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Training AI models and copyrighted materials
For the first time, a US court has found that copyright was infringed by copying works for the purpose of training an AI legal research tool.
Global | Publication | September 2022
On September 8, 2022 the Financial Reporting Council (FRC) published the results of a Thematic Review it has conducted into the reporting of earnings per share (EPS) in companies’ financial statements. The report highlights some of the more common errors and, using case studies and examples, it aims to explain the issues involved and show how companies can improve the reliability of their EPS by complying with the detailed requirements of IAS 33, and providing more helpful disclosures.
International Accounting Standard (IAS) 33 ‘Earnings per Share’ sets out the calculation, presentation and disclosure requirements for EPS under International Financial Reporting Standards (IFRSs). All listed companies, reporting under IFRSs or UK GAAP, are required to report EPS in accordance with IAS 33 in both their annual and interim financial statements, and to include comparatives for all periods presented. Reviews undertaken by the FRC’s Corporate Reporting Review (CRR) team show that some of the main principles of IAS 33 are not always well understood, or applied correctly, even in relatively straightforward circumstances. On several occasions, queries raised by CRR on a company’s annual report have resulted in a restatement of the company’s reported EPS in the following year.
The FRC’s findings highlight a number of areas where there is scope for companies to improve their reporting of EPS:
The FRC remind companies that:
(FRC, Thematic Review – Earnings per Share (IAS 33), 08.09.2022)
Publication
For the first time, a US court has found that copyright was infringed by copying works for the purpose of training an AI legal research tool.
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Federally regulated employers may soon be required to review and possibly raise pay rates for part-time, seasonal or casual workers under new “Equal Treatment” wage rules.
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Developing high-performance generative AI systems and other AI systems based on machine learning often requires access to vast amounts of data for training (AI training data) and improving their accuracy and performance, and data scraping is an approach that is taken to generate large enough data sets.
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