Judging the Judges

Business AI seeks out various correlations and makes predictions, which is having effects on China's medical world. Similar principles are now being applied to China’s legal system, another sprawling bureaucracy with highly uneven levels of expertise across regions.

The company iFlyTek has taken the lead in applying AI to the courtroom, building tools and executing a Shanghai-based pilot program that uses data from past cases to advise judges on both evidence and sentencing. An evidence cross-reference system uses speech recognition and natural-language processing to compare all evidence presented— testimony, documents, and background material—and seek out contradictory fact patterns. It then alerts the judge to these disputes, allowing for further investigation and clarification by court officers.

Once a ruling is handed down, the judge can turn to another AI tool for advice on sentencing. The sentencing assistant intakes the fact pattern—defendant’s criminal record, age, damages incurred, and so on—then its algorithms scan millions of court records for similar cases. It uses that body of knowledge to make recommendations for both jail time and fines paid. Judges can also view similar cases as data points scattered across an X–Y graph, clicking on each dot for details on the fact pattern that led to the sentence. It’s a process that builds consistency in a system with over 100,000 judges, and it can also rein in outliers whose sentencing patterns put them far outside the mainstream. One Chinese province is even using AI to rate and rank all prosecutors on their performance. Some American courts have implemented similar algorithms to advise on the “risk” level of prisoners up for parole, though the role and lack of transparency in these AI tools has already been challenged in higher courts.

As with RXThinking’s “navigation system” for doctors, all of iFlyTek’s judicial tools are just that: tools that aid a real human in making informed decisions. By empowering judges with data- driven recommendations, they can help balance the scales of justice and correct for the biases present in even well-trained judges. American legal scholars have illustrated vast disparities in U.S. sentencing based on the race of the victim and the defendant. And judicial biases can be far less malicious than racism: a study of Israeli judges found them far more severe in their decisions before lunch and more lenient in granting parole after having a good meal.

Who Leads?

So which country will lead in the broader category of business AI? Today, the United States enjoys a commanding lead (90–10) in this wave, but I believe in five years China will close that gap somewhat (70–30), and the Chinese government has a better shot at putting the power of business AI to good use. The United States has a clear advantage in the most immediate and profitable implementations of the technology: optimizations within banking, insurance, or any industry with lots of structured data that can be mined for better decision-making. Its companies have the raw material and corporate willpower to apply business AI to the problem of maximizing their bottom line.

There’s no question that China will lag in the corporate world, but it may lead in public services and industries with the potential to leapfrog outdated systems. The country’s immature financial system and imbalanced healthcare system give it strong incentives to rethink how services like consumer credit and medical care are distributed. Business AI will turn those weaknesses into strengths as it reimagines these industries from the ground up.

These applications of second-wave AI have immediate, real-world impacts, but the algorithms themselves are still trafficking purely in digital information mediated by humans. Third-wave AI changes all of this by giving AI two of humans’ most valuable information- gathering tools: eyes and ears.

Posted by Dr. Kai-Fu Lee on Feb 05, 2019 in All Posts In the Media

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