Fourth principle: Bias and Harm Mitigation
Fourth principle: bias and Harm Mitigation
Fourth principle: Bias and Harm Mitigation
Those responsible for AI enabled systems must proactively mitigate the risk of unexpected or unintended biases or harms resulting from these systems, whether through their original rollout, or as they learn, change or are redeployed.
Fourth principle: Bias and Harm Mitigation
Of particular concern is the risk of discriminatory outcomes resulting from algorithmic bias or skewed data sets.
Fourth principle: Bias and Harm Mitigation
Of particular concern is the risk of discriminatory outcomes resulting from algorithmic bias or skewed data sets.
Fourth principle: Bias and Harm Mitigation
Defence must ensure that its AI enabled systems do not result in unfair bias or discrimination, in line with the MOD’s ongoing strategies for diversity and inclusion.
Fourth principle: Bias and Harm Mitigation
Defence must ensure that its AI enabled systems do not result in unfair bias or discrimination, in line with the MOD’s ongoing strategies for diversity and inclusion.
Fourth principle: Bias and Harm Mitigation
Defence must ensure that its AI enabled systems do not result in unfair bias or discrimination, in line with the MOD’s ongoing strategies for diversity and inclusion.
Fourth principle: Bias and Harm Mitigation
A principle of bias and harm mitigation requires the assessment and, wherever possible, the mitigation of these biases or harms.
Fourth principle: Bias and Harm Mitigation
A principle of bias and harm mitigation requires the assessment and, wherever possible, the mitigation of these biases or harms.
Fourth principle: Bias and Harm Mitigation
This includes addressing bias in algorithmic decision making, carefully curating and managing datasets, setting safeguards and performance thresholds throughout the system lifecycle, managing environmental effects, and applying strict development criteria for new systems, or existing systems being applied to a new context.