AI - Limits and Prospects of Artificial Intelligence

The exploration of AI's boundaries isn't just a technical discussion— “AI - Limits and Prospects of Artificial Intelligence” raises critical questions about the balance between technological progress and fundamental human values. 

One of the key issues raised by the book is the "accuracy-interpretability trade-off" in AI (p. 231). This trade-off refers to the inverse relationship that often exists between the accuracy of an AI model and its interpretability. In other words, the more complex and accurate an AI becomes, the more difficult it is to understand how it arrives at its decisions.

At the heart of this issue lies the difference between "white box" and "black box" AI systems

• White-box systems are transparent; their inner workings are readily accessible and understandable. Traditional rule-based systems or simpler machine learning models like decision trees are examples of white-box AI (p. 231).
• Black-box systems, on the other hand, are opaque. They may achieve impressive results, but the reasoning behind those results remains hidden. Deep learning models, especially neural networks, fall into this category due to their complex architecture and numerous connections (p. 230).

The challenge arises because the most accurate AI models often involve complex algorithms and deep learning techniques that function as black boxes. While these models can sift through vast datasets and identify subtle patterns, their decision-making processes are often non-transparent, even to their creators. This lack of transparency creates significant problems, especially within the legal context.

The Black Box Problem

The lack of transparency in Black-box systems is not just an academic concern; It has significant implications in legal applications where understanding why a decision was made is as important as the decision itself. Consider, for example, an AI system used in sentencing or parole decisions. If the system denies parole, how can we ensure the decision is fair if the logic behind it is hidden?

The problem is further complicated by the fact that AI systems, especially those trained on large datasets, can incorporate and amplify existing societal biases (p. 8). As the authors explain, if the data sets used for training an AI contain social discrimination, the AI will inevitably reproduce and even reinforce these prejudices. This means that AI can become a tool for perpetuating existing inequalities, even when those biases are not part of the algorithm itself. It also means that even if the results of the model appear to have high accuracy, that accuracy may be skewed and may not apply equally to everyone.

Foucault and the Disciplinary Power of AI

The Foucauldian idea that power is not just exercised through overt force or explicit rules but also through more subtle mechanisms that shape behavior and thought would hold that AI systems, with their ability to analyze and categorize vast amounts of data, have the potential to become powerful tools of disciplinary power. If these systems are used in law enforcement, for instance, they can create a sense of constant surveillance leading to self-regulation and compliance with the perceived norms, even when those norms may be biased or discriminatory. 

Moving Towards Transparency

Given these issues, it is clear that a balance must be struck. The "accuracy-interpretability trade-off" is not a hurdle that can be easily overcome. It requires a multidisciplinary approach that draws from computer science, law, ethics, and social sciences (p. 148). While high accuracy in AI models is desirable, it cannot come at the cost of transparency and fairness. There is growing research into "explainable AI" (XAI), that aims to make the inner workings of AI systems more transparent (p. 223). However, much of this work is targeted at experts, and there is still a need for ways to explain AI decision-making to non-experts (p. 163). 

The integration of AI in the legal system offers many possibilities for progress, but also poses considerable risks. The "accuracy-interpretability trade-off" is not just a technical problem; it is a fundamental ethical challenge that goes to the very heart of justice. It will be crucial to prioritize transparency, fairness, and accountability while actively addressing this trade-off, to safeguard the core principles of the legal system.

AI : limits and prospects of artificial intelligence Klimczak, Peter, editor. ; Petersen, Christer, editor. 2023, Book , 288 pages. 9783837657326