TauJud: test augmentation of machine learning in judicial documents

is not a single piece of hardware or software, but rather, a constellation of technologies that gives a computer system the ability to solve problems and to perform tasks that would otherwise require human intelligence." 1 Artificial Intelligence-AI-is an ever more pervasive part of our lives. AI is embedded in shopping algorithms, navigational aids, and search engines, and, as we now know, it is used for public health contact tracing. Studies show that certain AI applications identify tumors with greater accuracy than medical personnel. Algorithms drive social media-and, increasingly, cars. It seems Generation Z has come of age knowing nothing but algorithms. 2 Just as AI is transforming the economy, health care, and American society, it will also transform the practice of government and law. Law firms use AI platforms to conduct discovery. At least seventy-five countries use facial recognition for domestic security and law enforcement purposes. 3 AI is used to determine travel patterns, to link suspects with crime scenes, and to populate watch lists. Between 2011 and 2019, the FBI used its facial recognition algorithm to search federal and state databases, including some state driver's license databases, over 390,000 times. 4 The National Security Commission on Artificial Intelligence (NSCAI) has predicted that "[t]he development of AI will shape the future of power." 5 13. NSCAI, supra note 1, at 8. 14. James e. Baker, The Centaur's Dilemma: National Security Law for the Coming AI Revolution 34 (Brookings Institution Press, 2020). AI computations and algorithms are also used to spot finite changes in stock pricing and generate automatic sales and purchases of stock as well as spot anomalies that generate automatic sales and purchases. All of this is based on algorithms created and initiated by humans but programmed to act autonomously and automatically because the calculations are too large, the margins too small, and the speed too fast for humans to keep pace and make decisions in real time. Of course, as one trader's algorithm gets faster, the next trader must change either his algorithm's design, its speed, or both to achieve advantage, reducing the window of opportunity for real-time human control even further. AI machine learning and pattern recognition are also used for translation, logistics planning, and spam detection, among many, many more commercial applications. In 2017, the former Chief Scientist for Baidu, Andrew Ng declared AI "the new electricity." 16 Perhaps the most prominent illustration of next-generation AI is the driverless vehicle. AI empowers driverless cars by performing myriad data input and output tasks simultaneously, as a driver does, but in a different way. Human drivers rely on intuition, instinct, experience, and rules to drive-seemingly all at once-using the neural networks of the brain. In driverless cars, sensors instantaneously feed computers data based on speed, conditions, and images of the sort ordinarily processed by the driver's eyes and brain. The car's software processes the data to determine the best outcome based on probabilities and based on what it has been programmed to understand and decide. This requires constant algorithmic calculations that a human actor could not make in real time. Luckily, humans do not rely on math to drive cars. They exercise their judgment and intuition, which is why (if they're alert) they generally handle situational change better than AI applications do. On the other hand, AI does not fall asleep at the wheel, text while driving, or drive drunk. Perhaps the most successful application of AI to date is found in the field of medical diagnostics. Here, narrow AI's capacity to detect and match patterns and find anomalies has led to breakthroughs in the detection of tumors as well as the onset of diabetic retinopathy. In places like India, where there is a shortage of ophthalmologists, the use of such screening diagnostics can help prioritize access to doctors and treatment by identifying at-risk patients. 17 Studies indicate that AI is generally more accurate than humans in detecting cancerous tumors. However, that is not the same as saying that humans are prepared to rely on AI alone, or wish to receive medical diagnoses from machines rather than doctors.