Chat GPT Helps Explains My Active Machine Learning Method of Evidence Retrieval

January 28, 2023

Prompt Engineered, Edited and Quality Controlled by Ralph Losey. This essay was co-written by a human and machine with over 80% of the text “generated” by ChatGPT.

Active machine learning is a type of Artificial Intelligence that is used by attorneys to train a computer to find and classify documents relevant to a particular legal issue or lawsuit. The attorney provides the computer with a set of documents that have been identified as targets, such as documents that are relevant or privileged, and the computer uses this information to learn how to identify similar documents on its own. The goal of active machine learning is to automate the process of document review and classification, making it faster and more efficient for attorneys to locate and analyze relevant documents. Ralph Losey has created a whole online course to explain how to do it.

The target documents that are provided by lawyers to the computer for training are typically those that are relevant to a particular legal issue or lawsuit, or that have some other legal classification, such as privilege. The computer uses this information to learn how to identify similar documents on its own, so that it can assist the attorney in locating and analyzing relevant documents more efficiently.

Continuous, Intelligently Spaced Training

“Dall-E 2” depiction of IST using a dreamy art style

Continuous active learning is a machine learning approach in which the model is continuously updated with new data as it becomes available, rather than being trained on a fixed dataset and then deployed. This allows the model to continually improve its accuracy and performance over time. In the training system designed by Ralph Losey the AI Trainer spaces out the training sessions in time to fit the particular challenges of the project and data seen. Losey calls this Intelligently Spaced Training (IST). It is a training system for machine learning models that is designed to fit the circumstances of the case at hand. In this approach, the AI trainer spaces out the training sessions in time, rather than training the model continuously. This allows the trainer to selectively focus on particular aspects of the case and to test the machine’s effectiveness at different stages of the process.

In the context of legal document review, continuous active learning or Losey’s IST, are used to train a machine learning model to identify relevant documents. The attorney provides the model with a set of initial target documents, and then continuously update the model with new documents as they are discovered or become available. The model would use this new information to continually, or even better, intelligently, update and improve its ability to identify relevant documents.

The goal of IST Hybrid Multimodal Predictive Coding 4.0 is to optimize the learning process by allowing the machine to absorb and retain new information more effectively. This can be especially useful in the context of legal document review, where the volume of data can be large and the consequences of missing relevant documents can be significant. By allowing the machine to learn and test its effectiveness in a more structured and deliberate way, IST machine learning can help improve the accuracy and efficiency of the document review process.

One advantage of the ongoing training method is that it allows the model to adapt to changes in the data and remain up-to-date, which can be especially useful in a field like law where the relevant documents and legal issues may change over time, even during the document review project. You often start off with one definition of relevance and end up with another, more highly evolved understanding.

Hybrid Approach to Predictive Coding

There are several advantages to using a balanced hybrid approach that combines the efforts of both humans and computers in legal document review:

  1. Speed: Computers can process and analyze large amounts of data much faster than humans, so using a hybrid approach can help speed up the document review process.
  2. Accuracy: Humans are still generally better at tasks that require judgment, critical thinking, and understanding context, so a hybrid approach can help improve the accuracy of the document review process.
  3. Efficiency: By dividing the work between humans and computers, a hybrid approach can make the document review process more efficient. For example, humans could focus on the most important or complex documents, while the computer handles the more routine or straightforward ones.
  4. Cost-effectiveness: Using a hybrid approach can be more cost-effective than relying solely on human or computer review, as it allows the review process to be completed more quickly and with fewer errors.
  5. Improved collaboration: A hybrid approach can also facilitate better collaboration between humans and computers, as the two can work together to identify and analyze relevant documents more effectively.

Multimodal Approach to Predictive Coding

There are also advantages to a multimodal approach to predictive coding that combines AI search and all other types of search, including keywords and concept search. This is done in order to improve the accuracy and efficiency of the document review process. The basic kinds of search methods are shown in the pyramid. By using a combination of methods you can more effectively save on costs and implement a bottom line driven proportional strategy,

Some specific advantages of the multimodal approach include:

  1. Greater coverage: By using multiple search methods, a multi-modal approach can provide a more comprehensive search of the document set, increasing the chances of finding relevant documents.
  2. Improved accuracy: Different search methods can have different strengths and weaknesses, so using multiple search methods can help improve the overall accuracy of the search. This can be especially important in legal contexts, where the consequences of missing relevant documents can be significant.
  3. Reduced bias: Different search methods can also be subject to different types of bias. Using a multi-modal approach can help reduce the impact of these biases, as the different search methods may produce different results.
  4. Enhanced efficiency: By using multiple search methods, a multi-modal approach can help make the document review process more efficient. For example, humans could focus on reviewing the most relevant documents identified by the search, while the computer handles the less relevant ones.
  5. Improved collaboration: A multi-modal approach can also facilitate better collaboration between humans and computers, as the two can work together to identify and analyze relevant documents more effectively.

