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What is the importance of Responsible AI?
in Data Science by Diamond (64,350 points) | 42 views

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The last few years have brought to light many failed cases of companies deploying AI without adequately considering or analyzing the risk of their models. In order to mitigate the risks of models overfitting, perpetuating historical human bias, or failing to adapt to data drift (among others), AI deployers should consider our core themes of Responsible AI:

  • Explainable AI (XAI): The ability to explain a model after it has been developed
  • Interpretable Machine Learning: Transparent model architectures and increasing how intuitive and understandable ML models can be
  • Ethical AI: Sociological fairness in machine learning predictions (i.e., whether one category of person is being weighted unequally)
  • Secure AI: Debugging and deploying ML models with similar counter-measures against insider and cyber threats as would be seen in traditional software
  • Human-Centered AI: User interactions with AI and ML systems
  • Compliance: Making sure your AI systems meet the relevant regulatory requirements whether that’s with GDPR, CCPA, FCRA, ECOA or other regulations
by Diamond (64,350 points)

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