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Explainable AI explained

Some day machine learning models may be more ‘glass box‘ than black box. Until then, ‘XAI’ tools and techniques can help us understand how a black box model makes its decisions.

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Explainable AI explained | InfoWorld

While machine learning and deep learning models often produce good classifications and predictions, they are almost never perfect. Models almost always have some percentage of false positive and false negative predictions. That’s sometimes acceptable, but matters a lot when the stakes are high. For example, a drone weapons system that falsely identifies a school as […]

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Explainable AI explained

Someday machine learning models may be more ‘glass box‘ than black box. Until then, explainability tools and techniques can help us understand how a black box model makes its decisions.

Matt-turek
Christoph-molnar
Google
Microsoft-research
Defense-advanced-research-projects-agency
Data-labeling-service
Wells-fargo
Microsoft
Machine-learning
Azure-machine
Interpretable-machine-learning
Google-cloud-platform

Supporting Responsible Use Of AI And Equitable Outcomes In Financial Services – Federal Reserve Governor Lael Brainard At The AI Academic Symposium Hosted By The Board Of Governors Of The Federal Reserve System, Washington, D.C. (Virtual Event)

Supporting Responsible Use Of AI And Equitable Outcomes In Financial Services – Federal Reserve Governor Lael Brainard At The AI Academic Symposium Hosted By The Board Of Governors Of The Federal Reserve System, Washington, D.C. (Virtual Event) Date 12/01/2021 Today s symposium on the use of artificial intelligence (AI) in financial services is part of the Federal Reserve s broader effort to understand AI s application to financial services, assess methods for managing risks arising from this technology, and determine where banking regulators can support responsible use of AI and equitable outcomes by improving supervisory clarity.1 The potential scope of AI applications is wide ranging. For instance, researchers are turning to AI to help analyze climate change, one of the central challenges of our time. With nonlinearities and tipping points, climate change is highly complex, and quantification for risk assessments requires the analysis of vast amounts of data, a task

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Interesting Research Programs from the 2010s

The Disordered Mind Theory of Mental Illness In a series of articles and books over the past decade, the philosopher George Graham develops a theory of mental disorder with the following qualities: 1. is non-reductionist with respect to the mind and the brain; 2. is informed by the philosophy of mind; and 3. coheres with the experiences of patients and clinicians. Graham’s theory holds that mental illness is distinct from somatic/bodily illness though may co-occur or otherwise be bound up with so-called broken brains. A helpful analogy for what is meant here by “the mental” or “the mind” is to view the brain as computer hardware, whereas the mind is software. On this picture, the mind and the brain are surely not independent; a hardware issue may impede the computation of some software. However, one may have bugs in one’s software - “gumming up the works” to use a common phrase of Graham - on perfectly functioning hardware.

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