Big data ethics, machine ethics or information ethics? Navigating the maze of applied ethics in IT

Multiple ethical frameworks exist for technology, but they're often confusing and overlapping. The paper identifies several branches of applied ethics (big data ethics, machine ethics, information ethics, AI ethics, and computer ethics) that developers encounter, highlighting the need for clearer guidance on which framework applies to different technological contexts.

Normative ethics provides the foundation for applied ethics in technology. The paper establishes that normative ethics (which examines what is right and wrong) must precede applied ethics, meaning developers need to understand underlying ethical principles before applying them to specific digital products and systems.

Context matters when selecting an ethical approach. Different technological systems require different ethical considerations—the paper demonstrates this through a facial recognition software example in medicine, showing that the same technology raises distinct ethical questions depending on its application domain.

Development teams need practical guidance, not just theory. The paper addresses a real gap: software developers, designers, and decision-makers are increasingly expected to conduct ethical evaluations, but they lack clear frameworks for navigating the various branches of applied ethics available to them.

Ethical analysis should be systematic and structured. By organizing the different ethical subfields and demonstrating how normative analysis can be conducted (using the facial recognition example), the paper suggests that ethical evaluation can be made more methodical and accessible to non-ethicists working in technology development.