Scary Stories About AI Gone Wrong (Let’s Get Ethical)
AI systems are already making high-stakes decisions—and sometimes getting them catastrophically wrong. This course dissects real-world AI failures to show you where ethics breaks down, why it matters to your career, and how to build accountability into systems from day one.
AIU.ac Verdict: Essential for anyone shipping AI features or reviewing AI implementations; you’ll leave with concrete red flags to spot before deployment. Fair warning: the 50-minute format means breadth over depth—expect a primer, not a comprehensive ethics framework.
What This Course Covers
The course walks through documented AI failures across hiring, healthcare, criminal justice, and autonomous systems—examining the technical, organisational, and human factors that led to harm. You’ll see how bias creeps into training data, how optimising for the wrong metric backfires, and why ‘we didn’t intend harm’ isn’t a defence. Practical takeaway: recognising these patterns in your own projects before they become headlines.
Beyond cautionary tales, the course signals how responsible teams embed ethics checks into development cycles—from data audits to stakeholder impact assessments. It’s framed for technologists and product leads who need to speak ethics language with compliance and leadership, not philosophers debating moral theory.
Who Is This Course For?
Ideal for:
- ML/AI engineers shipping models to production: You need to spot bias and fairness issues before they become PR disasters or regulatory fines.
- Product managers and tech leads evaluating AI tools: Understand the ethical landmines in third-party AI systems and ask the right questions of vendors.
- Career-switchers entering AI roles: Establish ethical foundations early; this course signals what responsible AI culture looks like.
May not suit:
- Ethics researchers or policy specialists: This is practitioner-focused; you’ll find it too shallow for academic or regulatory work.
- Learners seeking deep technical AI skills: The course prioritises storytelling over algorithms—it’s ethics literacy, not model-building.
Frequently Asked Questions
How long does Scary Stories About AI Gone Wrong (Let’s Get Ethical) take?
50 minutes. It’s a focused primer designed to fit into a lunch break or team sync—not a full certification track.
Do I need AI experience to take this course?
No. The course is built around real-world case studies, not code. Technical and non-technical team members benefit equally.
Will this teach me how to build ethical AI systems?
It teaches you to *recognise* ethical failures and ask the right questions. For hands-on implementation (bias testing, fairness metrics, etc.), you’ll want follow-up technical courses.
Is this accredited or certified?
It’s a Pluralsight course authored by THAT Conference—valuable for professional development and team alignment, but not a formal certification. AIU.ac recommends pairing it with your organisation’s ethics or compliance training.
Course by THAT Conference on Pluralsight. Duration: 0h 50m. Last verified by AIU.ac: March 2026.