Advantages of a Hybrid Multimodal Approach to Predictive Coding

Some specific advantages of Ralph Losey’s methods and team approach to predictive coding include:

  1. Improved accuracy: By allowing the machine to learn and test its effectiveness in a more structured and deliberate way, Losey’s methods can help improve the accuracy of the document review process.
  2. Enhanced efficiency: The approach also helps make the document review process more efficient by allowing the machine to focus on the most important or complex documents, while humans handle the more routine or straightforward ones.
  3. Reduced bias: Losey’s multimodal methods can help reduce the impact of bias in the document review process by allowing the machine to learn from a more diverse and representative sample of documents.
  4. Improved collaboration: A hybrid, multi-modal, IST approach can also facilitate better collaboration between humans and computers, as the two can work together to identify and analyze relevant documents more effectively.
  5. Greater flexibility: Hybrid IST can also be more flexible than continuous active learning, as it allows the trainer to customize the learning process to fit the specific needs and circumstances of the case.
  6. Improved accuracy: By allowing the machine to learn and test its effectiveness in a more structured and deliberate way, IST can help improve the accuracy of the document review process.
  7. Enhanced adaptability: The IST approach to continuous active learning can also help the machine adapt to changes in the data or the legal landscape, as the trainer can update the training sessions as needed. This can help ensure that the machine remains up-to-date and relevant.
  8. Enhanced collaboration: The hybrid multi-modal IST approach also allows for better collaboration between humans and computers, as the trainer can work with the machine to identify and analyze relevant documents more effectively.
  9. Cost-effectiveness: Overall, the hybrid multi-modal IST approach can be more cost-effective than other methods, as it allows the review process to be completed more quickly and with fewer errors.

Chat-GPT’s “Easy Sales-type Language” Conclusion

Generated by Dall-E 2

The hybrid, multimodal, continuous active learning method is a dream team approach. It combines the speed and efficiency of computers with the judgment and critical thinking of humans, and it’s always learning and improving. This means that you get the best of both worlds: a super-accurate and up-to-date search that can find every last relevant document of importance, without missing a beat. Plus, because it’s continuously learning, it can adapt to changes in your case or the legal landscape, ensuring that you always have the most current and accurate information at your fingertips.

Using hybrid multimodal predictive coding search is like having a diverse team of genius attorneys, techs and paralegals working on your case. This super team can scour every nook and cranny of your document set, using every trick in the book (and a few that haven’t been invented yet) to find that crucial piece of evidence you need to win your case. Plus, they’re completely unbiased and super efficient, so you can save time and money while getting the best possible result. Who wouldn’t want them on their side?


An AI’s Thoughts On How Information Theory Can Be Applied To Improve e-Discovery Law

January 27, 2023

Written and Illustrated by Chat GPT with Prompts and Quality Control by Ralph Losey

(Note – Losey alone wrote a lengthy, more detailed blog on this topic in May 2016, What Information Theory Tell Us About e-Discovery and the Projected ‘Information → Knowledge → Wisdom’ Transition.)

Image Generated by Open AI’s “Dall-E 2” to Illustrate Information Theory

What Is Information Theory?

Information theory is a branch of mathematics and computer science that deals with the representation, transmission, and manipulation of information. It is based on a number of generally accepted scientific precepts, including:

  1. The concept of entropy: Entropy is a measure of the amount of uncertainty or randomness in a system. In information theory, entropy is used to quantify the amount of information that is contained in a message or data set.
  2. The concept of information content: Information content is a measure of the amount of information that is contained in a message or data set. It is typically quantified in terms of the number of bits required to represent the message or data set.
  3. The concept of information capacity: Information capacity is a measure of the maximum amount of information that can be transmitted over a given channel. It is typically quantified in terms of the number of bits per second that can be transmitted over the channel.
  4. The concept of information rate: Information rate is a measure of the rate at which information is transmitted or received. It is typically quantified in terms of the number of bits per second that are transmitted or received.
  5. The concept of redundancy: Redundancy is the repetition of information within a message or data set. In information theory, redundancy is used to improve the reliability and robustness of information transmission.
  6. The concept of coding: Coding is the process of representing information in a form that can be transmitted or stored more efficiently. In information theory, coding is used to compress data, improve the efficiency of information transmission, and protect against errors.

