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The fairness doctrine of the United States Federal Communications Commission (FCC), introduced in 1949, was a policy that required the holders of broadcast licenses both to present controversial issues of public importance and to do so in a manner that fairly reflected differing viewpoints. [1]
Red Lion Broadcasting Co. v. Federal Communications Commission, 395 U.S. 367 (1969), was a seminal First Amendment ruling at the United States Supreme Court.The Supreme Court held that radio broadcasters enjoyed free speech rights under the First Amendment, but those rights could be partially restricted by the Federal Communications Commission (FCC) to maintain the public interest in equitable ...
Even after the end of the closely related fairness doctrine, the personal attack rule continued to be enforced until 2000. The rule, however, came to an end after the U.S. Court of Appeals for the D.C. Circuit ordered an immediate end with its ruling in Radio-Television News Directors Association, where the court chastised the agency for years ...
The Mayflower doctrine hearing was held in late March-early April 1948 and had 49 witnesses testify between the two sides. [2] A decision wasn't made until June 2, 1949, when it was ruled that the Mayflower doctrine was to be repealed. The FCC introduced later that year the Fairness doctrine as a replacement.
The equal-time rule should not be confused with the now-defunct FCC fairness doctrine, which dealt with presenting balanced points of view on matters of public importance. The Zapple doctrine (part of a specific provision of the fairness doctrine) was similar to the equal-time rule but applied to different political campaign participants. The ...
The Zapple doctrine came into existence as an addition to the FCC fairness doctrine. The fairness doctrine was a Federal Communications Commission (FCC) policy instated in June 1949. It required broadcasters to present multiple viewpoints about controversial matters of public importance.
Fairness, absence of bias in specific realms: In American broadcasting, presentation of controversies in accord with the Fairness Doctrine. In computer science, fairness is a property of unbounded nondeterminism. In computer science, and specifically in machine learning, fairness is a desirable property of algorithms to avoid bias.
This means people are treated fairly based on what they have earned, and are not treated equally unconditionally. This sixth foundation changes the theory so that the fairness/cheating foundation no longer has a split personality; it's no longer about equality and proportionality. It primarily becomes about proportionality. [7]