Ethical and Responsible AI Use

Understand what artificial intelligence is and how machine learning uses data-driven pattern recognition to drive recommendations and natural language processing. Compare AI with traditional software and emphasize responsible use.

Explore how AI touches daily life and work, from phone unlocks to hiring. Learn responsible use through accountability, fairness, and human oversight to address bias.

Explore how fairness in AI requires active effort to avoid disadvantaging groups, and how transparency and explainability build trust by revealing data, training, limitations, and decision factors.

Assign accountability across developers, deploying organizations, managers, and end users when AI makes harmful decisions. Guard privacy and safety by understanding data use and maintaining human oversight.

Apply a risk-based view of EU AI Act and NIST AIRMF and OWASP Top 10 for LLMs to govern AI use in high-risk settings like HR and credit.

Review your organization's AI use policy to identify approved tools, data handling, human oversight, and disclosure obligations for confident, compliant AI work.

Understand how AI bias arises from data, algorithm design, and human choices, and learn why biased systems amplify unfair outcomes and demand active scrutiny.

Spot bias in AI tools at work by watching for patterns in outcomes, homogeneous outputs, and stereotypes. Document observations and escalate through proper channels, pausing use until reviewed.

Explore the black box problem in AI, weighing accuracy against explainability in high-stakes decisions, and examine ethical and regulatory considerations for responsible AI use.

question AI outputs as a professional responsibility, not skepticism; assess consequences, task suitability, and known limitations like hallucinations, bias, and outdated data.

Identify automation bias and its impact on judgment, and apply strategies such as forming your view, requiring reasoning, introducing friction, varying sources, and meaningful human oversight under EU AI Act.

Act quickly to contain data incidents by deleting inputs, reporting details to the data protection team within 72 hours, and clarifying ownership and disclosure of AI-generated outputs.

Learn how personal data extends beyond names and emails to location data, IP addresses, biometric data, and inferences, and how consent, data minimization, and need-to-know protect individuals in AI.

Raise AI concerns through the right channels, with specific observations, and rely on whistleblower protections and AI governance committees to oversee policy and accountability.