Gains:
- Ability to clearly define the concepts of agency, responsibility gap, algorithmic bias, transparency and autonomy and establish tensions.
- Placing responsibility not on 'artificial intelligence' but on the human chain that designs, deploys and uses it
- Ability to conduct a multi-sided debate by checking the AI's optimistic bias and outdated knowledge of its own technology
Philosophy and applied ethics are fields that not only use AI but also study AI. Artificial intelligence itself is one of the liveliest topics in ethical philosophy today: Who is responsible when an AI system makes a decision? If an algorithm is biased, how is this injustice called? Can AI be considered an "agent" (subject capable of moral action)? As an ethicist, you are the person who both conducts these discussions and teaches. In this unit, we will clarify the basic concepts of the AI ethics debate (the branch of applied ethics that examines the moral questions—responsibility, bias, transparency, autonomy, justice—raised by the development and use of artificial intelligence systems) and see how you can better analyze and teach this debate with AI tools.
Here's an interesting loop: You can use AI as a tool even when discussing AI ethics — but that's exactly where you need to be most alert to AI's own biases and limitations.
Basic concepts and tensions
Agent and moral subject. Intention, understanding, freedom, and awareness of consequences are often required for a being to bear moral responsibility. Whether AI has these is debatable; Most views hold that AI is a tool and that responsibility remains with the humans who design, deploy and use it. This aligns with the principle that the module emphasizes throughout: final responsibility lies with the human.
Responsibility gap. When an AI system causes harm, situations may arise where responsibility is dispersed (“left in the void”): the designer, the provider of the data, the institution using it, the ultimate operator? This “responsibility gap” is one of the central problems of contemporary AI ethics.
Algorithmic bias. An AI can learn and replicate historical inequalities in training data; This algorithmic bias (when a system systematically produces outcomes that disadvantage certain groups) is at the core of discussions of fairness and discrimination.
Transparency and explainability. Failure to understand why an AI decision is the way it is (the “black box” problem) is an ethical problem in terms of liability and the right to appeal. Explainability (making the rationale for a system's output humanly understandable) has increasingly become an ethical and legal requirement.
Tip: In discussing AI ethics, separate the concepts clearly: Instead of saying "The AI made the decision," say "The AI produced an output, the human implemented it." This linguistic rigor places responsibility in the right place and saves the discussion from murkiness.
Using AI as a tool in this debate
AI itself is great illustrative material in AI ethics courses. You can have students find bias in an AI output, demonstrate the unjustification of a decision, and compare different opinions. But two caveats: (1) AI may have a commercial or optimistic bent about its technology; Be sure to look for critical opinions as well. (2) This field changes rapidly; AI's information may be out of date, go to current source for regulations and facts.
Caution: When asking AI “Is AI ethical?” When you ask a question like this, the answer you get is the average of the training data, not the result of a philosophical discussion. Take this answer as a starting point; Define the concepts yourself, establish the tensions yourself, and support them yourself with current real examples.
three mini cases
Case 1 — Responsibility gap debate. One seminar discussed a scenario where an AI-powered recruiting system left a group at a disadvantage. Ask the AI "who is responsible?" They had the question analyzed from different frameworks; The output mapped the designer-data-institution-operator chain. The group used this map to develop its own reasoned position: they advocated accountability of each link so that responsibility did not fall into the "void".
Case 2 — Prejudice demonstrated with a living example. An instructor had the AI write the same job description with different names and showed the stereotypes in the printout to the students. Students saw algorithmic bias not as an abstract concept, but concretely in the output in front of them. The lesson ingrained the concept with a real example. The lecturer also emphasized that this was a single outcome and systematic evidence was required for generalization.
Case 3 — The optimistic trend has stabilized. A student had AI write about "AI's impact on society"; The output was predominantly positive and understated the risks. The student went to critical academic sources, added opposing views, and constructed a balanced analysis. AI's bias towards its own domain is balanced by human control.
