Explainable AI is one of the key requirements for implementing responsible AI, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability, and accountability. ML models are often thought of as black boxes that are impossible to interpret. Neural networks used in deep learning are some of the hardest for a human to understand. AI Bias, often based on demographics such as race, gender, age, or location, has been a long-standing risk in training AI models. Further, AI model performance can drift or degrade because production data differs from training data.
The black box in AI poses a deep threat to individuality and ethical aspects of artificial intelligence in data science. This brings the cord closer to deepening the understanding and the critical role of responsible AI for the greater good. As AI becomes more advanced, ML processes still need to be understood and controlled to ensure AI model results are accurate. Hence, it is imperative to target massive career goals with strategic skill ramp-up with top data science certifications from USDSI®. These are sure to leverage greater gains as well as provide an in-depth competence to you. explore today!
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