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Explainable AI ( XAI ), often overlapping with interpretable AI, or explainable machine learning ( XML ), either refers to an artificial intelligence (AI) system over which it is possible for humans to retain intellectual oversight, or refers to the methods to achieve this. [1] [2] The main focus is usually on the reasoning behind the decisions ...
Right to explanation. In the regulation of algorithms, particularly artificial intelligence and its subfield of machine learning, a right to explanation (or right to an explanation) is a right to be given an explanation for an output of the algorithm. Such rights primarily refer to individual rights to be given an explanation for decisions that ...
Human-AI collaboration is the study of how humans and artificial intelligence (AI) agents work together to accomplish a shared goal. AI systems can aid humans in everything from decision making tasks to art creation. Examples of collaboration include medical decision making aids., hate speech detection, and music generation.
Less than a year ago, Anthropic was founded by former OpenAI VP of research Dario Amodei, intending to perform research in the public interest on making AI more reliable and explainable. Its $124 ...
The first consideration when discussing transparency in AI should be data, the fuel that powers the algorithms. Because data is the foundation for all AI, it is valid to want to know where the ...
Hewlett Packard Enterprise, the company better known as HPE, announced today that it acquired Pachyderm, a startup developing a data science platform for “explainable, repeatable” AI. The ...
“The new machine learning models we call ‘CfC’s’ [closed-form Continuous-time] replace the differential equation defining the computation of the neuron with a closed form approximation ...
The concept of trustworthy AI also encompasses the need for AI systems to be explainable, accountable, and robust. Transparency in AI involves making the processes and decisions of AI systems understandable to users and stakeholders.