Learn more about computerscience with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
A few key ways we've improved transparency in AI at LinkedIn:
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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, and members—can use these insights to understand these systems, suggest improvements, and identify pot...
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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 such as SHAP and LIME return top important features to help interpret model predictions, these top fea...
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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 go into the model, and finally, why it made a specific prediction. This is often also described as ...
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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 explainability tools that allow model developers to derive insights and characteristics about their model at a ...
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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 outlined three months ago, a key part of our work is designing products that provide the right protections...
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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.
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Other curated ideas on this topic:
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 go into the model, and finally, why it made a specific prediction. This is often also described as ...
Ensuring transparency and accountability in the realm of AI ethics stands as a formidable challenge, one that is central to fostering trust in AI systems and ensuring their responsible use.
The 'black box' nature of certain AI algorithms is akin to peering into the depths o...
To address this, there's a pressing need to establish clear guidelines and standards for AI development. These guidelines should encompass not only the technical aspects of AI but also ethical considerations.
Creating a transparent framework that outlines the development process, data sour...
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