Ethics in AI
As businesses adopt AI, they must address bias, transparency, privacy, security, and the impact of automated decisions. Ethical considerations should be part of system design and procurement, not an afterthought.
Building Trust
Clear policies, evaluation methods, documentation, and human escalation paths help build trust with customers and employees. Responsible deployment requires continued monitoring after launch.
Translate governance principles into operating decisions
For an internal knowledge assistant, governance means deciding which documents it may access, what it should do when sources conflict, and who investigates a reported error. These decisions can be tested. A broad statement that a system is responsible does not substitute for defined controls and accountable owners.
A practical pilot
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Record the intended users, permitted tasks, data sources, and foreseeable failure modes.
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Assign owners for evaluations, access reviews, incident response, and release approval.
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Set a review schedule and record changes to the model, prompts, connectors, and source documents.
Controls and limitations
Provide a usable way to question or correct output and to reach a person. Evaluate impacts on affected users, including people who do not operate the tool directly. Reassess the workflow when its scope or data changes rather than assuming an earlier review covers every future use.
What to measure
Track unresolved incidents, access violations, unsupported answers, and correction time. Use these findings to change operating limits or pause a workflow when its performance no longer meets the agreed criteria.
Explore governance and compliance controls.