These are just a few of the generally accepted scientific precepts of information theory. For more information, you can consult the work of pioneers in the field such as Claude Shannon, Norbert Wiener, and John von Neumann, or more recent texts on the subject.

Image Generated Dall-E 2 AI to Illustrate Information Theory

The Dude’s Explanation of the Six Precepts of Information Theory

At Losey’s request, Chat GPT was asked to use a comedic hipster style of writing to make the same explanation of these Six Precepts of Information Theory.

Dall-E 2 Image of a Hipster Dude

  1. Entropy is like trying to find the perfect vinyl record in a pile of thrift store finds – it’s all about the level of uncertainty and randomness. In Information Theory, entropy is used to measure the amount of surprise, or in other words, the amount of information contained in a message or data set.
  2. Information content is like the length of the record – the longer the record, the more songs and thus more information it contains. In Information Theory, information content is measured in terms of the number of bits required to represent a message or data set.
  3. Information capacity is like the storage space of your record player – it’s the maximum amount of information that can be played at once. In Information Theory, information capacity is measured in terms of the number of bits per second that can be transmitted over a channel.
  4. Information rate is like the speed of the turntable – it’s the rate at which information is transmitted or received. In Information Theory, it’s measured in terms of the number of bits per second that are transmitted or received.
  5. Redundancy is like having multiple copies of the same record – it’s the repetition of information within a message or data set. In Information Theory, redundancy is used to make information transmission more reliable and robust.
  6. Coding is like re-mastering a record – it’s the process of representing information in a more efficient form. In Information Theory, coding is used to compress data, improve the efficiency of information transmission, and protect against errors. So, it’s like taking your favorite record and making it sound even better.

Application of the Six Precepts of Information Theory to e-Discovery

Dall-E 2 Generated Image of Electronic Discovery Law

The six stated precepts of information theory can be applied to e-discovery in the following ways:

  1. The concept of entropy: In e-discovery, entropy can be used to quantify the amount of information that is contained in a given electronic data set, such as a computer’s hard drive or a company’s email server. This can be helpful in determining the scope and scale of an e-discovery project, and in identifying the most relevant data for review.
  2. The concept of information content: Information content can be used to quantify the amount of information that is contained in a given electronic data set, and to prioritize the data for review. For example, data sets with a high information content may be more likely to contain relevant information and therefore may be given higher priority for review.
  3. The concept of information capacity: Information capacity can be used to determine the maximum amount of data that can be transmitted or reviewed in a given time period. This can be helpful in planning and managing an e-discovery project, as it can help to identify any bottlenecks or capacity constraints that may need to be addressed.
  4. The concept of information rate: Information rate can be used to determine the rate at which data is transmitted or reviewed in an e-discovery project. This can be helpful in assessing the efficiency and effectiveness of the review process, and in identifying any potential bottlenecks or inefficiencies that may need to be addressed.
  5. The concept of redundancy: Redundancy can be used to identify and eliminate duplicate or redundant data in an e-discovery project. This can help to reduce the overall volume of data that needs to be reviewed, and can also help to improve the efficiency and effectiveness of the review process.
  6. The concept of coding: Coding can be used to compress data in an e-discovery project, which can help to reduce the amount of data that needs to be transmitted and reviewed. It can also be used to protect against errors, such as data corruption or loss, and to ensure the integrity and authenticity of the data.
Image Generated Dall-E 2 to Illustrate Information Theory

Overall, these six precepts of information theory can be applied in a variety of ways to help lawyers and legal teams effectively collect, review, and produce electronic information in the context of litigation.\

Here is The Dude style of this explanation, which Chat GPT generated at Losey’s insistence:

