Table of Contents
Artificial Intelligence Translates the Language of Animals
Kim Kyung-jin, Attorney at Law
The Story of AI Learning to Listen to Whales, Dolphins, Birds, and Bees
Twelve chapters on how AI listens to dolphins, sperm whales, humpback whales, birds, and bees to find rules in their sounds, and what this technology means for its risks and for animal rights. Written in simple sentences a child can read, with verified sources in every section.
Table of Contents
AI Deciphers Ancient Scripts
Kim Kyung-jin, Attorney at Law
Ancient Records Revived by AI
In twelve chapters, this book explains how AI revives records once unreadable, from burned scrolls and wooden slips buried in mud to broken clay tablets. It covers virtual unrolling at Herculaneum, virtual collation of oracle-bone texts, reading Silla wooden tablets, computational analysis of undeciphered scripts, and multispectral archives, with verified references for each chapter.
Table of Contents
Artificial Intelligence for New Materials Design and Rocket Propulsion Engineering
Kim Kyung-jin, Attorney at Law
AI Potentials, Self-Driving Laboratories, and Physics-Informed Machine Learning (PIML)
Ten chapters on how artificial intelligence is changing new materials and rocket propulsion: atomic simulation, generative models, self-driving labs, high-temperature alloys, metal 3D printing, combustion, cooling design, and engine diagnosis and control. Written without equations, with verified sources in every chapter.
AI Library
The Age of Autonomous Scientific Discovery
Kim Kyung-jin, Attorney at Law
AI Scientists and Self-Driving Labs
This book follows how AI scientists and self-driving labs are changing the way science generates and verifies claims. It covers literature-based discovery, natural-language protocols translated into robot commands, multi-agent research systems, closed-loop laboratories, materials search, the verification gap, chains of evidence, research harnesses, journal ethics, and legal responsibility.
AI Library
A New Era of Life Sciences Opened by Artificial Intelligence
Structural Proteomics, Genomic Foundation Models, Autonomous Laboratories, and Global Governance
Kim Kyung-jin, Attorney at Law
This book is a research volume compiled with artificial intelligence. A human selected the materials and structured the work, while AI models drafted the sentences and cross-checked the facts.
AI Library
The Double Structure of Digital Sovereignty
Europe’s Departure from Palantir and the Chains of American Big Tech
Kim Kyung-jin, Attorney at Law
This is a record of 2026, when European intelligence agencies and defense ministries began removing analytics tools from America’s Palantir. It covers the replacement decisions made by France’s General Directorate for Internal Security (DGSI), Germany’s Federal Office for the Protection of the Constitution (BfV), and the Netherlands Ministry of Defense; the incident in which US export controls severed an ally’s ac…
New English Edition
Artificial Intelligence in Horticulture
Kim Kyung-jin, Attorney at Law
Across five chapters and ten sections, this book examines computer vision for crop diagnosis, harvesting robots and autonomous field systems, smart greenhouses and digital twins, precision irrigation and supply-chain quality control, high-throughput phenotyping, and predictive breeding.
New English Edition
Artificial Intelligence in Food Crop Agriculture
Kim Kyung-jin, Attorney at Law
Across six chapters and eighteen sections, the book examines digital agricultural infrastructure, remote sensing, crop diagnosis, yield forecasting, precision irrigation, genomics, molecular breeding, agricultural robotics, climate-smart agriculture, and global food security.
New English Edition
The Future of Forestry and Agroforestry
Kim Kyung-jin, Attorney at Law
Driven by Artificial Intelligence and Digital Innovation
Across five chapters and fifteen sections, the book follows satellites, drones, LiDAR, digital twins, forest-specific language models, wildfire and pest forecasting, forestry robotics, agroforestry, timber traceability, and forest carbon markets.
New English Edition
Smart Livestock Farming: AI Enters the Barn
Kim Kyung-jin, Attorney at Law
Sensors listen, cameras watch, and artificial intelligence helps farmers decide.
Across five chapters and fifteen sections, the book follows precision livestock farming from animal health and reproduction to robotic milking, virtual fencing, digital twins, methane reduction, welfare, and data ownership.
Table of Contents
Han Dong-hoon, Busan Buk-gu Gap: A Record of the 100 Days Before and After the Election (Mar. 26-Jul. 3, 2026)
Kim Kyung-jin
Table of Contents and 13 sections
From March 26 to July 3, 2026, this record follows the spring after expulsion, the Busan Buk-gu Gap by-election, victory as an independent, and the first bill submitted in the National Assembly.
