A High‑Level Overview of AI Ethics

Authors: Edina Harbinja Kazim & Thilo Koshiyama

Published in: Patterns | 2021

In “A high‑level overview of AI ethics” (Patterns, 2021), Kazim & Koshiyama offer a well‑structured, accessible introduction to the interdisciplinary field of AI ethics—bringing together perspectives from philosophy, law, and computer science.

Why it matters

As AI systems grow more powerful and pervasive, so do their ethical implications—think bias in hiring algorithms, privacy breaches, or opaque decision-making ("black‑box" AI). The authors spotlight emerging ethical dilemmas and call for frameworks that address accountability, responsibility, and transparency at every stage of AI development .

Key takeaways:

1. Why ethics in AI is essential:• Algorithms can unfairly discriminate or misuse personal data.• Without transparency, we can't verify how or why AI makes decisions.

2. Three pillars of responsible AI (RAI):

   • Accountability: Systems must be explainable and auditable.

   • Responsibility: Designers and users should interpret outcomes and spot failures.

   • Transparency: Mechanisms of AI should be clear, understandable, and repeatable.

3. Bridging theory and practice:The paper surveys major ethical guidelines now in circulation (industry, academia, and government), notes where they overlap—and, importantly, where hard gaps remain—especially in real-world application across the AI lifecycle.

4. Gaps in implementation:Many ethical frameworks remain theoretical, lacking operational tools to guide AI development, deployment, and auditing. There’s a real need for practical RAI toolkits that span the development journey 

Read the full original paper here.