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Algorithmic bias does not only include protected categories, but can also concern characteristics less easily observable or codifiable, such as political viewpoints. In these cases, there is rarely an easily accessible or non-controversial ground truth, and removing the bias from such a system is more difficult. [148]
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).
One of the most notorious examples of representational harm was committed by Google in 2015 when an algorithm in Google Photos classified Black people as gorillas. [9] Developers at Google said that the problem was caused because there were not enough faces of Black people in the training dataset for the algorithm to learn the difference ...
Similar biases have been uncovered in algorithms used to determine resource allocation, such as how much assistance people with disabilities receive. These are just a handful of many examples ...
Story at a glance New research underscores the implicit bias present in some artificial intelligence language models. Researchers found models were generally more likely to rate content containing ...
Algorithmic accountability refers to the allocation of responsibility for the consequences of real-world actions influenced by algorithms used in decision-making processes. [ 1 ] Ideally, algorithms should be designed to eliminate bias from their decision-making outcomes.
These manipulations often stem from biases in the data, the design of the algorithm, or the underlying goals of the organization deploying them. One major cause of algorithmic bias is that algorithms learn from historical data, which may perpetuate existing inequities. In many cases, algorithms exhibit reduced accuracy when applied to ...
Sponsored by the Association for Computing Machinery, this conference focuses on issues such as algorithmic transparency, fairness in machine learning, bias, and ethics from a multi-disciplinary perspective. [2] The conference community includes computer scientists, statisticians, social scientists, scholars of law, and others. [3]