{
 "@context": "https://schema.org",
 "@type": "DefinedTerm",
 "@id": "https://wulfkaal.github.io/entities/machine-learning",
 "identifier": "kaal:entity:machine-learning",
 "name": "Machine learning",
 "termCode": "machine-learning",
 "inDefinedTermSet": {
  "@id": "https://wulfkaal.github.io/entities/index.json"
 },
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0000-0003-0757-275X"
 },
 "dateModified": "2026-07-29",
 "canonicalForm": "https://wulfkaal.github.io/entities/machine-learning.md",
 "sha256": "876fbe2c97630ace232d67afc56036cc39e7f7b8179870523d6721c94cc0bf03",
 "additionalProperty": [
  {
   "@type": "PropertyValue",
   "name": "status",
   "value": "derived"
  },
  {
   "@type": "PropertyValue",
   "name": "claim_count",
   "value": 10
  },
  {
   "@type": "PropertyValue",
   "name": "work_count",
   "value": 5
  },
  {
   "@type": "PropertyValue",
   "name": "year_span",
   "value": [
    "2017",
    "2025"
   ]
  },
  {
   "@type": "PropertyValue",
   "name": "non_current_claims",
   "value": 0
  }
 ],
 "subjectOf": [
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/2939127-023",
   "identifier": "kaal:claim:2939127-023",
   "text": "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.",
   "abstract": "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.",
   "citation": "Mark Fenwick, Wulf A. Kaal, Erik P. M. Vermeulen, Legal Education in the Blockchain Revolution (2017). SSRN: https://ssrn.com/abstract=2939127",
   "datePublished": "2017",
   "claim_type": "mechanism",
   "confidence": "argued",
   "is_failure_mode": false,
   "scope_conditions": [
    "settings where Legal Tech and blockchain generate usable data at scale"
   ],
   "source_pdf_sha256": "abcef529c7200261c59eec43018375b3b26e3186239fb9d002bacc298dba26ff",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/2998033-021",
   "identifier": "kaal:claim:2998033-021",
   "text": "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.",
   "abstract": "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.",
   "citation": "Wulf A. Kaal, Blockchain Innovation for Private Investment Funds (2017). SSRN: https://ssrn.com/abstract=2998033",
   "datePublished": "2017",
   "claim_type": "failure",
   "confidence": "argued",
   "is_failure_mode": true,
   "scope_conditions": [
    "applies to adaptive data analysis where the same data set is used repetitively"
   ],
   "source_pdf_sha256": "aafb1be3c25cd33da477d759df9ca2f856f0a8fe133d6396da2e75d0af573dbd",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/3409548-003",
   "identifier": "kaal:claim:3409548-003",
   "text": "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.",
   "abstract": "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.",
   "citation": "Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548",
   "datePublished": "2019",
   "claim_type": "mechanism",
   "confidence": "asserted",
   "is_failure_mode": false,
   "scope_conditions": [],
   "source_pdf_sha256": "74227ab2656b06bfe4a29c942fc2ba26df9f476917e0a9f85ec038b8c3402c40",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/3409548-005",
   "identifier": "kaal:claim:3409548-005",
   "text": "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.",
   "abstract": "However, according to research done by Preqin, the typical systematic fund doesn't always perform as well as funds operated by human managers.",
   "citation": "Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548",
   "datePublished": "2019",
   "claim_type": "failure",
   "confidence": "evidenced",
   "is_failure_mode": true,
   "scope_conditions": [
    "typical systematic fund",
    "Preqin research as of 2016"
   ],
   "source_pdf_sha256": "74227ab2656b06bfe4a29c942fc2ba26df9f476917e0a9f85ec038b8c3402c40",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/3409548-007",
   "identifier": "kaal:claim:3409548-007",
   "text": "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.",
   "abstract": "ML can help in accurate portfolio diversification by looking for uncorrelated instruments that match requirements of the risk profile.",
   "citation": "Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548",
   "datePublished": "2019",
   "claim_type": "mechanism",
   "confidence": "asserted",
   "is_failure_mode": false,
   "scope_conditions": [
    "portfolio construction against a defined risk profile"
   ],
   "source_pdf_sha256": "74227ab2656b06bfe4a29c942fc2ba26df9f476917e0a9f85ec038b8c3402c40",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/3409548-009",
   "identifier": "kaal:claim:3409548-009",
   "text": "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.",
   "abstract": "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.",
   "citation": "Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548",
   "datePublished": "2019",
   "claim_type": "failure",
   "confidence": "argued",
   "is_failure_mode": true,
   "scope_conditions": [
    "data scientists reusing a single data set repetitively"
   ],
   "source_pdf_sha256": "74227ab2656b06bfe4a29c942fc2ba26df9f476917e0a9f85ec038b8c3402c40",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/3409548-025",
   "identifier": "kaal:claim:3409548-025",
   "text": "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.",
   "abstract": "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.",
   "citation": "Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548",
   "datePublished": "2019",
   "claim_type": "mechanism",
   "confidence": "asserted",
   "is_failure_mode": false,
   "scope_conditions": [
    "large order execution"
   ],
   "source_pdf_sha256": "74227ab2656b06bfe4a29c942fc2ba26df9f476917e0a9f85ec038b8c3402c40",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/3409548-026",
   "identifier": "kaal:claim:3409548-026",
   "text": "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.",
   "abstract": "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.",
   "citation": "Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548",
   "datePublished": "2019",
   "claim_type": "empirical",
   "confidence": "evidenced",
   "is_failure_mode": false,
   "scope_conditions": [
    "2018 BarclayHedge survey respondents"
   ],
   "source_pdf_sha256": "74227ab2656b06bfe4a29c942fc2ba26df9f476917e0a9f85ec038b8c3402c40",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/5245185-028",
   "identifier": "kaal:claim:5245185-028",
   "text": "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.",
   "abstract": "rely on machine learning models trained on agent data, which may replicate biases or fail to detect novel deviations not present in training sets.",
   "citation": "Wulf A. Kaal, How can we Best Monitor AI Agents (2025). SSRN: https://ssrn.com/abstract=5245185",
   "datePublished": "2025",
   "claim_type": "failure",
   "confidence": "evidenced",
   "is_failure_mode": true,
   "scope_conditions": [
    "machine learning monitors trained on historical agent behavior",
    "novel agent behaviors outside training distribution"
   ],
   "source_pdf_sha256": "4d7adba83ec722480e97bde6528cbe9ce98c709e45cb18794f157a64b8fe7da2",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/5541658-001",
   "identifier": "kaal:claim:5541658-001",
   "text": "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.",
   "abstract": "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,",
   "citation": "Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658",
   "datePublished": "2025",
   "claim_type": "mechanism",
   "confidence": "argued",
   "is_failure_mode": false,
   "scope_conditions": [
    "applies to the historical development of AI and law from the 1980s through the 2000s"
   ],
   "source_pdf_sha256": "e543a2d698fcd522d4d02e034cc9ee1344d0015d2c824b40b9e05ab7c0728c60",
   "status": "current"
  }
 ],
 "description": "10 claims in the published works of Wulf A. Kaal carry the concept tag 'machine-learning'. Derived node: a roster, not an adjudicated definition."
}