The Supreme Court appeared to close the door on considering any future cases of partisan gerrymandering with its ruling June 27 on cases from two states. Illinois political scientist Wendy K. Tam Cho has focused her research on addressing the excesses of political redistricting (or gerrymandering), working with research colleague Yan Liu on a system for drawing partisan-free maps using a supercomputer. She also provided expert testimony for a recent case in Ohio. She spoke with News Bureau social sciences editor Craig Chamberlain.
The court, with a 5-4 majority, ruled that partisan gerrymandering was “non-justiciable.” What does that mean?
It means that the court has declared partisan gerrymandering claims to be political questions that are only properly resolved by political bodies. These types of claims are judged to be unresolvable according to legal principles and therefore beyond the reach of the federal courts.
The cases ruled on here came from Maryland and North Carolina. What might be the consequences for other states where lower courts have ruled against apparent gerrymanders and called for redrawing district maps?
Cases that have been brought in federal courts will now be dismissed for lack of jurisdiction. That applies to current cases on appeal from Ohio, Wisconsin and Michigan. The ruling does not affect cases that are brought in state courts, where the ruling is based on state law and state constitutions.
Political parties, throughout U.S. history, have sought partisan advantage through redistricting. In fact, the term “gerrymandering” was coined in the early 1800s. Why is it such a concern now?
Last year, Justice Kagan summarized the concern going forward when she wrote that the 2010 redistricting cycle “produced some of the worst partisan gerrymanders on record,” predicting “the technology will only get better, so the 2020 cycle will only get worse.” Over the last couple of decades, our ability to collect and analyze data has improved dramatically, leading to software that enables map drawers to synthesize many information sources to meticulously construct electoral maps to fulfill particular goals.
The hope was that the court would provide a check on the legislature when it overstepped and abused its power by constructing maps for nefarious purposes. Now that the court has dismissed itself from this role for partisan gerrymandering cases, the fear is that legislatures will wield unfettered power.
What has been the legal sticking point for the court? And do you think it’s justified?
The sticking point has been whether there is a judicially manageable standard for determining whether a map is a partisan gerrymander. In essence, the court was looking for a way to objectively understand the characteristics of a “fair map” that it could use to adjudicate the constitutionality of any electoral map. The court needs to be able to make a reasoned legal judgment that is devoid of any political bias. Efforts to convince the court that such a standard exists fell one justice short.
The standard that I and my research colleague have been advocating is based in the same technological advances of the last couple of decades. We have worked hard to develop statistical models and algorithmic advances on the world’s most powerful computing environments to enable courts to make reasoned and objective judgments.
So what do you see as the path forward?
Now that we know the Supreme Court will not intervene after a map is drawn, our focus must switch to preventing gerrymanders in the first place. I have argued that the means of such prevention lies not with the courts but in technological advances. Previously, technology for redistricting has led only to the exclusion and isolation of power. Moving forward, we must harness the power of technology to ensure democracy. The promise of technology is to augment human capabilities to engage in productive, inclusive and contemplative decision-making about how society is governed.
Because our collective voice is composed of the individual voices of many distinct and diverse groups, political fairness is a complex phenomenon. It requires compromise and the balancing of competing interests so that members of all groups are represented. Currently, all groups are not equally able to garner the legal and political expertise to translate their goals into actual electoral maps, making their voices easily muted.
This is where intelligent computational algorithms can play a part. I have been working to create advances that amalgamate these wide and varied interests to identify electoral maps that are acceptable to a broad segment of society.
Lastly, I will note that all the work that has been done is still highly relevant in state courts. The Supreme Court closed one avenue, but many others remain. All has not been lost.