AISP Toolkit Feb25 2025 - Flipbook - Page 75
Terms
Data Sharing: The practice of providing access to information not otherwise available.
Data Integration: The process of bringing together data from different sources, which often
includes identi昀椀able information (e.g., name, date of birth, SSN) so that records can be linked at the
individual level.
Administrative Data: Data collected during the routine process of administering programs.
Administrative Data Reuse: Using administrative data in a way not originally intended, e.g., for
research.
Asset-Framing: de昀椀ning people and communities by their contributions and strengths before noting
challenges and de昀椀cits.
Bias: The tendency to favor one perspective, outcome, or group over others, often in an unfair or
unbalanced way. Bias can stem from personal beliefs, cultural in昀氀uences, or systemic factors, and
leads to distorted judgment or decision-making. Bias may be conscious (explicit) or unconscious
(implicit) and affects all stages of the data life cycle. Common examples of bias in data include
sampling bias and con昀椀rmation bias.
Community Engagement: The process of working collaboratively with and through groups of people
to address issues affecting the group’s well-being. Community engagement should include authentic
processes at all stages of a project. Centering the community in agency work is necessary to achieve
long-term and sustainable outcomes.
TERMS
Community: A group of people who share a common place, experience, interest, or a larger system
that people are a part of (e.g., youth in foster care).
Consent: Explicit permission regarding the collection, storage, management, and use of personal
information. Individuals can give active (i.e., opt-in) or passive consent (i.e., implicit or opt-out). Consent
must be freely given, speci昀椀c, informed, and unambiguous. See Future of Privacy Forum & AISP.
Data: Information collected to help decision-making.
Data Ethics: A branch of ethics that evaluates data practices with the potential to adversely impact
people and society. Ethical concerns should be considered and addressed at all stages of the data
life cycle. See Open Data Institute.
Data Governance: The people, policies, and procedures that determine how data are used and
protected.
Data Infrastructure: The systems, technologies, and processes for using, storing, securing, and
interpreting data. This includes hardware, software, and organizational practices.
Data Minimization: The principle of limiting or minimizing the collection, storage, and disclosure
of data to only what is necessary to accomplish a speci昀椀c use. Data minimization is an important
principle that supports privacy and ethical data use.
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