible congressional districts, score them on the basis of discarded votes, and if the scoring of the drawn districts is greater than two standard deviations from the mean district, determine it is unfairly drawn. I found particularly striking an audio recording the professor shared of a lawyer struggling to answer John Robert's questions on technical topics. The professor used this as an example to be prepared to answer questions that you may not have a background in, even if the expert witnesses had already explained the concepts. Unfortunately, the court rejected the proposed determination of unfair gerrymandering in a 5-4 decision, with the dissent stating that the presented way to determine unfair gerrymandering was clever, correct, and should be revisited.

As we continue to push the frontiers of what we can do with computers, we need informed lawyers who can clearly present deep technical topics, and we need judges who are capable of understanding them.

[abdullahkhalids]

Interesting. My country Pakistan, took a different approach in the last elections. An algorithm [1] was agreed upon, on how to draw constituency boundaries for each district. Further, there can ordinarily only be 10% variation between constituencies in a single district. The entire delimitations exercise was done in open, and there were multiple review steps.

I imagine the algorithm could be further improved, but it at least ensured some amount of certainty and transparency.

------ [1] As far as possible, the delimitation of constituencies of an Assembly shall start from the Northern end of the district1[**] and then proceed clock-wise in zigzag manner keeping in view that population among the constituencies of an Assembly shall remain as close as may be practicable to the quota: https://www.ecp.gov.pk/documents/laws2017/1-3-2020/The%20Ele...

[kemitchell]

I'd argue all of that diving into technical details didn't end up mattering that much, either in Vieth itself or especially after Rucho v. Common Cause.

[rarefied_tomato]

I can get behind statistical and CS concepts being used to detect gerrymandered districts. There's a whole related field of anomaly detection.

My quackery sense tingles when I hear NP-completeness was mentioned in the argument. Do you have more info on the claimed relevance to gerrymandering?

[bvod]

Of course, you can find the full explanation in the amicus brief: https://www.brennancenter.org/sites/default/files/legal-work...

It is NP complete in determining to an absolute degree that a redistricting plan is excessively unfair, as the number of possibly districts grows exponentially. Demonstrating to a quantitative degree is more clear (eg stop drawing more maps after a few billion).

I highly recommend anyone interested read at least the summary of the above brief, but relevant details from page 4 are reproduced:

"With modern computer technology, it is now straightforward to (i) generate a large collection of redistricting plans that are representative of all possible plans that meet the State’s declared goals (e.g., compactness and contiguity); (ii) calculate the partisan outcome that would occur under each such plan, based upon actual precinct-level votes in one or more recent elections; (iii) display the distribution of the outcomes across these plans; and (iv) situate the State’s chosen plan along that continuum to reveal the degree to which that plan is an outlier. One can analyze outcomes for a statewide plan as a whole, or for an individual district within a plan. In this way, it is now straightforward to measure the quantitative degree to which a partisan gerrymander is excessive."

[rarefied_tomato]

I'll check it out. Thanks!

Edit: I didn't find anything in that particular resource. A similar work mentioning complexity is here: https://desh2608.github.io/static/report/ohio.pdf

Roughly, it boils down to a constrained search for the best mapping of precincts into districts, which is NP-hard.

[petr_tik]

I tried to find the curriculum structure for this course to check if it involves any of the topics you brought up, but could not do it.

If this course doesn't include relevant legal topics, do you know any other "programming for lawyers" course that you would recommend?

[extra88]

Here is the OpenCourseWare site for the material [0]. It includes all the lecture videos, slides, and assignments plus notes, subtitles, and transcripts for each.

Based on the lecture titles, statistical concepts may be obliquely touched on but probably not graphs or np-completeness.

[0] https://cs50.harvard.edu/law/2019/

[rajacombinator]

Oh come on. That’s a bunch of buffoonery presented by an academic who wants to sound much smarter than they are, and has zero application to reality. Our legal system is about dividing up the pie amongst those who can pay for it.