IEEE

A worldwide standard for narrowband power line communications (PLC) via alternating current, direct current, and nonenergized electric power lines using frequencies below 500 kHz. Data rates of up to 500 kb/s are supported. The field of use includes Smart Grid applications. Coexistence mechanisms that can be used by other PLC technologies operating below 500 kHz are also included. These coexistence mechanisms may be used separately from the rest of the standard.

Physical (PHY) and media access control (MAC) layers of the medium frequency band (less than 12 MHz) broadband power line communication technology for smart grid applications (SGPLC) based on orthogonal frequency division multiplexing (OFDM) are specified in this standard. The necessary security requirements that assure communication privacy and allow use for mission critical and security sensitive services and applications are addressed in this standard. The coexistence with other technologies based on IEEE Std 1901(TM)-2010 also are addressed. The approach that is geared towards achieving an extended communication range with medium speeds in comparison with the existing power line communication technologies operating in similar frequency bands is defined in this standard.

The interoperability and compliance testing for IEEE 1901.1 products built, in which physical (PHY) and media access control (MAC) layers of the medium frequency band (less than 12 MHz) broadband power line communication technology for smart grid applications (SGPLC) based on orthogonal frequency division multiplexing (OFDM), is specified in this standard. The corresponding PLC application scenarios and test environment are introduced, and the PHY/MAC test cases and test scenarios are provided. The procedures for compliance, interoperability, and certification of IEEE Std 1901.1(TM) are specified in this standard.

A radio interface including medium access control sublayer and physical layer of white space dynamic spectrum access radio systems supporting fixed and mobile operation in white space frequency bands, while avoiding causing harmful interference to incumbent users in these frequency bands is specified in this standard.

This standard establishes a uniform set of definitions and a process model for the procurement of Artificial Intelligence (AI) and Automated Decision Systems (ADS) by which government entities can address socio-technical and responsible innovation considerations to serve the public interest. The process requirements include a framing of procurement from an IEEE Ethically Aligned Design (EAD) foundation and a participatory approach that redefines traditional stages of procurement as: problem definition, planning, solicitation, critical evaluation of technology solutions (e.g. Impact assessments), and contract execution. The scope of the standard not only addresses the procurement of AI in general, but also government in-house development and hybrid public-private development of AI and ADS as an extension of internal government procurement practices.

Specific methodologies to help employers in accessing, collecting, storing, utilizing, sharing, and destroying employee data are described in this standard. Specific metrics and conformance criteria regarding these types of uses from trusted global partners and how third parties and employers can meet them are provided in this standard. Certification processes, success criteria, and execution procedures are not within the scope of this standard.

A set of ontologies with different abstraction levels that contain concepts, definitions, axioms, and use cases that assist in the development of ethically driven methodologies for the design of robots and automation systems is established by this standard. It focuses on the robotics and automation domain without considering any particular applications and can be used in multiple ways, for instance, during the development of robotics and automation systems as a guideline or as a reference “taxonomy” to enable clear and precise communication among members from different communities that include robotics and automation, ethics, and correlated areas. Users of this standard need to have a minimal knowledge of formal logics to understand the axiomatization expressed in Common Logic Interchange Format.

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 diminishing stakeholder or user well being and for which there are good reasons to be considered inappropriate. Possible elements include (but are not limited to): benchmarking procedures and criteria for the selection of validation data sets for bias quality control; guidelines on establishing and communicating the application boundaries for which the algorithm has been designed and validated to guard against unintended consequences arising from out-of-bound application of algorithms; suggestions for user expectation management to mitigate bias due to incorrect interpretation of systems outputs by users (e.g. correlation vs. causation).

To coordinate global data and AI literacy building efforts, this standard establishes an operational framework and associated capabilities for designing policy interventions, tracking their progress, and empirically evaluating their outcomes. The standard includes a common set of definitions, language, and understanding of data and AI literacy, skills, and readiness.

This standard describes recognizable audio and visual marks to assist with the identification of communicating entities as human or machine intelligence to facilitate transparency, understanding, and trust during online, telephone, or other electronic interactions. Interventions to discern whether an interaction is with a machine or not (such as a Turing Test) are not within the scope of this standard. This standard is concerned only about the declaration of the nature of the agency influencing an interaction.

Test specifications with a set of indicators for common corruption and adversarial attacks, which can be used to evaluate the robustness of artificial intelligence-based image recognition services are provided in this standard. Robustness attack threats and establishes an assessment framework to evaluate the robustness of artificial intelligence-based image recognition service under various settings are also specified in this standard.

This recommended practice establishes an evaluation framework for the capabilities of artificial intelligence dialogue systems such as chatbots, consulting terminals, or operation interfaces. The recommended practice defines and classifies the types and levels of the intelligence capabilities according to a checklist of criteria. The checklist tables describe the criteria used to determine the level that a dialogue system achieves based on the analysis of behavior and performance.