(Circular E), Employer's Tax Guide - Publication 15 (For Use in 2021)


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Employer's Tax Guide (Circular E) - The Families First Coronavirus Response Act (FFCRA), enacted on March 18, 2020, and amended by the COVID-related Tax Relief Act of 2020, provides certain employers with tax credits that reimburse them for the cost of providing paid sick and family leave wages to their employees for leave related to COVID‐19. Qualified sick and family leave wages and the related credits for qualified sick and family leave wages are only reported on employment tax returns with respect to wages paid for leave taken in quarters beginning after March 31, 2020, and before April 1, 2021, unless extended by future legislation. If you paid qualified sick and family leave wages in 2021 for 2020 leave, you will claim the credit on your 2021 employment tax return. Under the FFCRA, certain employers with fewer than 500 employees provide paid sick and fam-ily leave to employees unable to work or telework. The FFCRA required such employers to provide leave to such employees after March 31, 2020, and before January 1, 2021. Publication 15 (For use in 2021)




Congressional Record


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Guidelines Manual


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Public Library Laws


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Encyclopedia of Ethical Failure


Book Description

The Standards of Conduct Office of the Department of Defense General Counsel's Office has assembled an "encyclopedia" of cases of ethical failure for use as a training tool. These are real examples of Federal employees who have intentionally or unwittingly violated standards of conduct. Some cases are humorous, some sad, and all are real. Some will anger you as a Federal employee and some will anger you as an American taxpayer. Note the multiple jail and probation sentences, fines, employment terminations and other sanctions that were taken as a result of these ethical failures. Violations of many ethical standards involve criminal statutes. This updated (end of 2009) edition is organized by type of violations, including conflicts of interest, misuse of Government equipment, violations of post-employment restrictions, and travel.




United States Code


Book Description

"The United States Code is the official codification of the general and permanent laws of the United States of America. The Code was first published in 1926, and a new edition of the code has been published every six years since 1934. The 2012 edition of the Code incorporates laws enacted through the One Hundred Twelfth Congress, Second Session, the last of which was signed by the President on January 15, 2013. It does not include laws of the One Hundred Thirteenth Congress, First Session, enacted between January 2, 2013, the date it convened, and January 15, 2013. By statutory authority this edition may be cited "U.S.C. 2012 ed." As adopted in 1926, the Code established prima facie the general and permanent laws of the United States. The underlying statutes reprinted in the Code remained in effect and controlled over the Code in case of any discrepancy. In 1947, Congress began enacting individual titles of the Code into positive law. When a title is enacted into positive law, the underlying statutes are repealed and the title then becomes legal evidence of the law. Currently, 26 of the 51 titles in the Code have been so enacted. These are identified in the table of titles near the beginning of each volume. The Law Revision Counsel of the House of Representatives continues to prepare legislation pursuant to 2 U.S.C. 285b to enact the remainder of the Code, on a title-by-title basis, into positive law. The 2012 edition of the Code was prepared and published under the supervision of Ralph V. Seep, Law Revision Counsel. Grateful acknowledgment is made of the contributions by all who helped in this work, particularly the staffs of the Office of the Law Revision Counsel and the Government Printing Office"--Preface.










Regulating Artificial Intelligence


Book Description

This book assesses the normative and practical challenges for artificial intelligence (AI) regulation, offers comprehensive information on the laws that currently shape or restrict the design or use of AI, and develops policy recommendations for those areas in which regulation is most urgently needed. By gathering contributions from scholars who are experts in their respective fields of legal research, it demonstrates that AI regulation is not a specialized sub-discipline, but affects the entire legal system and thus concerns all lawyers. Machine learning-based technology, which lies at the heart of what is commonly referred to as AI, is increasingly being employed to make policy and business decisions with broad social impacts, and therefore runs the risk of causing wide-scale damage. At the same time, AI technology is becoming more and more complex and difficult to understand, making it harder to determine whether or not it is being used in accordance with the law. In light of this situation, even tech enthusiasts are calling for stricter regulation of AI. Legislators, too, are stepping in and have begun to pass AI laws, including the prohibition of automated decision-making systems in Article 22 of the General Data Protection Regulation, the New York City AI transparency bill, and the 2017 amendments to the German Cartel Act and German Administrative Procedure Act. While the belief that something needs to be done is widely shared, there is far less clarity about what exactly can or should be done, or what effective regulation might look like. The book is divided into two major parts, the first of which focuses on features common to most AI systems, and explores how they relate to the legal framework for data-driven technologies, which already exists in the form of (national and supra-national) constitutional law, EU data protection and competition law, and anti-discrimination law. In the second part, the book examines in detail a number of relevant sectors in which AI is increasingly shaping decision-making processes, ranging from the notorious social media and the legal, financial and healthcare industries, to fields like law enforcement and tax law, in which we can observe how regulation by AI is becoming a reality.




QDROs


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