Artificial Intelligence

This standard is designed to provide organizations handling child and student data governance-oriented processes and certifications guaranteeing the transparency and accountability of their actions as it relates to the safety and wellbeing of children, their parents, the educational institutions where they are enrolled, and the community and societies where they spend their time, both on and offline. It is also designed to help parents and educators, with an understanding that most individuals may not be tech-savvy enough to understand underlying issues of data usage, but still must be properly informed about the safety of their children's data and provided with tools and services that provide proper opportunities for content based, pre-informed choice regarding their family's data.

The standard provides phenotypic and demographic definitions that technologists and auditors can use to assess the diversity of face data used for training and benchmarking algorithmic performance, establishes accuracy reporting and data diversity protocols/rubrics for automated facial analysis, and outlines a rating system to determine contexts in which automated facial analysis technology should not be used.

ISO/IEC TR 20547-2:2018 provides examples of big data use cases with application domains and technical considerations derived from the contributed use cases.

The Akoma Ntoso standard distinguishes between concepts regarding the description and identification of legal documents, their content, and the context in which they areused.  Names are used to associate the document representations to concepts so that documents can be “read/understood” by a machine, thus allowing sophisticated services that are impossible to attain with documents containing only typographical information, such as documents created in word-processing applications.To make documents machine-readable, every part with a relevant meaning and role must have a “name” (or “tag”) that machines can read. The content is marked up as precisely as possible according to the legal analysis of the text. This requires precisely identifying the boundaries of the different text segments, providing an element name that best describes the text in each situation, and also providing a correct identifier to each labelled fragment.

This standard is designed to provide individuals or organizations creating algorithms, largely in regards to autonomous or intelligent systems, certification-oriented methodologies to provide clearly articulated accountability and clarity around how algorithms are targeting, assessing and influencing the users and stakeholders of said algorithm. Certification under this standard will allow algorithm creators to communicate to users, and regulatory authorities, that up-to-date best practices were used in the design, testing and evaluation of the algorithm to avoid unjustified differential impact on users.