entity · derived
Training data
Derived node: assembled mechanically from the claims carrying training-data. A roster, not an adjudicated definition.
Every claim under this term
- 3128900-003 : The performance of an AI neural network's learning algorithm during supervised training rises with the quality and quantity of the labelled datasets it is trained on, which ties AI progress directly t
- 3128900-005 : Existing centralized micro task marketplaces cannot adequately meet the rising demand for high quality labelled AI training data.
- 4796714-011 : Bias in AI systems arises when algorithms incorporate discriminatory practices carried in their training data, and the resulting outputs reveal a profound misalignment between AI operations and societ
- 4796714-034 : Routing proposals through the Forum and then through Validation Pool review is what allows the input parameters and learning data of AI systems to be governed by expert community consensus, because on
- 4796714-035 : AI learning is degraded by Web2 platforms because their engagement driven algorithms amplify extreme viewpoints and negativity, so the human sentiment and ethics the models absorb from that data are s
- 4855607-003 : Deep learning models inadvertently learn and amplify whatever biases exist in their training data, so the composition of the training corpus, not the architecture, is the source of unfair or discrimin
- 4855607-026 : Requiring community members to stake reputation tokens in order to validate data quality is what produces robust and reliable training datasets, and this participatory validation improves annotation a
- 4941807-015 : A critical unsolved challenge for AI governance is bias mitigation, because biases enter inadvertently when algorithms incorporate discriminatory practices carried in the data used for training.
- 5541658-013 : Bias in judicial AI arises because models are trained on historical data that reflect past inequities, and the standard remedy of fairness through unawareness, meaning the omission of protected charac
- 5541658-030 : Because AI systems are predominantly developed in the West and trained mostly on Western data, their outputs are liable to carry cultural biases that inadequately represent non-Western cultures and th
- 6607458-017 : Computational abundance does not eliminate information asymmetry; it transforms its locus, since traditional informational advantages such as knowledge of market conditions, contract terms, and domain