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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.
Regulation of artificial intelligence is the development of public sector policies and laws for promoting and ... including bias, discrimination, ... For example, the ...
In the 2010s public concerns about racial and other bias in the use of AI for criminal sentencing decisions and findings of creditworthiness may have led to increased demand for transparent artificial intelligence. [7] As a result, many academics and organizations are developing tools to help detect bias in their systems. [60]
The post Watch: Artificial intelligence and the bias against dark skin appeared first on TheGrio. “It puts us in danger…,” says Damon Hewitt, head of the Lawyers’ Committee for Civil ...
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
Also known as current moment bias or present bias, and related to Dynamic inconsistency. A good example of this is a study showed that when making food choices for the coming week, 74% of participants chose fruit, whereas when the food choice was for the current day, 70% chose chocolate.