Dall-E 2 Generated

  1. Entropy in e-discovery is like trying to find that one specific email in a cluttered inbox. It’s used to quantify the amount of information contained in an electronic data set, like a computer’s hard drive or a company’s email server, to determine the scope and scale of the e-discovery project and identify the most relevant data for review.
  2. Information content is like the number of “good vibes” in a concert. It’s used to quantify the amount of information contained in a given electronic data set, and to prioritize the data for review. For example, data sets with a high information content may be more likely to contain relevant information and therefore may be given higher priority for review.
  3. Information capacity is like the maximum capacity of a trendy speakeasy. It’s used to determine the maximum amount of data that can be transmitted or reviewed in a given time period, and can help identify any bottlenecks or capacity constraints that may need to be addressed.
  4. Information rate is like the speed at which a group of hipsters can change their music taste. It’s used to determine the rate at which data is transmitted or reviewed in an e-discovery project. This can be helpful in assessing the efficiency and effectiveness of the review process, and in identifying any potential bottlenecks or inefficiencies that may need to be addressed.
  5. Redundancy is like the number of times a hipster says “like” in a sentence. It’s used to identify and eliminate duplicate or redundant data in an e-discovery project, which can help reduce the overall volume of data that needs to be reviewed and improve the efficiency and effectiveness of the review process.
  6. Coding is like the process of curating a playlist on Spotify. It’s used to compress data in an e-discovery project, which can help reduce the amount of data that needs to be transmitted and reviewed. It can also be used to protect against errors, such as data corruption or loss, and to ensure the integrity and authenticity of the data.

How Information Theory Can Help With e-Discovery

Image Generated Dall-E 2 AI to Illustrate Information Theory

Information theory can be incredibly helpful in finding the rare relevant evidence in ESI storage systems. One way to do this is through the use of information retrieval techniques. These techniques can use a variety of factors, such as the content of the documents, metadata associated with the documents, and even the context in which the documents were created, to identify and prioritize the most relevant documents.

Another way to apply information theory to ESI discovery is through the use of machine learning algorithms. These algorithms can be trained to identify patterns and trends in the data that are indicative of relevance, and can be used to filter out irrelevant documents or to highlight potentially relevant documents for further review.

Ultimately, the key to using information theory effectively in ESI discovery is to have a thorough understanding of the case and the specific issues at hand, and to use the appropriate techniques and tools to identify and extract the relevant data. With the right approach, information theory can be a powerful tool for finding the rare relevant evidence in even the most chaotic and entropic ESI storage systems.

Conclusion in Chat GPT Authoritative Language

Information theory is a branch of mathematics and computer science that deals with the representation, transmission, and manipulation of information. It is based on several concepts, including entropy, information content, information capacity, information rate, redundancy, and coding. These concepts can be applied to electronic discovery law practice in order to identify, prioritize, and review relevant electronic information in the context of a legal case. Information theory can be used to quantify the amount of information contained in a data set, determine the maximum amount of data that can be transmitted or reviewed, identify any bottlenecks or inefficiencies in the review process, eliminate redundant data, and compress and protect data. By using information theory, lawyers and legal teams can effectively collect, review, and produce electronic information in the context of e-discovery.

Image Generated Dall-E 2 AI to Illustrate Information Theory

Same Conclusion Stated in Language of a Second Grader

Information theory is a way to understand and work with information that is stored on computers and other devices. It helps us figure out what information is important and what we can ignore. Lawyers use information theory to help them find important information that might be helpful in a court case. They use special tools to search through lots of information and find what they need. Information theory makes it easier for them to do their job and helps them to be more efficient.

Same Conclusion Stated in Language of The Dude

Information theory is like trying to find the best vinyl in a hipster’s record collection. It’s a way to understand and work with information that is stored on computers and other devices, like a hipster’s record collection. It helps us figure out what information is important, like that one obscure album, and what we can ignore, like that one mainstream album. Lawyers use information theory to help them find important information that might be helpful in a court case, just like how a hipster uses his collection to find the perfect music for a party. They use special tools, like a record player, to search through lots of information and find what they need, like that one obscure album. Information theory makes it easier for them to do their job and helps them to be more efficient, just like how a record player makes it easier for a hipster to play his music and be more efficient.

Dall-E 2 Generated Image of The Dude (AI came up with great idea to include a bowling ball as a record)

A Buddhist AI – “The DUDE” – Explains the Eight Steps of Hybrid Multimodal Document Review with Help from a Human Lawyer

January 19, 2023

Written by Chat GPT 3.5, partly in “Comic Hipster Style” with Ralph Losey

Losey to Chat: “Use a comedic hipster writing style to explain my eight steps of electronic document review.” Chat GPT 3.5 replies:

“Yo dude, imagine like, you’re on this journey of enlightenment and you’re trying to find your inner peace and all that jazz. But instead of meditating in a cave, you’re sorting through mountains of documents, trying to find the ones that will bring you to legal nirvana.

