Accountability
The obligation to take responsibility for AI decisions and be able to explain their impacts.
Accountability means clear responsibility for AI decisions: Who is in charge, who explains, who is liable? Must be clarified BEFORE problems.
Explanation
Accountability requires: Clear responsibilities (who is in charge?), documentation (audit trails), answerability (explanation on issues), consequences (liability). Part of the RAI framework.
Marketing Relevance
Who is responsible when marketing AI discriminates, spreads misinformation, or causes harm? Accountability must be clarified BEFORE the incident.
Example
A company defines: AI Product Owner bears responsibility for model outputs, documented in RACI matrix and incident response playbook.
Common Pitfalls
Diffuse responsibility ("nobody in charge"). Audit trails missing. Accountability on paper, not in practice.
Origin & History
Algorithmic Accountability Act (USA, proposed 2019) and EU AI Act (2024) made accountability a legal requirement. IEEE and ISO developing standards.
Comparisons & Differences
Accountability vs. Responsibility
Responsibility is the moral duty; Accountability is formal answerability with consequences.
Accountability vs. Transparency
Transparency makes visible what happens; Accountability makes clear who is responsible for it.