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Fairness of AI
Ensuring AI algorithms are fair and unbiased involves several measures:Ensuring AI algorithms are fair and unbiased involves several measures: -Diverse Data: it is advisable to use datasets that are different and are taken from -different sources to minimize bias that is found in the data. -Bias DetRead more
Ensuring AI algorithms are fair and unbiased involves several measures:Ensuring AI algorithms are fair and unbiased involves several measures:
-Diverse Data: it is advisable to use datasets that are different and are taken from -different sources to minimize bias that is found in the data.
See less-Bias Detection: Use techniques that prevent biases in the data and models and quantify its effect.
-Algorithm Audits: Semi-periodically review the algorithms in order to assess and fix the sources of prejudice.
-Transparency: Ensure that the development process and logic behind decision made by AI are clear to all the stakeholders.
-Fairness Metrics: Engage in aspects of fairness metrics and guidelines that may be used in the assessment and prevention of biased result occurrences in various categories.
-Human Oversight: Introduce human supervision in rechecking and tweaking of AI outcomes especially in sensitive operations.
-Ethical Guidelines: Abide to already existing standards and policies or creating a new set of policies on artificial intelligence.
-Continuous Monitoring: In order to mitigate the problems with AI system new biases that might appear after deployment these should be continuously monitored.
With these measures, the developers of AI can thus strive to build algorithms that are free from prejudice.
Machine Learning
Supervised learning uses training data which is defined as the input data plus the output label that belongs to it. This approach is useful where one needs to make direct prediction like in a classification or regression problem. Supervised learning can be applied in legal language models to build tRead more
Supervised learning uses training data which is defined as the input data plus the output label that belongs to it. This approach is useful where one needs to make direct prediction like in a classification or regression problem. Supervised learning can be applied in legal language models to build the models for document classification, prediction of legal outcomes, and identification of certain information such as the name of the case or date of the case from the legal documents.
On the other hand, unsupervised learning works with the data that has no labels, the goal of which is to find the structure in the input data. Some of the strategies that are classified under this category include clustering and dimensionality reduction. When applied to models of legal language, the unsupervised learning is used for the topic modeling, abstraction and discovering other latent structures in the large number of legal documents.
To sum up, supervised learning offers specific input/output relationships to use the labeled data which is very appropriate for providing clear oriented answers while the unsupervised learning reveals the concealed structures in the unlabeled data which is useful for discovering the trends.
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