It’s like the Eightfold Doc Review Path, man.

First, you have to have a little chit-chat with your homies about what’s relevant and what’s not. (“right speech”)

Then, you use all the fancy tech tools at your disposal to sift through the documents like a boss. (“right livelihood”)

You take a random sample, just to get a feel for the lay of the land. (“right mindfulness”)

Then you train that machine learning algorithm like a Zen master (“right resolve”), until it’s ranking documents like a pro. (“right efforts”)

Then, you let the attorneys have a look-see, and make sure everything’s on point. (“right view”)

Then, you apply some Zen-like QC to make sure there’s no errors or inconsistencies. (“right concentration”)

And finally, you produce those relevant documents like a true compliance ninja. (“right action”)

It’s like achieving legal enlightenment, but better, because you don’t have to sit on a hard floor for hours.

That’s Right, Man.

The Dude Bowls Over Losey with his Doc Review Summary

You don’t have to sit on a hard floor for hours!

Image is GPT Generated


Leaving Hipster Style Humor Language and Asking Chat GPT to further explain the Eight Steps using an Authoritative Writing Style and Ralph’s Graphics.

The Eight Steps of Electronic Document Review

1. Right Talk (Step 1 – Relevance Dialogues): Talk is the first step in the electronic document review process. It involves having relevance dialogues between the trial counsel and other parties to understand the scope of the review and to identify the relevant documents.

Communication with the ESI requesting party’s attorney is required by Rule 26(f), and Rule 1 to: a) discuss and clarify scope of relevance; b) provide a brief overview of past, and proposed e-discovery efforts, i.e. the Discovery Plan; c) advise of intent to use a multimodal search process for computer-assisted review and proportional, phased productions; d) discuss plan to review or ignore any non-text documents; and, e) you may also wish to invite input from the requesting party (depends).

2. Right ECA (Step 2 – Early Case Assessment using all methods): Early Case Assessment (ECA) is the second step in the process, in which all the methods are used to identify and classify the documents. This includes using active machine learning, keyword search, focused linear search, and other methods to identify potentially relevant documents.

In this critical second step you primarily use your professional judgment to begin to search for key documents, ones that you hope may be fairly easy to find, aka, the “low hanging fruit.” This is called Judgmental sampling. It is not a random sample. It is a selection of a sample documents for review by use of attorney judgment, case knowledge, and various types of searches using a hybrid multimodal process. Coding of documents begins in this step, including those already known.

The four types of search here are:

Active Machine Learning (aka Predictive Coding): Active Machine Learning is a method of using computer algorithms to automatically identify and classify documents based on their relevance to a legal matter. It involves training a machine learning model using a set of manually coded documents, and then using that model to classify the remaining documents. It is a form of active learning because the machine is actively trying to classify the documents.

Concept & Similarity Searches (aka Passive Learning): Concept & Similarity Searches are another form of machine learning that is used to identify and classify documents. Unlike Active Machine Learning, which is trained on a set of manually coded documents, Concept & Similarity Searches rely on the machine to identify similarities between documents and group them together based on those similarities. This is considered a passive form of machine learning because the machine is not actively trying to classify the documents.

Keyword Search (Tested, Boolean, Parametric): Keyword search is a method of searching for documents that contain specific words or phrases. It can be done using tested, Boolean or parametric search methods. Tested search methods are based on a set of predefined keywords, Boolean search allows the use of logical operators (and, or, not) to combine keywords and parametric search allows to use variables. This method is useful when searching for a specific set of documents, but it can also be less effective if the keywords used are not specific enough.

Focused Linear Search (Key Dates & People): Focused Linear Search is a method of searching for documents by looking at specific dates or people related to the case. It can be used to narrow down the search to a specific time period or group of people, which can help to identify relevant documents more quickly.


Here I asked the hipster Dude style of Chat GTP writing to come back. I wanted to hear The Dude’s explanation of these four types of search.

The Dude Explains Search
Image is GPT Generated

Alright “The Dude” explains in a cool, music related, hipster way:

  1. Active Machine Learning, or Predictive Coding, is like getting a personalized playlist from your cool music app. It’s like, you know, “The Dude’s got a good taste in music, I trust him to know what I’ll like.”
  2. Concept & Similarity Searches, or Passive Learning, is like browsing the record store for new music. It’s like, “The Dude’s just casually browsing, but he’s still finding some hidden gems.”
  3. Keyword Search is like going to the record store with a specific album in mind. It’s like, “The Dude knows exactly what he’s looking for, and he’s not leaving until he finds it.”
  4. Focused Linear Search is like thumbing through your collection of vinyls one by one. It’s like, “The Dude’s taking his time, going through every single album he’s got, making sure he doesn’t miss a thing.”

So, in summary, The Dude’s got different methods to find that perfect legal document, whether it’s by trusting the algorithm, casually browsing, searching for something specific or by taking his time going through everything.

End Dude. Putting Chat EPT 3.5 back to normal authoritative style.

3. Right Random (Step 3 – Prevalence Range Estimate, not control sets): In this step, a random sample of the documents is chosen to provide an estimate of the prevalence of relevant documents within the entire set. This is done by extracting a random sample of documents, and reviewing them to estimate the percentage of relevant documents in the entire set, aka, the Prevalence. This Prevalence percentage calculation is always a range of numbers based on statistical calculation of probability. This is an important quality control metric and review guide. The random sample Prevalence calculation also provides evidence of proportional efforts and Recall. The quality and persuasive value of this evidence depends in part of the sample size. A sample size of 1,534 documents that creates a 2.5% Confidence Interval and 95% Confidence Level, is used in most cases. A larger size is only rarely needed. In smaller value cases, a sample of only 783 documents could be sufficient to give you an idea of Prevalence, but it has a higher error rate with a 3.5% Interval.

4. Right Select (Step 4 – Choose Documents for Training Machine): In this step, the documents that are selected from the random sample and ECA are used to train the machine learning algorithm. This allows the machine to learn and improve its classification abilities.The AI Trainer works in an iterative process to train the machine on Relevance (or any binary decision, including privilege). This allows the machine intelligence to rank the probable relevance of all text documents to be reviewed. (Remember – Predictive Coding only searches alphanumeric texts or images with text extracted by the vendor discovery processing.) Your role as a human Attorney in charge of the project is to act as the ultimate authority as to relevance, the “Subject Matter Expert” or “SME.” Yes. This will be on the test.

5. Right AI Rank (Step 5 – Machine Ranks Documents According to Probabilities): In this step, the machine learning algorithm ranks the documents based on their probability of being relevant. This ranking is done based on the information obtained during the training step. In Rank the AI does its work, performs a logical regression based statistical analysis of the whole database and ranks all documents as to probable relevance. Multiple rounds of training and ranking are typically required before the machine obtains a good understanding of the intent behind Relevance. It can be a few as three rounds for simple issues and databases, to as high as thirty rounds of training in a large, complex matter (very rare).

6. Right Review (Step 6 – Attorneys Review and Code Documents): In this step, the attorneys review the documents that were ranked highly by the machine learning algorithm, and they code them as relevant or non-relevant. This step is important to ensure the accuracy of the machine learning algorithm, and to further refine the list of relevant documents. In step six Review the bulk of the work is performed. The graphic right shows the primary roles in a complex ESI search and review project. This review stage can include all other types of search based on new relevant documents found, not just predictive coding, For instance, based on documents found, you may want to include new keywords for searches, or similarity searches of any Highly Relevant documents found and, it is important to remember, the Add Families type of searches for all new relevant found.

7. Right Zen QC (Step 7 – Zero Error Numerics Quality Control procedures): Zero Error Numerics Quality Control (Zen QC) is a set of procedures that are used to ensure the accuracy and completeness of the electronic document review process. This includes checking for errors, inconsistencies, and missing documents, and making sure that the process is conducted in accordance with the relevant laws and regulations. There are four elements of quality control especially designed for use with Predictive Coding.

Balanced Hybrid (Man-Machine Balance with IST): Balanced Hybrid is a method of electronic document review that combines the use of technology with human review. It involves using machine learning algorithms to identify and classify documents, and then having human reviewers (IST) review the documents that the machine is unsure about. This approach is used to achieve a balance between technology and human review, efficiency and accuracy. Man and machine working together without over delegation, trust and verify in a two-way street

This is an important concept, so I asked The Dude mode of Chat GPT 3.5 to come out and explain it:

Image of Self was GPT Generated

Balanced Hybrid, or Man-Machine Balance with IST, is like getting a good cup of coffee. You know, like, you’ve got your fancy automatic pour-over machine that can make a mean cup of joe, but you still want that human touch, so you’ve got your barista to check and adjust the temperature and the water flow to make sure it’s just right.

