Enterprise AI Analysis

The second map groups the ethical problems according to whether they relate to the outcome of AI sentencing, the process of AI sentencing, or the overall criminal justice system. This analytical framework highlights the different loci of ethical concern.

Outcome-Based Problems:

Problems related to the outcome directly concern whether AI can determine sentences that are correct according to some standard and fulfill the expected functions of human-meted sentences.

• Accuracy Problem: Concerns the AI's ability to achieve identified ethical standards in sentencing.
• Fairness Problem: Addresses whether the distribution of sentences will be more or less fair, considering issues like bias and noise.
• Equity Problem: Focuses on AI's capacity to handle 'hard cases' and make just exceptions, requiring prudence.
• Censure Problem: Explores whether AI sentences can effectively express societal condemnation for criminal offenses.

Process-Based Problems:

These problems relate to the procedural aspects of AI sentencing, particularly how decisions are made and communicated.

• Transparency Problem: Asks if AI sentencing makes the process less transparent, especially regarding how conclusions are reached ('black box' problem).
• Justification Problem: Deals with whether AI-generated sentences can be properly justified with non-arbitrary, principled reasons.
• Responsibility Problem: Examines who should be held responsible for AI-determined sentences, given AI's lack of knowledge and control.

System-Based Problems:

These challenges affect the criminal justice system as a whole, often arising from aggregate effects or broader societal values.

• Assessment Problem: A meta-problem concerning how to measure and evaluate AI sentencing improvements against ethical standards.
• Dignity Problem: Questions if AI sentencing is compatible with treating offenders and parties with respect for their rational autonomy.
• Legitimacy Problem: Addresses whether AI sentencing will be perceived as legitimate by stakeholders, impacting compliance and trust.
• Private vs. Public Problem: Concerns the compatibility of privately developed AI with public criminal justice values, including influence and data privacy.
• Transformative Effects Problem: Explores how widespread AI use might gradually transform criminal justice, potentially eroding core human values or altering the nature of punishment.
• Implementation Problem: A meta-problem focusing on the ethically best way to integrate AI into sentencing, considering various models (e.g., autonomous vs. advisory).

AI Agency: Autonomous vs. Advisory Impact

This map differentiates ethical problems based on the level of AI autonomy in sentencing. Some problems are exclusive to systems where AI acts as an autonomous 'robojudge,' while others are relevant even when AI functions purely in an advisory capacity to human judges.

For instance, the ability of AI to handle 'hard cases' (Equity) becomes a distinct problem only when an AI makes the final decision, as a human judge could override AI advice. Similarly, the communicative function of censure and the direct ascription of responsibility are more critically undermined in fully autonomous AI systems. However, fundamental challenges like the overall assessment of AI's effectiveness or its impact on the system's legitimacy remain pertinent regardless of AI's agency level.

Ethical Domains: Penal Specific vs. General AI

This map distinguishes between ethical problems unique to AI in criminal sentencing, requiring insights from penal ethics, and those that are general to any AI application in public governance. The unique nature of criminal sentencing, involving state-sanctioned force and the expressive function of punishment, gives rise to specific dilemmas.

For instance, the 'Censure' problem, concerning how punishment expresses societal condemnation, is intrinsically tied to penal theory. Similarly, the concept of 'Dignity' in the context of criminal justice involves specific considerations about reciprocity between the state and the accused. In contrast, issues like 'Transparency,' 'Justification,' and 'Responsibility' are fundamental to all forms of AI in public administration, whether it's for tax collection or social benefits, and can be addressed by general ethical frameworks.