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Optional stopping is a practice where one collects data until some stopping criteria is reached. While it is a valid procedure, it is easily misused. The problem is that p-value of an optionally stopped statistical test is larger than what it seems.
Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. [1] A survey from May 2020 revealed practitioners' common feeling for better protection of machine learning systems in industrial applications.
Using machine learning to detect bias is called, "conducting an AI audit", where the "auditor" is an algorithm that goes through the AI model and the training data to identify biases. [162] Ensuring that an AI tool such as a classifier is free from bias is more difficult than just removing the sensitive information from its input signals ...
Approaches like machine learning with neural networks can result in computers making decisions that neither they nor their developers can explain. It is difficult for people to determine if such decisions are fair and trustworthy, leading potentially to bias in AI systems going undetected, or people rejecting the use of such systems.
Detailed discussion of these topics may be found on their main pages. These characterizations were made in the context of educating the public about questionable or potentially fraudulent or dangerous claims and practices, efforts to define the nature of science, or humorous parodies of poor scientific reasoning.
The images above demonstrate an example of how an artificial neural network might make a false positive result in object detection. The input image is a simplified example of the training phase, using multiple images that are known to depict starfish and sea urchins, respectively. The starfish match with a ringed texture and a star outline ...
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).
In general, the risk () cannot be computed because the distribution (,) is unknown to the learning algorithm. However, given a sample of iid training data points, we can compute an estimate, called the empirical risk, by computing the average of the loss function over the training set; more formally, computing the expectation with respect to the empirical measure: