As AI adoption grows across all areas of business, so do the ethical challenges that come with it. Kubicle’s new course, AI Ethics and the Workplace, helps learners understand how to use AI responsibly by exploring core ethical principles and practical frameworks for evaluation, oversight, and decision-making.
This course is designed to build awareness and promote accountability in the use of AI tools—across teams, processes, and procurement.
Course Overview: AI Ethics and the Workplace
The course introduces learners to six core principles of ethical AI—transparency, accountability, fairness, privacy, reliability and safety, and beneficence—and shows how these can be applied in everyday workplace scenarios. It supports critical thinking, risk recognition, and values-based implementation.
Included Lessons:
- Introduction to AI Ethics and the Workplace
Defines AI in the workplace and outlines why ethical use is a growing concern for organizations and individuals. - The Pillars of Trustworthy AI
Introduces a six-pillar ethical framework for evaluating and managing AI use. - Building Ethical AI Practices through Values
Links ethical AI use to organizational values and culture, supporting trust and alignment. - When Speed Overrides Ethics
Explores how performance-driven decisions can compromise fairness and integrity. - Who’s Accountable when AI Fails?
Highlights the importance of clear oversight and accountability structures. - Understanding Ethical Risks in AI
Identifies common risks such as bias, privacy invasion, and systemic exclusion. - Uncovering Bias in Algorithms
Explains how training data and design choices can lead to discriminatory outcomes. - Transparency and Explainability
Emphasizes the need to understand and communicate how AI systems make decisions. - Data, Privacy, and Consent
Focuses on the ethical management of user data and the role of consent in responsible AI. - Ethical AI in Content and Communication
Addresses risks associated with AI-generated content and communication. - Hallucinations, Hidden Logic, and Exclusion
Covers the risk of AI misinformation, hidden logic, and the exclusion of diverse voices. - Spotting the Red Flags Early
Provides techniques for early detection of problematic AI behaviors or use cases. - Asking the Right Questions
Introduces a questioning mindset to guide ethical evaluations of AI tools. - Evaluating AI Tools and Vendors
Applies ethical criteria to vendor selection and procurement decisions.
Key Outcomes:
By the end of this course, learners will be able to:
- Apply ethical principles to evaluate AI tools and workplace practices
- Identify common risks such as bias, exclusion, and misinformation
- Understand the importance of transparency, accountability, and data protection
- Recognize early warning signs of unethical AI behavior
- Support values-based procurement and implementation decisions
This course is ideal for teams adopting or scaling AI tools and for organizations building responsible AI practices into governance, procurement, and training.