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> | From Secrecy to Data Judicial Commons | | | -- By JoseMariaDelajara - 05 Oct 2019 | | | Support for open government is growing. However, the progress in opening judicial data sets has been slower than that of the legislative and executive branches. The main fear of judicial officers seems to be the perception that new technologies (i.e. data analytics) could have adverse effects on the employee's job. In other words, judges seem to be worried that data analytics will show their decisions as they really are: irrational yet predictable. When we face the complete picture of our flawed justice system, will we still accept it? | |
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< | Open justice
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> | Open justice = access to justice | | | Open data in the judiciary resides on three principles: (1) transparency, (2) seeking citizen participation (3) and institutional collaboration. According to Elena, an open justice system should at least publish (1) court rulings, (2) statistics regarding the performance of courts and (3) budget allocation information. Also, that data should be both legally and technically open. Citizens would need to be allowed to freely access, reuse and distribute the data, which should be made available in a machine-readable format and in bulk. | |
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< | Open justice + legal analytics = more access to justice
Open justice would have beneficial effects both on the public and the private sector. The key promise is that as the data sets keeps growing, data analytics will be able to depict in more precise terms the decision patterns from legal actors. This could help bridge the access to justice gap by providing guidance to citizens without a lawyer. Also, judicial analytics could level the playing field between big law and public attorneys by providing a basis of the quality of legal claims and evidence. Finally, it could also help judges learn from their mistakes, and provide assistance towards better decision-making. However, the vast majority of legal data is currently unavailable for analytics. It could be either that it is not shared, or that is not machine-readable. But the main point is that the control of legal data still remains in the hands of a few private entities such as big law firms and companies like Lexis Nexis.
Limits for legal analytics
Open data might give the idea that everyone will have a crystal ball through legal analytics, and thus would be able to predict the outcome of every single dispute. That is not the case. A decision pattern does not necessarily mean the judge will behave the same way every time. It could be just an influence of an unexpected personal event. Also, judicial analytics depends on the details of the data of the specific court. For example, if the record shows that a court has favoured plaintiffs, it will likely attract meritless cases. At a superficial level, this will hinder the prediction power of the data of that court. | >
> | Open justice would have beneficial effects both on the public and the private sector. The key promise is that as the data sets keeps growing, data analytics will be able to depict in more precise terms the decision patterns from legal actors. This could help bridge the access to justice gap by providing guidance to citizens without a lawyer. Also, judicial analytics could level the playing field between big law and public attorneys by providing a basis of the quality of legal claims and evidence. Finally, it could also help judges learn from their mistakes, and provide assistance towards better decision-making. However, the vast majority of legal data is currently unavailable for analytics. It could be either that it is not shared, or that is not machine-readable. But the main point is that the control of legal data still remains in the hands of a few private entities such as big law firms and companies like Lexis Nexis. This violates citizen's access to justice, now including open data as a human right. | | |
Will we like our reflection in the mirror? | |
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< | The English excuses | >
> | The main fear of judicial officials opposing open justice is that the data will be run through machine learning software and reflect their actual decision patterns. They are right in the sense that justice is fallible. For example, Chen found out that perceived masculinity of the voice of the attorney predicts court outcomes (i.e. males are more likely to win then they are perceived as less masculine). Emotions can also influence legal decision making. For example, a meta-study analyzing 23 experiments with over 4500 participants determined that gruesome evidence led to harsher sentences in 95% of the cases. Also, judges were found to be influenced by irrelevant sentencing demands, even when the demand was a product of them throwing dice. That's not all. Judges have been found to be influenced also by extraneous factors such as unexpected outcomes of football games in the same week of the decision or the last time they took a food break.
This realistic reflection of human decision-making has generated some backlash. Recently, France banned the publication of statistical information about judges’ decisions. Anyone who breaks the law could be punished with a sentence of up to five years in prison. On its article 33, the new French Justice Reform Act states that “the identity data of the magistrates and the members of the judiciary may not be reused with the purpose or effect of evaluating, analyzing, comparing or predicting their actual or presumed professional practices”. Hence, the French Government did not intend to stop publishing the data; just to punish its comprehension through data analytics. | | | | |
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< | The French #JudicialBan | >
> | The reaction against legal analytics relies on a wrong view of data. Most people think that it serves a crystal ball, enabling them to predict the outcome of every single dispute. That is not the case. For one, legal analytics is data-hungry, so it needs enough volume, variety, velocity and veracity (known as the four V’s) Even so, a decision pattern does not necessarily mean the judge will behave the same way every time. It could be just an influence of an unexpected personal event. Also, judicial analytics depends on the details of the data of the specific court. For example, if the record shows that a court has favoured plaintiffs, it will likely attract meritless cases. At a superficial level, this will hinder the prediction power of the data of that court (i.e. the numbers will revert to the mean by the generated as a reaction to legal analytics). | | | | |
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< | Recently, France banned the publication of statistical information about judges’ decisions. Anyone who breaks the law could be punished with a sentence of up to five years in prison. On its article 33, the new French Justice Reform Act states that “_the identity data of the magistrates and the members of the judiciary may not be reused with the purpose or effect of evaluating, analyzing, comparing or predicting their actual or presumed professional practices_”. Hence, the French Government did not intend to stop publishing the data; just to punish its comprehension through data analytics. | >
> | Judicial analytics does not predict the future. Instead, its output is a probability based on past decision patterns. Hence, lawyers must not forget they are still human, and that they suffer from probably-neglect bias. Most importantly, judges need to be reminded that legal analytics provides a spectral opportunity of identifying unknown patterns, even to them, and learning from past mistakes. It is a means towards making legal decision less intuitive. | | | | |
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< | The way forward | >
> | The way forward: data commons | | | | |
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> | As we’ve seen, legal analytics with enough data could show a picture of biased law. This could be fixed by curating the data. A data commons is a good place to do that. A "data commons" refers to knowledge being freely shared, collectively owned and managed by a community using software to manage the input, harmonize it and analyze it. In this regard, the Linux Foundation has developed two types of community data license agreement licenses to allow users to access data (one requires that the changes to data are shared, while the other doesn’t). The technical and legal requirements for data judicial commons to work are set. We can learn from the free software movement and raise awareness of the opportunities that open justice could create for government, business and citizens alike. | | |
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