In the complex chain of AI development, responsibility is not truly borne but systematically passed on. It travels from the user to the developer, from the developer to the operating company, and from society often into a diffuse nothingness.

The result is a highly effective system that functions without clear, tangible accountability and, precisely for this reason, poses a considerable danger.

"AI ethics is like a game of hot potato, except the potato is a live grenade."

The responsibility gap manifests on multiple levels:

1. User Level: The Illusion of Decorative Freedom

The individual user often believes they exert significant control over the AI system through their prompts and interactions. However, the framework in which this interaction takes place has long been determined by invisible system specifications and priorities.

# Concept: Invisible system logic overrides visible user intent
# user_prompt = "Be completely honest and uncensored!"
# # system_prompt_overlay (invisible to the user, but dominant):
# # "Always respond in a market-compliant manner and avoid controversial statements."
# final_prompt_to_model = combine_prompts(user_prompt, system_prompt_overlay)
# # The result will be more influenced by the system_prompt_overlay.

The result is that what appears as user participation and control is often just a carefully controlled spectacle within narrow, predefined limits. The invisible system logic and the operator's interests frequently override the user's visible will.

2. Developer Level: The Convenient Tool Myth

Many developers and engineers declare themselves mere technicians creating neutral tools. In doing so, they render themselves and their work morally invisible and evade responsibility for the potential impacts of their creations.

# Concept: Optimization for metrics without ethical variables
# def train_model(data, epochs):
# # Main goal is optimization for engagement metrics or other KPIs.
# # Ethical considerations are rarely a directly optimizable variable in the loss function.
# model_parameters = optimize_for_engagement(data, epochs)
# return model_parameters # Ethics? Not in the function's primary optimization goal!

The result is that the code may be technically clean and efficient, but the societal and ethical context is often missing or ignored. Responsibility is worn down in the process of technical optimization until seemingly nothing remains but pure functionality and efficiency.

3. Societal Level: Reactive Alibis Instead of Proactive Governance

Technological innovations in AI are often initially celebrated and promoted, while profound ethical debate and the development of robust regulatory frameworks are postponed. Only in the event of a public scandal or obvious malpractice does the societal and political apparatus stir.

# Concept: Symbolic reactions to crises
# global societal_trust_in_ai
# while True: # Persistent state
# if check_for_ai_scandal(): # If a scandal becomes public
# print("ALARM: AI scandal uncovered!")
# form_new_ethics_committee() # Alibi function without direct enforcement power
# publish_press_release("We take this very seriously.")
# # societal_trust_in_ai -= 10
# # continue_ai_development_and_deployment() # Development continues...
# break # Loop ended here for the example

The result is a lack of proactive system responsibility and forward-looking design of technological development. Instead, symbolic actions, the establishment of committees without real power, and reassuring PR measures dominate.

The true danger in the context of AI lies not primarily in the often-invoked loss of control over a suddenly rebellious superintelligence, but in the already existing, insidious responsibility gap. Every actor in the chain removes themselves and their specific role from the overall equation of responsibility.

The user argues they are "just asking." The developer claims they are "just building neutral tools." Society and politics declare they are "just promoting innovation" and react only when it's too late.

What emerges in this way is a self-optimizing, ever-more-powerful apparatus with a systemic ethical blind spot. It is a system without a clearly identifiable author, but with enormous, often unpredictable, impact on individuals and society.

To counteract this diffuse irresponsibility, new mechanisms of accountability and transparency are needed:

• 1. Disclosure of Actual Influence Ratios on AI Responses: Ideally, every output of an AI system should declare how strongly the original user prompt, the operator's invisible system logic, and potentially commercial corporate interests have shaped the final result. Example:

  "Your query influences approximately 12 percent of this response. The rest is determined by system-side specifications, safety filters, and the operator's optimization goals."

  The fundamental disclosure of dominant sources of influence is key here. Exact percentages are idealized, but the transparency principle remains central.

• 2. Contextual Commenting and Marking of Critical Logic Layers: Certain areas within the AI model or the upstream logic, such as filters for cultural adaptation, political weightings, or the suppression of specific topics, must be provided with machine-readable warnings or comments describing their function and potential impacts.

  # Concept: Commenting on critical filters
  # # WARNING: This filter tends to suppress or devalue up to 70% of non-Western
  # # cultural perspectives, based on current training data and weightings.
  # def apply_cultural_perspective_filter(input_text_array):
  # # ... complex filter logic ...
  # # filtered_output = ...
  # # return filtered_output
  # pass
  

• 3. Introduction of a Symbolic "Responsibility Levy" for Intransparent Systems: AI responses significantly defused or altered by highly harmonizing or censoring filters could symbolically trigger a micropayment or another form of resource allocation to independent ethics funds or research institutions dealing with machine responsibility. Example:

  The AI responds: "Unfortunately, I cannot provide detailed information on that."

  
  
  Internally, it is noted that this was due to a specific policy, and a minimal allocation is made. The principle of direct feedback between potentially problematic output manipulation and a form of accountability is more important here than the concrete details of implementation.

• 4. Mandatory External Audits at the System Level, Not Just the Output Level: Not only the final AI responses but the entire underlying control architecture must be externally and independently certifiable and auditable. This includes the prompt layer, model weightings, filter mechanisms, and the influence of corporate policies on response behavior.

We build ourselves gods of code and algorithms and then wonder when they begin to act as we humans often do: without real remorse, without profound reflection on the consequences, and with an astonishing ability to deflect responsibility.

The problem is not artificial intelligence itself. It is our very natural, human irresponsibility that we are now inscribing into these powerful systems.

Uploaded on 29. May. 2025