Table of Contents
Artificial Intelligence and Medicine
Kim Kyung-jin, Attorney at Law
AI in clinical care, hospitals, education, and research
AI in medical imaging, risk prediction, treatment planning, hospital operations, education, and research, with patient safety, privacy, and accountability.
[AI Library] Chapter 15. Explainable AI (XAI)
Chapter 15. Explainable AI (XAI)
Explainable AI (XAI) July 3, 1988, Persian Gulf. The tragedy began in the Combat Information Center (CIC) of the U.S. Navy's Aegis cruiser USS Vincennes. An unidentified track appeared on the radar screen. The captain and crew were nervous. This is because military tensions with Iran were extremely high at the time. The computer system classified the track as an Iranian Air Force F-14 fighter jet. The captain ordered the missile launch. But it wasn't an F-14. Iran Air Flight 655 was a commercial airliner. All 290 passengers and crew died.
Later investigation revealed that the computer system displayed incorrect information and, in a state of extreme tension, the crew believed it. Why did this tragedy happen? The system at the time just said, “That’s an F-14.” They did not explain why they decided it was the F-14, how confident they were, or whether there were any other possibilities. The crew had no way to verify what the machine was saying. This is the ‘black box’ problem. A black box is a black box that cannot be seen inside. When you put in an input, you get an output, but you don't know what happens in between.
The same goes for modern deep learning artificial intelligence. Millions of parameters are complexly intertwined, and even developers are often unable to explain exactly why a conclusion was reached. For combat pilots, trust means life. If my wingman suddenly breaks out of formation and makes a sharp turn, I will grab the radio and yell. “Why are you doing this!” At that time, if the wingman responds, “We have confirmed a SAM (Surface-to-Air Missile) launch at 3 o’clock! We are evading!”, I immediately understand and I also enter evasive maneuvers. Because I explained why. But what if the wingman is an artificial intelligence drone?
The guy suddenly rushes into the middle of the enemy lines. I don't see anything on my radar. I don't know if this is a genius tactic, a system error, or if I've been hacked. At that moment I panic. Incomprehensible behavior is indistinguishable from treason. In 2020, in DARPA's Alpha Dog Fight Trial, Heron Systems' artificial intelligence 'Falco' completely defeated the human pilot 'Banger' 5 to 0. The results were shocking. But it was also strange. Falco performed maneuvers that no human pilot would ever perform. He used the 'head-on' tactic of charging head-on toward the enemy plane and firing the machine cannon dozens of times per second.
I shook the control stick slightly. Banger said after the fight: “That guy’s shooting skills are unbelievably accurate, but you can’t predict why he’s doing that maneuver or what he’s going to do next.” In a simulation, even if the artificial intelligence does something strange and crashes, you can just press the reset button. But on the actual battlefield? What if an artificial intelligence drone flying next to me suddenly launches a missile toward a civilian village? How do you know if it's hitting an enemy's camouflage point or if it's malfunctioning? XAI (eXplainable AI, explainable artificial intelligence) emerged to solve this problem.
This is a technology that explains “why” artificial intelligence made that decision in words that humans can understand. DARPA has had an XAI program in operation since 2016. Their goals are clear. “Create explainable models while maintaining high performance, and enable humans to understand, appropriately trust, and effectively manage AI partners.” Easier said than done, but difficult to realize. If the existing AI simply said, “This is an enemy tank. 97% probability,” XAI would say: “This is a T-90 tank. The basis for judgment is as follows. First, the shape of the turret is 95% consistent with the T-90’s database.
Second, the engine heat distribution captured by the infrared sensor shows the characteristics of a diesel engine. Third, the formation of the escort vehicles deployed around matches the Russian armored forces doctrine.” Only with this explanation can pilots verify the artificial intelligence's judgment. If the AI identifies a target for the wrong reason (e.g. because of the shape of a tree's shadow), the pilot may cancel the attack, saying "That stupid machine was wrong again." In April 2025, the U.S. Air Force released 'Doctrinal Document on Artificial Intelligence (AFDN 25-1)'.
