Transparency & Trust

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Where there are items to search and sort, the user wants the most valuable items to be surfaced, via a deep insight into what they value and how that value is manifested in the data. 

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The user wants to know, in general terms, how the AI is operating: what it takes as inputs, what calculations it runs, and what it outputs.

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When a machine learning system is being trained through the user's repeated inputs, the user wants to know that this is the case.

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Sometimes, an AI system doesn't work as the user wants it to or they're not comfortable using an AI driven system.

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Users want to ensure their data does not fall into the wrong hands if another user has access to their device.

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Users want to know what happens to their data when it's captured by the system.

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While processing an image, the user wants to see the results as quickly as possible, even if that means only seeing part of the image completed.

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The user wants to trust an AI system but finds the concept of an artificial intelligence worrying.

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Users want to know how much trust to place in a system's intelligence, especially in critical applications such as the diagnosis of health issues.

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The user wants the system to return search results based on that system’s understanding of the content of those items, not just via matching keywords contained in each.