How to drive bias out of AI without making mistakes of Google Gemini

When Google recently took its Gemini image-generation feature offline for further testing because of bias issues, the episode raised red flags about the potential dangers of generative artificial intelligence.Ensuring transparency in how generative AI systems operate and make decisions is crucial for building trust and addressing bias concerns.

Ensuring transparency in how generative AI systems operate and make decisions is crucial for building trust and addressing bias concerns, said Ritu Jyoti, group vice president, AI and automation, market research and advisory services at International Data Corp. Biases can arise if the training data is limited or skewed towards certain demographics, Atkinson said."By collecting data from a wide range of sources and making sure it is representative of the population, companies can reduce the risk of biased outcomes," he said.

Continuous evaluation of an AI system's performance is also important to help identify and rectify any biases that may arise.Regularly monitoring the outputs of generative AI systems is essential to identify and mitigate biases," Jyoti said."Organizations should establish evaluation frameworks and metrics to assess the fairness and ethical implications of the generated content.

In addition, companies should set up systems for gathering input and feedback."Creating channels for users to report inaccuracies or unexpected outputs is critical to knowledge sharing and making sure you are catching inconsistencies or biases before it becomes a widespread problem," Atkinson said.

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