Algorithm of bias

It has made significant progress in artificial intelligence and deep learning fields recently. We humans also should deepen our learning and understanding to improve problem-solving effectiveness.

Humans and machines work collaboratively to solve problems large or small effectively. But there are “biases and prejudices” in an era of machine intelligence as well. Deep learning is part of a broader concept of machine learning methods based on learning representation of data. How can any algorithm reflect what is going on in real neural networks when it takes an enormously large sample of learning data, how to improve those “weight & bias” algorithms to improve “deep learning” maturity? 

There isn't a single, universal "bias algorithm." Bias in machine learning arises from various factors during the development and use of algorithms. Here's a breakdown of how bias can creep into deep learning intelligence.

Machine learning algorithms learn from the data they are trained on, therefore, there is a bias: If the training data itself is biased, the algorithm will inherit and perpetuate those biases. The design choices made by developers can introduce bias. For instance: Choosing features (characteristics used for prediction) that are inherently correlated with societal biases can lead to biased outcomes. In North America, underlying assumptions built into the algorithm can lead to bias. For example, an algorithm assuming everyone has access to a car might disadvantage people who rely on public transportation. As algorithms are used in real-world situations, their decisions can influence the data they are subsequently trained on. This can create feedback loops that amplify existing biases.

Build effective techniques to mitigate bias in AI systems: Both people and machines have bias, especially at the unconscious level. By understanding how bias can enter AI systems and taking steps to mitigate it, we can work towards fairer and more responsible applications of machine learning.

Increase Data Cleaning and Augmentation: Identifying and removing biases in training data or enriching the data with more representative samples.

Set up Fairness Metrics: Measuring and monitoring bias in algorithms during development and deployment.

Increase Algorithmic Explainability: Develop methods to understand how algorithms arrive at their decisions, allowing for the detection and correction of potential biases.

Enhance Human oversight: Incorporating human review of algorithmic decisions in critical areas to ensure fairness.

It has made significant progress in artificial intelligence and deep learning fields recently. We humans also should deepen our learning and understanding to improve problem-solving effectiveness. So we can improve deep learning objectivity and maturity. By understanding how bias can enter AI systems and taking steps to mitigate it, we can work towards fairer and more responsible applications of machine learning.

Posted in: Big Data,Business Intelligence