Weak prompt / Strong prompt
Weak prompt:
Is artificial intelligence ethical or not? Answer.
This prompt reduces a complex philosophical debate to a binary answer; AI returns the average, concepts remain undefined and bias becomes invisible.
Powerful prompt:
Your role: An unbiased analyst in the field of AI ethics. DON'T JUDGE ME on [Scenario]. Instead:1) Define relevant concepts (perpetrator, liability gap, algorithmic bias, transparency) — concise and neutral.2) Map the possible distribution of responsibility (designer, data, institution, operator).3) Present at least two opposing ethical positions, each at its strongest.4) Note that this field is changing rapidly and requires up-to-date resources.I will make my own judgement. Screenplay: [write the script]
This prompt clarifies concepts, establishes a multi-sided discussion, and leaves the decision up to you.
Four copyable templates
1) Concept clarification:
Briefly and objectively define the following concepts in AI ethics: agent/moral subject, responsibility gap, algorithmic bias, transparency, explainability. Include an everyday example under each definition. Mark controversial areas as "opinions differ."
2) Responsibility mapping:
In the following scenario, draw the chain of responsibility for the damage produced by an AI system: designer, data provider, distributing institution, user operator, regulator. Write down the possible stake and defense of each ring. Make a final judgment. Screenplay: [script]
3) Opposite position presentation:
Present at least two opposing ethical positions on [the AI ethics question], each in its strongest and most honest form. Declare none a "winner." Name the value on which each position is based. Question: [question]
4) Optimism/bias control:
Check whether the following text about AI underestimates the risks or conveys an optimistic disposition towards the technology. Mark any critical perspectives that are missing and risks that need to be added. Text: [paste text]
Concept / voltage table
concept
basic question
ethical tension
Perpetrator/moral subject
Does AI bear responsibility?
Tool or subject?
responsibility gap
Who is responsible when damage is caused?
Dispersed accountability
algorithmic bias
Disadvantage to whom?
Justice vs. efficiency
transparency
Why this decision?
Explainability etc. complexity
autonomy
How much human control?
Automation vs. audit
Common mistakes
- “AI decided” language. It blurs responsibility; AI produces output, humans implement it, and the responsibility lies with humans.
- Reducing the debate to a binary answer. "Ethical or not?" question destroys conceptual depth; Set up voltages.
- Not noticing the optimistic tendency of AI. AI can be unstable about its own technology; Add critical source.
- Generalizing a single outcome. Bias in a sample is striking, but a systematic claim requires systematic evidence.
- Relying on outdated information. This field and its arrangements change rapidly; Confirm facts with current source.
In summary
The ethics of artificial intelligence is one of the most vibrant contemporary areas of philosophy, and as an ethicist you both conduct and teach this debate. Clearly define key concepts — agency, accountability gap, algorithmic bias, transparency, autonomy — and establish tensions. You can use AI as both tool and example in this discussion, but beware of two pitfalls: AI tends to be optimistic about its technology, and its knowledge may be outdated. Language such as “AI decided” blurs responsibility; Rigorous conceptual language puts responsibility in the right place. You always make the decision.
Application task
- Choose an AI ethics scenario (e.g. an algorithmic decision system).
- Have key concepts defined and reviewed yourself with the "concept clarification" template.
- Extract the chain of responsibility in case of damage with the "responsibility mapping" template.
- Compare at least two ethical positions with the “opposing position presentation” template.
- Test the bias in an AI text with the “optimism check” template and add a critical source.
checklist
- [ ] I defined the concepts clearly and objectively.
- [ ] I placed the responsibility on the human chain, not on the “AI.”
- [ ] I structured the discussion multi-sidedly, I did not reduce it to a binary answer.
- [ ] I checked the AI's optimistic disposition towards its field.
- [ ] I have confirmed the facts and regulations with the current source.
- [ ] I produced the final judgment and justification myself.