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Artificial Intelligence

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IEEE - P7010 - Wellbeing Metrics Standard for Ethical Artificial Intelligence and Autonomous Systems

This standard establishes wellbeing metrics relating to human factors directly affected by intelligent and autonomous systems and establishes a baseline for the types of objective and subjective data these systems should analyse and include (in their programming and functioning) to proactively increase human wellbeing.
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IEEE - P7007 - Ontological Standard for Ethically Driven Robotics and Automation Systems

The standard establishes a set of ontologies with different abstraction levels that contain concepts, definitions and axioms which are necessary to establish ethically driven methodologies for the design of Robots and Automation Systems.
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Development Status: Under development
 


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IEEE - P7005 - Standard for Transparent Employer Data Governance

The standard defines specific methodologies to help employers to certify how they approach accessing, collecting, storing, utilizing, sharing, and destroying employee data. The standard provides specific metrics and conformance criteria regarding these types of uses from trusted global partners and how vendors and employers can meet them.


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IEEE - P7003 - Algorithmic Bias Considerations

This standard describes specific methodologies to help users certify how they worked to address and eliminate issues of negative bias in the creation of their algorithms, where "negative bias" infers the usage of overly subjective or uniformed data sets or information known to be inconsistent with legislation concerning certain protected characteristics (such as race, gender, sexuality, etc.); or with instances of bias against groups not necessarily protected explicitly by legislation, but otherwise dimini


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IEEE - P7002 - Data Privacy Process

This standard defines requirements for a systems/software engineering process for privacy-oriented considerations regarding products, services, and systems utilizing employee, customer or other external user's personal data. It extends across the life cycle from policy through development, quality assurance, and value realization. It includes a use case and data model (including metadata).


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