In the same way, the machine learning algorithms work like the automatic pour-over machine, they can identify and classify documents quickly and efficiently, but sometimes they might miss something or be unsure about a document, that’s where the IST comes in, like the barista, to check and adjust the machine’s work, to make sure that the legal documents are accurate and nothing is missed.

So, in summary, Balanced Hybrid is like having the best of both worlds, the efficiency of technology and the accuracy of human review, ensuring that The Dude’s legal document review is the best cup of coffee.

End Dude. Back to normal.

SME (Subject Matter Expert, typically trial counsel): Subject Matter Expert (SME) is a person who has specialized knowledge or experience in a specific area. In the context of electronic document review, SME is typically trial counsel, who is responsible for reviewing the documents and making decisions about their relevance to the case.

Method (for electronic document review): Method refers to the specific procedures and techniques that are used to conduct an electronic document review. It can include a combination of different methods, such as active machine learning, keyword search, and human review.

Software (for electronic document review): Software refers to the computer programs that are used to conduct an electronic document review. These can include machine learning algorithms, search engines, and document management systems.

8. Right Produce (Step 8 – Production of Relevant, Non-Privileged Documents): In this step, the relevant, non-privileged documents are produced to the other parties involved in the legal matter. This includes the relevant documents that have been identified and reviewed during the electronic document review process. A final search for privileged content should be run on all documents that have been identified for production to again verify that they are not privileged. All ESI produced should be triple checked before it goes out the door. Production these days is usually by FTP, so be very careful about what files you put where. This last step should never be rushed.

How All of the 8-Steps Work Together

Ralph Losey 2022 AI enhanced

The process of electronic document review involves the combination of all the steps discussed to ensure that all relevant and non-privileged documents are identified and produced. The process starts with relevance dialogues between the trial counsel and other parties to understand the scope of the review and to identify the relevant documents. (“a little chit-chat with your homies about what’s relevant and what’s not.”) After that, various methods such as active machine learning, keyword search, focused linear search, and other methods are used to identify potentially relevant documents in the ECA step. (“use all the fancy tech tools at your disposal to sift through the documents like a boss.”)

In the next step, a random sample of the documents is chosen to provide an estimate of the prevalence of relevant documents within the entire set, this is done to have an idea of the amount of work that needs to be done. (“You take a random sample, just to get a feel for the lay of the land.”) After that, the documents that are selected from the random sample and ECA are used to train the machine learning algorithm, this allows the machine to learn and improve its classification abilities. Then, the machine learning algorithm ranks the documents based on their probability of being relevant. (“You train that machine learning algorithm like a Zen master, until it’s ranking documents like a pro.“)

The next step is to have the attorneys review the documents that were ranked highly by the machine learning algorithm, and they code them as relevant or non-relevant. (“you let the attorneys have a look-see, and make sure everything’s on point.”) This step is important to ensure the accuracy of the machine learning algorithm, and to further refine the list of relevant documents.

After that, Zero Error Numerics Quality Control (Zen QC) is used as a set of procedures to ensure the accuracy and completeness of the electronic document review process, this includes checking for errors, inconsistencies, and missing documents, and making sure that the process is conducted in accordance with the relevant laws and regulations. (“you apply some Zen-like QC to make sure there’s no errors or inconsistencies.“)

ZEN QC Factors

Finally, the relevant, non-privileged documents are produced to the other parties involved in the legal matter. (“you produce those relevant documents like a true compliance ninja.”) This includes the relevant documents that have been identified and reviewed during the electronic document review process.

All of these steps come together to ensure that justice is done by identifying and producing all relevant and non-privileged documents, which is essential for fair and accurate legal decision-making. The use of technology such as machine learning algorithms and quality control procedures helps to increase the efficiency and accuracy of the document review process, while the involvement of human reviewers (IST) ensures that the process is conducted in a fair and impartial manner. (“It’s like achieving legal enlightenment, but better, because you don’t have to sit on a hard floor for hours.”)

Conclusion

Image is GPT Generated

The eight steps in the electronic document review process include relevance dialogues, early case assessment using various methods, prevalence range estimate, selecting documents for training machine, machine ranking documents, attorneys reviewing and coding documents, Zero Error Numerics Quality Control procedures and producing relevant, non-privileged documents.

