How can we promote critical evaluation of AI-driven decisions?

Aug 13, 2018 ... This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. We argue that algorithmic bias evolves with human interaction in an iterative manner, which may have a long-term effect on algorithm performance and humans' ... This review will discuss the relevant ethical and bias considerations in AI-ML specifically within the pathology and medical domain. Both of these existing methods address the problem of bias by adjusting the machine learning model. However, oftentimes it is the training data itself – i.e., ... Mar 19, 2010 ... A bias value allows you to shift the activation function to the left or right, which may be critical for successful learning. A method of assessing and correcting inaccuracies in a machine learning model. It involves measuring the difference between the model's predicted outcomes and ... Jun 8, 2023 ... Bias mitigation in machine learning models · 1. Pre-processing algorithms · 2. In-processing algorithms · 3. Post-processing algorithms. Mar 5, 2019 ... Reports of successful applications of machine learning (ML) methods in structure-based virtual screening (SBVS) are increasing. What is machine learning bias (AI bias)? ... Machine learning bias, also known as algorithm bias or AI bias, is a phenomenon that occurs when an algorithm ... Feb 4, 2021 ... Data bias in machine learning is a type of error in which certain elements of a dataset are more heavily weighted and/or represented than others ...