"data poisoning" meaning in All languages combined

{
  "forms": [
    {
      "form": "data poisonings",
      "tags": [
        "plural"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {
        "1": "~"
      },
      "expansion": "data poisoning (countable and uncountable, plural data poisonings)",
      "name": "en-noun"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "categories": [
        {
          "kind": "other",
          "name": "English entries with incorrect language header",
          "parents": [
            "Entries with incorrect language header",
            "Entry maintenance"
          ],
          "source": "w"
        },
        {
          "kind": "topical",
          "langcode": "en",
          "name": "Artificial intelligence",
          "orig": "en:Artificial intelligence",
          "parents": [
            "Computer science",
            "Cybernetics",
            "Computing",
            "Sciences",
            "Applied mathematics",
            "Systems theory",
            "Technology",
            "All topics",
            "Mathematics",
            "Systems",
            "Fundamental",
            "Formal sciences",
            "Interdisciplinary fields",
            "Society"
          ],
          "source": "w"
        },
        {
          "kind": "topical",
          "langcode": "en",
          "name": "Computer security",
          "orig": "en:Computer security",
          "parents": [
            "Computing",
            "Technology",
            "All topics",
            "Fundamental"
          ],
          "source": "w"
        }
      ],
      "examples": [
        {
          "ref": "2019, Simon N. Foley, editor, Data and Applications Security and Privacy XXXIII […], Springer, page 4",
          "text": "While data sanitization shows promise to defend against data poisoning, it is often impossible to validate every data source [14].",
          "type": "quotation"
        },
        {
          "ref": "2022, Alfred Z. Spector, Peter Norvig, Chris Wiggins, Jeannette M. Wing, Data Science in Context: Foundations, Challenges, Opportunities, Cambridge University Press, page 148",
          "text": "Similarly, the Tay chatbot suffered from data poisoning. To mitigate data poisoning, it is important not to let any one group contribute too much data to a model.",
          "type": "quotation"
        },
        {
          "ref": "2023, Katharine Jarmul, Practical Data Privacy, O'Reilly",
          "text": "Data poisoning is one type of adversarial attack—where a user or group of users submit false data to influence the model toward a particular or incorrect prediction.",
          "type": "quotation"
        },
        {
          "ref": "2023, Paul Scharre, Four Battlegrounds: Power in the Age of Artificial Intelligence, W. W. Norton & Company",
          "text": "Some forms of data poisoning are undetectable. Attackers can insert adversarial noise into the training data, altering the training data in a way that is hidden to human observers.",
          "type": "quotation"
        }
      ],
      "glosses": [
        "The deliberate use of a training dataset with data designed to increase errors in the output of a machine learning model."
      ],
      "id": "en-data_poisoning-en-noun-hNz9NCnE",
      "links": [
        [
          "machine learning",
          "machine learning"
        ],
        [
          "computer security",
          "computer security"
        ],
        [
          "dataset",
          "dataset"
        ],
        [
          "model",
          "model"
        ]
      ],
      "qualifier": "machine learning; computer security; machine learning; computer security",
      "raw_glosses": [
        "(machine learning, computer security) The deliberate use of a training dataset with data designed to increase errors in the output of a machine learning model."
      ],
      "synonyms": [
        {
          "word": "poisoning attack"
        }
      ],
      "tags": [
        "countable",
        "uncountable"
      ]
    }
  ],
  "word": "data poisoning"
}

{
  "forms": [
    {
      "form": "data poisonings",
      "tags": [
        "plural"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {
        "1": "~"
      },
      "expansion": "data poisoning (countable and uncountable, plural data poisonings)",
      "name": "en-noun"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "categories": [
        "English countable nouns",
        "English entries with incorrect language header",
        "English lemmas",
        "English multiword terms",
        "English nouns",
        "English terms with quotations",
        "English uncountable nouns",
        "en:Artificial intelligence",
        "en:Computer security"
      ],
      "examples": [
        {
          "ref": "2019, Simon N. Foley, editor, Data and Applications Security and Privacy XXXIII […], Springer, page 4",
          "text": "While data sanitization shows promise to defend against data poisoning, it is often impossible to validate every data source [14].",
          "type": "quotation"
        },
        {
          "ref": "2022, Alfred Z. Spector, Peter Norvig, Chris Wiggins, Jeannette M. Wing, Data Science in Context: Foundations, Challenges, Opportunities, Cambridge University Press, page 148",
          "text": "Similarly, the Tay chatbot suffered from data poisoning. To mitigate data poisoning, it is important not to let any one group contribute too much data to a model.",
          "type": "quotation"
        },
        {
          "ref": "2023, Katharine Jarmul, Practical Data Privacy, O'Reilly",
          "text": "Data poisoning is one type of adversarial attack—where a user or group of users submit false data to influence the model toward a particular or incorrect prediction.",
          "type": "quotation"
        },
        {
          "ref": "2023, Paul Scharre, Four Battlegrounds: Power in the Age of Artificial Intelligence, W. W. Norton & Company",
          "text": "Some forms of data poisoning are undetectable. Attackers can insert adversarial noise into the training data, altering the training data in a way that is hidden to human observers.",
          "type": "quotation"
        }
      ],
      "glosses": [
        "The deliberate use of a training dataset with data designed to increase errors in the output of a machine learning model."
      ],
      "links": [
        [
          "machine learning",
          "machine learning"
        ],
        [
          "computer security",
          "computer security"
        ],
        [
          "dataset",
          "dataset"
        ],
        [
          "model",
          "model"
        ]
      ],
      "qualifier": "machine learning; computer security; machine learning; computer security",
      "raw_glosses": [
        "(machine learning, computer security) The deliberate use of a training dataset with data designed to increase errors in the output of a machine learning model."
      ],
      "synonyms": [
        {
          "word": "poisoning attack"
        }
      ],
      "tags": [
        "countable",
        "uncountable"
      ]
    }
  ],
  "word": "data poisoning"
}