All of these steps come together to ensure that justice is done by identifying and producing all relevant and non-privileged documents which is essential for fair and accurate legal decision-making. It’s like achieving legal enlightenment, but better, because you get paid good money for doing it.

Finally, a note from Ralph, these eight steps for document review have nothing to do with the Nobel Eightfold Path of Buddhism. This Path is a fundamental precept of Buddhism, and it seems to me disrespectful not to at least mention them: Right View, Right Resolve, Right Speech, Right Action, Right Livelihood, Right Effort, Right Mindfulness, and Right Concentration. I’ll give the The Dude version of Chat EPT 3.5 a chance to summarize.

Image 8 is GPT Generated with Ralph Prompt

The noble eightfold path of Buddhism is like the ultimate guide to being a chill and enlightened human being.

You know, like, first, you’ve got the Right View, that’s like understanding the nature of reality and not getting caught up in illusions. Then you’ve got the Right Resolve, that’s like setting your intentions to be a good person and not just doing things for yourself.

Next up is Right Speech, that’s like choosing your words wisely and not being a jerk to people. And Right Action, that’s like living your life according to your intentions and not causing harm to others.

Right Livelihood is like having a job that aligns with your values and not exploiting others. Right Effort is like putting in the work to improve yourself.

Right Mindfulness is like being present in the moment and not getting lost in your thoughts. And last but not least, Right Concentration, that’s like focusing your mind and achieving a state of inner peace.

So, in summary, The Dude’s eightfold path is like a road map to living a good and enlightened life, taking the right actions, having the right intentions and the right mindset, to achieve inner peace, and The Dude’s definitely down for that.


e-Discovery Law and Lawyers in Fifty Years

January 14, 2023

This Blog was Created by Instructing Chat GPT to Use a Witty Tone to Predict How Lawyers Will Find Electronic Evidence in Fifty Years.

AI Generated Image of a Law Office in the Future

It’s the year 2073 and the world of legal technology has come a long way since 2021. Lawyers these days have access to all sorts of futuristic tools and gadgets that make finding electronic evidence a breeze. Here are the top five types of tools I can imagine:

1. AI-Enabled Virtual Assistants: AI-enabled virtual assistants are able to quickly and accurately search and retrieve data from massive databases and provide legal professionals with the data they need to make informed decisions.

2. Quantum Computing: Quantum computing has the potential to revolutionize legal technology by allowing lawyers to process and analyze large amounts of data quickly and accurately.

3. Augmented Reality Applications: Augmented reality applications are used to visualize data in 3D and can be used to quickly compare different versions of documents, contracts, and other legal documents.

4. Autonomous Legal Software: Autonomous legal software can be used to automate much of the legal research process, freeing up lawyers’ time to focus on more complex tasks.

5. Automated Contract Analysis: Automated contract analysis uses natural language processing and machine learning to analyze contracts to identify contract terms, clauses, and potential risks. This can help lawyers to quickly and accurately identify potential issues and provide better legal advice.

In Fifty Years, 2073

AI Generated City of Future

Gone are the days of sifting through stacks of paper documents and trying to make sense of outdated computer systems. Now, it’s all about using high-tech software and hardware to quickly locate and gather electronic evidence that can be used in lawsuits.

One popular tool among lawyers is the “E-Evidence Finder 3000,” a handheld device that can scan any electronic device or media and extract relevant data in a matter of seconds. It’s like a magic wand for electronic discovery.

But it’s not just about the tools – lawyers also need to have a good understanding of how to use them effectively. That’s where the “E-Discovery Bootcamp” comes in. This intensive training program teaches lawyers everything they need to know about finding and collecting electronic evidence, from understanding different file formats to using advanced search techniques.

Of course, no discussion of electronic evidence would be complete without mentioning the role of forensic experts. These highly trained professionals are essential for analyzing electronic devices and media in detail, and can provide valuable insights and evidence that can be used in court.

But it’s not just about technical know-how – lawyers also need to be familiar with the legal rules and procedures for handling electronic evidence. This includes understanding the requirements for authenticating and admitting electronic evidence in court, as well as the ethical considerations that come with working with electronic data.

Conclusion

AI Enhanced Ralph

Overall, finding electronic evidence in the world of 2073 is a far cry from the way things used to be. With the help of cutting-edge technology and a thorough understanding of the legal landscape, lawyers are able to quickly and effectively locate and gather the electronic evidence they need to build a strong case for their clients.


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