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Responsible AI Use at Work: A Plain Overview
A plain overview of responsible AI use at work, covering the main duties around fairness, privacy, accuracy, transparency and accountability for teams.
Responsible AI
Privacy, Ethics and Risk
Data protection, bias, accuracy and accountability for AI use.
Building an AI Risk Checklist for Your Organization
Build a reusable risk checklist for assessing a new AI use, with questions on purpose, data, accuracy, fairness, oversight and what to do if things go wrong.
Copyright and Ownership Questions Around AI Content
A general look at the unsettled questions about who owns AI-generated text and images, what inputs are safe to use, and how businesses can reduce risk.
Data Protection Basics for AI Projects
General data protection principles for AI projects, including data minimization, purpose limits, access control, retention and when to seek expert advice.
Deepfakes and Synthetic Media Risks for Businesses
How fabricated audio, images and video can be used against businesses, what warning signs to look for, and practical steps to verify and respond.
Environmental Costs of Running AI Systems
A balanced look at the electricity, water and hardware resources AI systems use, why estimates vary, and what organizations can reasonably do about it.
Human Review Loops That Actually Work
How to design human review of AI output so it is genuine oversight rather than a rubber stamp, with guidance on risk tiers, reviewer time and feedback.
Protecting Customer Data When Using AI Tools
What to avoid pasting into AI tools, how to anonymize and limit what you share, and practical steps for reducing customer data exposure at work.
Transparency: Telling People When AI Is Involved
When and how to disclose AI assistance to customers, colleagues and applicants, with practical wording ideas and cautions about legal requirements.
Understanding Bias in AI Systems
How skewed data and design choices produce unfair AI outputs, where bias shows up at work, and how teams can look for it and limit the harm.
What Happens to the Data You Enter Into AI Tools
Why you should check how AI tools store, retain, review and share what you enter, and which terms and settings to look for before using one at work.
Who Is Accountable When an AI Tool Gets It Wrong
Why responsibility for AI mistakes stays with the organization using the tool, how vendor terms fit in, and how to assign owners and prepare for errors.
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