Our approach to building transparent and explainable AI systems - Deepstash

Delivering the best member and customer experiences with a focus on trust is core to everything that we do at LinkedIn. As we continue to build on our Responsible AI program that we recently outlin...

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Our approach to building transparent and explainable AI systems

engineering.linkedin.com

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Transparency means that AI system behaviour and its related components are understandable, explainable, and interpretable. The goal is that end-users of AI—such as LinkedIn employees, customers, an...

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Transparency allows modellers, developers, and technical auditors to understand how an AI system works, including how a model is trained and evaluated, what its decision boundaries are, what inputs...

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A few key ways we've improved transparency in AI at LinkedIn:

  • Explainable AI for model consumers to build trust and augment decision-making.
  • Explainable AI for modellers to perfo...

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Complex predictive machine learning models often lack transparency, resulting in low trust from these teams despite having high predictive performance. While many model interpretation approaches su...

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Machine learning engineers at LinkedIn need to understand how their models are making the decisions to identify blindspots and, thereby, opportunities for improvement. For this, we have explainabil...

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From a holistic “responsible design” perspective, there are many non-AI initiatives that help increase the transparency of our products and experiences.

  • On the backend, researchers have ...

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