entity · derived
Data quality
Derived node: assembled mechanically from the claims carrying data-quality. A roster, not an adjudicated definition.
Every claim under this term
- 2348463-007 : The SEC has not standardized the disclosures required in Form PF, and there is evidence that Form PF requirements rest on an inconsistent use of industry terms, which can in turn produce inconsistent
- 2348463-022 : Form PF disclosures have not been standardized, and anecdotal evidence indicates that the SEC and the FSOC may be working with contradictory, misleading, inaccurate, and incomplete systemic risk data.
- 2447306-003 : Form PF's counterparty credit exposure requirement is difficult to satisfy at the source, because the exposure is highly sensitive information that individual fund managers often cannot readily determ
- 2447306-004 : If advisers' allegations that Form PF disclosures cannot be answered other than by guessing are correct, then the SEC's capacity to evaluate the data is compromised, and regulation built on incomplete
- 2447306-034 : Because only 27.08 percent of respondents used a service provider to complete Form PF, the widespread concern that outside service providers would overinterpret required Form PF data on filers' behalf
- 2447306-037 : Form PF fund performance metrics are not accurate or comparable across filers, because reporting entities employ different calculation methodologies to produce them.
- 2470008-001 : The SEC data collected from private fund advisers feeds every stage of the FSOC's systemic risk assessment, and the FSOC leans most heavily on precisely those disclosure items that are the most proble
- 2470008-002 : Accuracy and consistency problems in the SEC's private fund data collection can impair the FSOC's ability to evaluate the systemic risk posed by private fund advisers.
- 2470008-022 : The SEC itself reports that the consistency of investment advisers' responses on Form PF is not ensured and may be questionable.
- 2470008-023 : Advisers take different approaches and make different assumptions when completing Form PF, which the SEC identifies as a further challenge to the usability of the data.
- 2470008-024 : The SEC's initial analysis of Form PF data turned up anomalies attributed to filer error, which prompted SEC concern about the quality of the information private fund advisers report.
- 2470008-025 : Expanding the uses of Form PF data remains difficult so long as there is insufficient confidence in the accuracy of what advisers report, notwithstanding SEC efforts to improve quality through interpr
- 2470008-026 : Form PF data quality and utility are likely to improve over time as filers grow familiar with the form's requirements and calculation methods, because the SEC's experience with the data is still early
- 2470008-028 : The substantive defects in Form PF data are the ambiguity of several key questions, inaccurate definitions paired with insufficient SEC guidance, and difficulty aggregating the required information.
- 2470008-030 : The Form PF definition of Regulatory Assets under Management is the leading example of a definition that forced filers to interpret what they were required to report.
- 2470008-031 : The interpretation Form PF demands generated particular concern among filers about the definition of counterparties and about counterparty performance measures.
- 2470008-033 : Because several core Form PF questions feeding the FSOC's stage one threshold screen are themselves defective, the FSOC's systemic risk assessment process could be compromised.
- 2470008-034 : Because the FSOC uses RAUM related valuations directly and indirectly to set stage one thresholds, and because RAUM requires substantial filer interpretation, it is questionable whether the FSOC can u
- 2470008-036 : Widespread filer disagreement with Form PF definitions implies that a large share of filers are uncertain how to answer, which raises the possibility that they complete the form with estimates and var
- 2470008-037 : If the FSOC relies on inaccurate Form PF data in its systemic risk assessment, its work on private funds may itself be erroneous.
- 2470008-039 : Matching the identified Form PF defects against the FSOC's specific uses of that data suggests possible inaccuracies in the FSOC's systemic risk assessment process, although the author disclaims scien
- 2732915-012 : Form PF required disclosures of counterparty credit exposure constitute sensitive information that individual fund managers often cannot readily determine, which makes that reporting requirement hard
- 2732915-017 : Industry concerns about the burdensome nature of Title IV's mandatory private fund adviser registration and disclosure requirements appear mostly unfounded, although data inconsistencies remain a conc
- 2834531-003 : Increasing the quantity of data does not dissolve the foundational methodological problems of data: construct validity, measurement, reliability, and data dependencies remain the same regardless of ho
- 2998097-013 : Some Form PF disclosure requirements are not answerable as designed, because counterparty credit exposure is sensitive information that individual private fund managers often cannot readily determine.
- 2998097-021 : The SEC's private fund data collection encountered accuracy and consistency problems that hampered the FSOC's ability to evaluate the systemic risk of private funds.
- 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
- 3411897-015 : Big data is often not the output of instruments designed to generate valid and reliable data suitable for scientific analysis, so foundational data quality problems persist regardless of how much data
- 4755632-001 : Data quality is a necessary but not a sufficient condition for AI growth: algorithms, computing power, and developer expertise also play significant roles, yet access to quality datasets remains a fun
- 4755632-002 : Transformer neural network architecture removes the scale constraint on training data but not the quality constraint, so data quality continues to be a major unsolved issue for large language models e
- 4900880-026 : Many existing studies of automation and job loss rest on flawed assumptions and weak data, and their seemingly precise figures conceal those defects, so their headline numbers should not be taken at f
- 5095633-004 : The apparent abundance of internet text overstates the usable supply, because much of it fails quality thresholds for model training due to redundancy, noise, or irrelevance.
- 5095633-020 : Automating annotation to gain speed and cost savings produces less nuanced labeling that misses the complex human judgments and context certain AI applications require.
- 5095633-036 : Ocean Protocol's market-driven reputation signal is too indirect: it does not measure individual expertise or annotation consistency, and market forces lag behind real-time shifts in best practices an