Ethics Maturity Continuum Glossary

Accountability: When someone is accountable it means they are answerable for the results of an action after it has been performed. AI and software accountability means that a company deploying AI and software has designated roles that are both answerable for the impact of the AI and software as well as responsible for AI and software governance within company processes.

AI and/or Software Governance: The process or framework used to evaluate and monitor algorithms and systems for effectiveness, data quality, and bias.

Reference: AI (Artificial Intelligence) Governance: How To Get It Right

Intentional Design: Successful AI and software design focuses on creating products that serve human-centric needs, either on the individual or societal level. Intentional design goes a step further by ensuring significant thought and consideration has gone into understanding potential intended and unintended consequences of designing an AI or software product to serve such needs.

Unintended consequences: Outcomes of technology that were not intended or foreseen.

Reference: Value Sensitive Design

Fairness: Fairness seeks to minimize instances of unwanted bias and instead promote inclusive representation in AI and software development.

Unwanted bias: Occurs when system based decisions are made using individual traits that should not otherwise correlate to the outcome (i.e. gender being used as a deciding factor for job applicants).

Reference: What Do We Do About the Biases in AI?

Social Impact: AI and software have the potential to impact not only vast numbers of individuals but also shape the societies in which we function. It is therefore essential to consider the short- and long-term effects the introduction of an AI or software product will have, giving particular attention to the wellbeing of end-users.

Short-term wellbeing: Moment to moment happiness or pleasure.

Long-term wellbeing: Overall satisfaction with life.

Reference: IEEE Wellbeing Standards

Trust and Transparency: Data is information on individuals and collective behavior, which means users must be able to clearly understand how their data is being handled and protected. In addition to transparent communication and robust security, the user must feel that their information remains as private as they so want, the combination of which results in strong user trust.

Reference: Customer Data: Designing for Transparency and Trust