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Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions made by such models after a learning process may be considered unfair if they were based on variables considered sensitive (e.g., gender, ethnicity, sexual orientation, or disability).
The study of algorithmic bias is most concerned with algorithms that reflect "systematic and unfair" discrimination. [3] This bias has only recently been addressed in legal frameworks, such as the European Union's General Data Protection Regulation (proposed 2018) and the Artificial Intelligence Act (proposed 2021, approved 2024).
The ethics of artificial intelligence covers a broad range of topics within the field that are considered to have particular ethical stakes. [1] This includes algorithmic biases, fairness, [2] automated decision-making, accountability, privacy, and regulation.
The new government will also ensure that AI systems developed in America are free from “ideological bias and never restrict our citizens’ right to free speech,” he said. Annoa Abekah-Mensah ...
Executive Order 14179, titled "Removing Barriers to American Leadership in Artificial Intelligence", is an executive order signed by Donald Trump, the 47th President of the United States, on January 23, 2025. The executive order aims to initiate the process of strengthening U.S. leadership in artificial intelligence, promote AI development free ...
An example of a scenario where AI systems of surveillance could bring discrimination to a new high is the initiative to create LGBT-free zones in Poland. [ 11 ] [ 7 ] Skeptical of ethical regulations to control the technology, McQuillan suggests people's councils and workers' councils, and other forms of citizens' agency to resist AI. [ 7 ]
President-elect Donald Trump has telegraphed big changes to the nation's all-important AI strategy, many of which are expected to be implemented immediately after his inauguration in January.
Discussions on regulation of artificial intelligence in the United States have included topics such as the timeliness of regulating AI, the nature of the federal regulatory framework to govern and promote AI, including what agency should lead, the regulatory and governing powers of that agency, and how to update regulations in the face of rapidly changing technology, as well as the roles of ...