Court Leader’s Advantage Podcast: June 2019 Episode

You may not be aware, but artificial intelligence (AI) has already established itself in our daily lives.  From Amazon to Alexa, sophisticated algorithms affect much of what we do.  The next ten years will see advancements in electronic decision-making, facial recognition, language translation, and voice-to-text.

Are you willing to accept the cost in loss of privacy due to AI’s insatiable thirst for data for the benefit in added productivity?  What will be the new careers in AI world?

  Abhijeet Chavan and IV Ashton walk us through some of the inner workings of AI, some expectations in areas like Natural Language Processing, and give us advice on how to prepare for the future of this technology.

This is a fascinating episode for listeners interested in court technology, Natural Language Processing, algorithms, individual privacy, language translation, and emerging technologies.

Listen to the Podcast


Leave a comment or question about the podcast at


About the Guests Speakers

Abhijeet Chavan

Senior Executive Advisor, Tyler Technologies

Mr. Chavan has over 20 years of technology consulting experience with public sector, higher education, and non-profit clients. He was named to the Fastcase 50 list of global legal innovators in 2017. He regularly presents at conferences on access to justice and artificial intelligence.  Mr. Chavan sits on committees of the State Bar of California, American Bar Association, and National Association for Court Management. He is a co-author of NACM’s 2019 Plain Language Guide. Previously, Mr. Chavan served as chief technology officer of a consulting firm; created legal tools WriteClearly, ReadClearly, and DLAW; co-founded a media business; and managed geographic data projects.  Mr. Chavan has graduate degrees from the University of Illinois at Urbana-Champaign.

IV Ashton

President & Founder, LegalServer/

IV Ashton has spent his 20-year legal career leveraging technology to increase access to justice for vulnerable populations throughout the World.  Mr. Ashton began his legal career working internationally, applying technology to promote the rule of law. He oversaw the development of the Kosovo War Crimes Database (documenting human rights violations in Kosovo), as well as several online tools for various Ministries of Justice throughout the Balkans, to help strengthen emerging legal systems.  In 1999 Mr. Ashton co-founded Illinois Legal Aid Online (ILAO), a leading website in the US that provides important legal resources to pro se litigants, pro bono attorneys, and legal aid attorneys. In 2001, he founded LegalServer, a web-based case management platform used by hundreds of nonprofit agencies and public defenders. LegalServer has the singular purpose of helping those who cannot afford attorneys by leveraging technology to overcome the complexities and inefficiencies of our legal system.  Most recently, Mr. Ashton created a web-based platform applying machine learning to automate and streamline legal processes for vulnerable populations throughout the United States.


The following is an excerpt from the book, Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb

April 17, 2018 Apple Books

From Cheap to Strategy

The single most common question corporate executives ask us is: “How will AI affect our business strategy?” We use a thought experiment to answer that question. Most people are familiar with shopping at Amazon. As with most online retailers, you visit its website, shop for items, place them in your cart, pay for them, and then Amazon ships them to you. Right now, Amazon’s business model is shopping-then-shipping.

During the shopping process, Amazon’s AI offers suggestions of items that it predicts you will want to buy. The AI does a reasonable job. However, it is far from perfect. In our case, the AI accurately predicts what we want to buy about 5 percent of the time. We actually purchase about one of every twenty items it recommends. Considering the millions of items on offer, that’s not bad!

Imagine that the Amazon AI collects more information about us and uses that data to improve its predictions, an improvement akin to turning up the volume knob on a speaker dial. But rather than volume, it’s turning up the AI’s prediction accuracy.

At some point, as it turns the knob, the AI’s prediction accuracy crosses a threshold, changing Amazon’s business model. The prediction becomes sufficiently accurate that it becomes more profitable for Amazon to ship you the goods that it predicts you will want rather than wait for you to order them.

At some point, as it turns the knob, the AI’s prediction accuracy crosses a threshold, changing Amazon’s business model. The prediction becomes sufficiently accurate that it becomes more profitable for Amazon to ship you the goods that it predicts you will want rather than wait for you to order them.

With that, you won’t need to go to other retailers, and the fact that the item is there may well nudge you to buy more. Amazon gains a higher share of wallet. Clearly, this is great for Amazon, but it is also great for you. Amazon ships before you shop, which, if all goes well, saves you the task of shopping entirely. Cranking up the prediction dial changes Amazon’s business model from shopping-then-shipping to shipping-then-shopping.”

Of course, shoppers would not want to deal with the hassle of returning all the items they don’t want. So, Amazon would invest in infrastructure for the product returns, perhaps a fleet of delivery-style trucks that do pickups once a week, conveniently collecting items that customers don’t want.8

If this is a better business model, then why hasn’t Amazon done it already? Because if implemented today, the cost of collecting and handling returned items would outweigh the increase in revenue from a greater share of wallet. For example, today we would return 95 percent of the items it ships to us. That is annoying for us and costly for Amazon. The prediction isn’t good enough for Amazon to adopt the new model.

We can imagine a scenario where Amazon adopts the new strategy even before the prediction accuracy is good enough to make it profitable because the company anticipates that at some point it will be profitable. By launching sooner, Amazon’s AI will get more data sooner and improve faster. Amazon realizes that the sooner it starts, the harder it will be for competitors to catch up. Better predictions will attract more shoppers, more shoppers will generate more data to train the AI, more data will lead to better predictions, and so on, creating a virtuous cycle. Adopting too early could be costly, but adopting too late could be fatal.9

Our point is not that Amazon will or should do this, although skeptical readers may be surprised to learn that Amazon obtained a US patent for “anticipatory shipping” in 2013.10 Instead, the salient insight is that turning the prediction dial has a significant impact on strategy. In this example, it shifts Amazon’s business model from shopping-then-shipping to shipping-then-shopping, generates the incentive to vertically integrate into operating a service for product returns (including a fleet of trucks), and accelerates the timing of investment. All this is due simply to turning up the dial on the prediction machine.

Do You Want to Know More?

Practical Legal Services Applications of Artificial Intelligence,

Legal Services Corporation ITC Conference, Jan 2019, New Orleans, Louisiana

By Abhijeet Chavan, IV Ashton, and Justin Brownstone, Slides (PDF), Video

Due Process and Ethics in the Age of Tech: Innovations In the Justice System

Equal Justice Conference, American Bar Association,  May 2018, San Diego, CA

By Abhijeet Chavan, Angela Tripp, and Jonathan Pyle, (Slides), (Doc)    

Understanding the Role of Artificial Intelligence/Machine Learning in Delivering Legal Services

Legal Services Corporation TIG Conference, Jan 2017, San Antonio, TX

By Abhijeet Chavan, IV Ashton, and Jonathan Pyle, (Slides)

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