What is bias and AI and how can we overcome it?
Projects rooted in artificial intelligence are fast becoming an integral part of the modern technological paradigm, aiding in decision-making processes across various sectors, from finance to healthcare. However, despite the significant progress, AI systems are not without their flaws.
Lastly, human biases can inadvertently be introduced during the data labeling process, as labelers may harbor unconscious prejudices. The choice of features or variables used in AI models can result in biased outcomes, as some features may be more correlated with certain groups, causing unfair treatment. To mitigate these issues, researchers and practitioners need to be aware of potential sources of skewed objectivity and actively work to eliminate them.
Maintaining objective neutrality requires careful attention to the various stages of AI development, including data collection, model training and evaluation. Additionally, ongoing monitoring and updating of AI systems are crucial to addressing potential prejudices that may arise over time. “What blockchain and Web3 systems can offer is not complete objectivity or lack of bias but rather transparency so that users can clearly see what bias an AI system has. It also offers open configurability so that a user community can tweak an AI model to have the sort of bias it prefers and transparently see what sort of bias it is reflecting,” he said.
Similarly, Dan Peterson, chief operating officer for Tenet — an AI-focused blockchain network — told Cointelegraph that it’s tough to quantify neutrality and that some AI metrics cannot be unbiased because there is no quantifiable line for when a data set loses neutrality. In his view, it eventually boils down to the perspective of where the engineer draws the line, and that line can vary from person to person.
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