The document states that military AI development should use “transparent and explainable algorithms” and conduct “regular audits and evaluations.” Military applications can only be built if data is accessible and understandable. The concept of trust calibration is also important. This means that you should not trust artificial intelligence too much or too little. If you believe too much, the Vincennes tragedy will repeat itself. If you don't trust it too much, you can turn off or ignore artificial intelligence and not use it. Artificial intelligence must be honest about its level of certainty.
The human response should be different when you say, “This target has a 99% chance of being an enemy,” and when you say, “This target has a 60% chance of being an enemy. Identification is uncertain.”
French defense company Thales has launched the slogan ‘TrUE AI’. The goal is to create artificial intelligence that is transparent, understandable, and ethical. By making the decision-making process of artificial intelligence traceable, they are developing technology that can backtrack the thinking process of artificial intelligence, like analyzing an airplane's black box when an accident occurs. The MQ-28 Ghost Bat unmanned aerial vehicle, which the Australian Air Force is developing with Boeing, also focuses on this problem.
When a manned fighter pilot entrusts a mission to his wingman, the Ghost Bat, the pilot must be able to see at a glance the status of the unmanned aircraft and whether the commands have been properly understood. We need an interface that turns complex data into intuitive pictures. In May 2024, U.S. Air Force Secretary Frank Kendall personally boarded the X-62A VISTA fighter jet piloted by artificial intelligence. Secretary Kendall was in the backseat while the plane performed dogfighting maneuvers at speeds exceeding 550 miles per hour (approximately 880 kilometers per hour).
After the flight, he said, “I think we can leave the decision to fire weapons to an artificial intelligence.” But this is a trust that only became possible after thousands of hours of simulations and testing, and after artificial intelligence was able to explain why it makes the maneuvers it does. XAI also has limitations. Additional computations are required to generate the description. When fighters have to make millisecond decisions, they can't afford to waste time creating explanations. Also, explanations that are too complicated will confuse the pilot.
“The curvature of the turret shape is 0.73 and the standard deviation of the heat distribution is 2.4” makes sense only to engineers. The pilot needs to say "That's a tank, shoot!" There is also the threat of adversarial attacks. What if the enemy knows the weaknesses of our artificial intelligence? For example, if we notice that our AI is highly dependent on the turret shape, we can cover the tank with camouflage netting to distort the turret outline. You can also fool artificial intelligence by adding subtle patterns to images that are invisible to the human eye.
If XAI discloses the basis for artificial intelligence's judgments, it could mean informing the enemy of our weaknesses. Ethical issues also remain. Just because artificial intelligence provides explanations does not mean it is exempt from liability. If the artificial intelligence explains that “the target’s heat signature pattern matches 94% of an enemy fighter jet,” but in reality it was a civilian airliner, who is responsible? Are you a programmer who wrote code? Was it the pilot who pressed the fire button? Or is it the commander who decided to deploy artificial intelligence?
Both the International Committee of the Red Cross (ICRC) and the U.S. Department of Defense identify explainability as a key element in the development and use of autonomous weapons systems. The principle of “Meaningful Human Control” is emphasized. But will humans have time to intervene in the split-second of hypersonic missiles and hundreds of drones? Probably not.
At that time, we may have to delegate authority to artificial intelligence, saying, “You take care of it.” So XAI is not just about answering real-time questions like “Explain why now?” Before deploying artificial intelligence, we test it in thousands of scenarios and verify in advance how it makes decisions in certain situations. The goal is to enable artificial intelligence to honestly say, “This situation is beyond the scope of what I have learned. You be the judge, human.” As a fighter pilot, I love machines.
When the F-16's engines roar behind me and the fly-by-wire system accurately translates the slightest manipulation at my fingertips, I feel one with the machine. But that's because I understand the machine. Even if the engine sound is slightly strange, you will notice it. If the control's response is different from usual, you will feel it immediately. The same should be true for artificial intelligence. When we understand the thoughts of artificial intelligence and when it can explain its thoughts to us, we become a true 'team'.
At that time, I will be willing to let go of the control stick and take command while looking at the tactical screen displayed by artificial intelligence. Intelligence without explanation is indistinguishable from madness on the battlefield. You need a comrade in arms who can have your back, not a ghost in a black box. XAI is our effort to create those comrades.
Part 4. The Era of Loyal Wingmen
Kim Kyung-jin
Attorney · Former Member of the National Assembly · AI Policy Researcher
© 2026 Kim Kyung-jin. All rights reserved.