🎯 AI Fairnees #3: The Algorithmic Bias
🧑🏽💻 Another machine learning bias: the Algorithmic Bias.
So, what is algorithmic bias? It's when the algorithms used to build our machine learning models have a built-in bias, either intentionally or unintentionally.
For example, let's say we have a machine learning model that's supposed to predict who will get a job based on their resume.
If the algorithm is designed to prioritize certain types of data, like experience or education, it might produce biased results against candidates who don't have those characteristics.
But don't worry, there are ways to mitigate this problem.
One way is by using techniques like "algorithmic fairness," which ensure that our algorithms are making unbiased decisions across different groups.
So, next time you're building a machine learning model, make sure to pay attention to algorithmic bias and use techniques like algorithmic fairness to ensure your models are making fair and unbiased decisions.
Hopefully you liked this video 💚
🔥 Subscribe to Bitswired
👍🏽 Leave us a like/comment to support us
💬HASHTAGS:
#bias #fairness #machinelearning #ai #deeplearning #datascience #fair #sampling #samplingbias #python #pytorch #tensorflow #pythonprogramming #learnpython #learnprogramming #engineering #data #algorithms #machinelearningalgorithms #shorts #short
