# Machine learning `kaal:entity:machine-learning` **Status.** derived This node is assembled mechanically from the 10 claims that carry the concept tag `machine-learning`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself. ## Every claim under this term 10 claims across 5 works, 2017 to 2025. **2017** - [2939127-023](https://wulfkaal.github.io/claims/2939127-023) [mechanism/argued] -- Leveraging the big data collected through Legal Tech solutions and blockchain applications in combination with machine learning produces more creative and faster tools, which in turn generates a surge of innovative platforms that disrupt the legal industry. > Leveraging the big data that is collected by using Legal Tech solutions and blockchain applications in combination with machine learning creates more creative and faster tools. Mark Fenwick, Wulf A. Kaal, Erik P. M. Vermeulen, Legal Education in the Blockchain Revolution (2017). SSRN: https://ssrn.com/abstract=2939127 - [2998033-021](https://wulfkaal.github.io/claims/2998033-021) [failure/argued] *(failure mode)* -- Repeated use of the same dataset by data scientists creates an overfitting risk: the training model fits the test set so closely that its performance on a different dataset degrades. > When data scientists use the same data set repetitively a risk exists that the training model will overfit the test set of data which can limit the performance of the applied model on a different dataset. Wulf A. Kaal, Blockchain Innovation for Private Investment Funds (2017). SSRN: https://ssrn.com/abstract=2998033 **2019** - [3409548-003](https://wulfkaal.github.io/claims/3409548-003) [mechanism/asserted] -- Current hedge fund trading technology is hard coded by humans, whereas deep learning systems can be given a simple command and derive a result from data on their own; this is the operative difference between legacy quantitative tools and machine learning. > The current technology used in hedge fund trading is hard coded by a human. In contrast, a deep learning AI machine learning algorithm can be given a simple command and automatically find a result based on data. Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548 - [3409548-005](https://wulfkaal.github.io/claims/3409548-005) [failure/evidenced] *(failure mode)* -- Systematic, computer model driven funds do not reliably outperform human managed funds: research finds the typical systematic fund does not always perform as well as funds run by human managers. > However, according to research done by Preqin, the typical systematic fund doesn't always perform as well as funds operated by human managers. Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548 - [3409548-007](https://wulfkaal.github.io/claims/3409548-007) [mechanism/asserted] -- Machine learning improves portfolio diversification by searching for instruments that are uncorrelated with each other and that still match the requirements of the target risk profile. > ML can help in accurate portfolio diversification by looking for uncorrelated instruments that match requirements of the risk profile. Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548 - [3409548-009](https://wulfkaal.github.io/claims/3409548-009) [failure/argued] *(failure mode)* -- Repeated use of the same data set by data scientists creates an overfitting risk: the training model overfits the test set, which limits the performance of the applied model on a different dataset. > When data scientists use the same data set repetitively a risk exists that the training model will overfit the test set of data, which can limit the performance of the applied model on a different dataset. Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548 - [3409548-025](https://wulfkaal.github.io/claims/3409548-025) [mechanism/asserted] -- Machine learning applied to execution algorithms lets large orders be split into thousands of smaller transactions without moving the market, with the algorithm adjusting its aggressiveness to market conditions. > ML can be applied in execution algorithms that help execute large orders by dividing them into thousands of smaller transactions without influencing the market, while adjusting their aggressiveness to the market situation. Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548 - [3409548-026](https://wulfkaal.github.io/claims/3409548-026) [empirical/evidenced] -- Artificial intelligence and machine learning are used far more heavily for idea generation and portfolio optimization than for execution: two thirds of surveyed funds use them to generate trading ideas and optimize portfolios, while only just over a quarter use automation to execute trades. > Two-thirds of respondents use AI/ML to generate trading ideas and optimize portfolios.101 Just over a quarter of respondents use automation to execute trades. Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548 **2025** - [5245185-028](https://wulfkaal.github.io/claims/5245185-028) [failure/evidenced] *(failure mode)* -- Anomaly detection and behavioral analysis models trained on agent data may replicate the biases in that data and fail to detect novel deviations absent from the training set. > rely on machine learning models trained on agent data, which may replicate biases or fail to detect novel deviations not present in training sets. Wulf A. Kaal, How can we Best Monitor AI Agents (2025). SSRN: https://ssrn.com/abstract=5245185 - [5541658-001](https://wulfkaal.github.io/claims/5541658-001) [mechanism/argued] -- The limitations of rule-based legal expert systems drove the field toward case-based reasoning in the 1990s and machine learning in the early 2000s, because data-driven approaches allow legal AI to operate without relying solely on predefined rules. > The limitations of rule-based systems prompted a shift toward case-based reasoning in the 1990s and subsequently machine learning (ML) in the early 2000s. These approaches, more particularly ML, allowed AI to rely on data rather than rely solely on predefined rules, Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658 ## Verify Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust. curl -s https://wulfkaal.github.io/entities/machine-learning.md | sha256sum **Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.