AI primer for All Employees: Responsible Use in Business

Explore foundational principles for responsible AI use in business, centering humans, ensuring accountability, and value addition, with AI as a co-pilot, not a replacement, to boost creativity and safety.

Own accountability for AI-generated reports; you are the pilot responsible for final results. Use AI to assist with writing, analysis, and recommendations, but it lacks business context and policy understanding.

Apply value-add by using AI only when it saves time, reduces errors, or improves the experience for real people; avoid tools that speed work yet harm customers.

Ensure ai treats people fairly and avoids bias by following origins, reality, and vigilance—the three principles that guard against reflecting past discrimination in data.

Reveal how biased AI causes unequal outcomes in hiring, lending, and risk assessment, including resume screening. Examine blind spots from biased historical data that threaten fair treatment.

Exercise vigilance by reviewing AI outputs for fairness and questioning patterns to ensure equal treatment. Pause and verify data when bias signals appear in candidate profiles or escalation patterns.

Explain the three principles of transparency and explainability—disclosure, reasoning, and trust—and how signaling when users chat with AI protects trust and sets expectations.

Prioritize glass box AI by revealing the reasoning behind results rather than just the outcomes. Explain factors like credit history, income, and risk indicators to validate and defend decisions.

Build trust in ai by being transparent about when users chat with a bot, like a caller id for ai, so expectations stay clear and interactions remain secure.

Protect privacy and data security by avoiding non-public information in public AI tools, and follow ownership and approval standards to safeguard company secrets and customer data.

Clarify ownership of ai-generated content and rights to use, modify, or distribute it. Respect copyright, trademarks, and other intellectual property rights to avoid infringement, as the bakery logo example shows.

Apply company-approved AI as a secured vault: use only IT-approved tools, ensure data safety, delete sensitive information after use, and avoid training external models; when in doubt, ask before using.

Govern AI responsibly through governance and risk assessment, applying risk, human in the loop, and flagging, using a traffic light system to guide policy-compliant reviews by risk.

Embed human-in-loop processes to ensure AI outputs are reviewed and approved before action. AI can highlight issues, but humans must assess risk and decide the final outcome.

Flag AI recommendations when something seems off to prevent automation bias. Empower humans to stop and escalate to ethics or tech teams, ensuring life-or-death decisions have human checks.

Study real-world case studies to learn what works and what goes wrong with ai. Analyze Zillow's misfire to recognize ai risk and the need to compare insights with real-world data.

This case shows how responsible AI use adds real value, with JPMorgan Chase's COIN assisting lawyers by handling repetitive contract reviews in seconds, freeing humans for judgment and strategy.

Evaluate AI from regulation and impact to protect stakeholders, comply with EU AI Act and GDPR, and use secure approved AI systems to protect personal data and build trust.

Explore how AI decisions affect people, balancing efficiency with ethics and shaping experiences and outcomes. Identify who benefits or is harmed and why fairness, inclusion, and trust matter in interactions.

Explore future challenges in responsible AI use as AI evolves toward agentic capabilities that act on your behalf, such as clicking buttons and sending emails, while humans remain accountable.

Identify deepfakes and ai-generated voices, images, and videos that look and sound real, eroding trust and security; learn to verify identities and sources rather than relying on appearances.

Explore how AI's environmental footprint arises from data centers and cloud infrastructure, consuming energy and water, and learn to use AI where it adds real value rather than mere convenience.

Use the AI ethics checklist at work as a safety net. Pause to check privacy, fairness, clarity, and human review before relying on AI outputs.