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This bias often stems from training data that reflects historical and systemic inequalities. For example, AI systems used in hiring, law enforcement, or healthcare may disproportionately disadvantage certain racial groups by reinforcing existing stereotypes or underrepresenting them in key areas.
“If bias encoding cannot be avoided at the algorithm stage, its identification enables a range of stakeholders relevant to the AI health technology's use (developers, regulators, health policy ...
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 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, automated decision-making, accountability, privacy, and regulation.
ChatGPT creators OpenAI say the system has been “politically biased, offensive” and “otherwise objectionable”, and has committed to changing how it works.
The only way to combat this kind of hidden bias will be to mandate that tech companies reveal far more about how their AI models have been trained and allow independent auditing and testing.
Another prevalent example of representational harm is the possibility of stereotypes being encoded in word embeddings, which are trained using a wide range of text. These word embeddings are the representation of a word as an array of numbers in vector space , which allows an individual to calculate the relationships and similarities between ...
Selection bias involves individuals being more likely to be selected for study than others, biasing the sample. This can also be termed selection effect, sampling bias and Berksonian bias. [3] Spectrum bias arises from evaluating diagnostic tests on biased patient samples, leading to an overestimate of the sensitivity and specificity of the ...