Artificial Intelligence Doesn't Care if it's Right or Wrong: Understanding the Nature of AI Decision-Making
Artificial intelligence (AI) is playing an increasingly central role in various sectors. As AI systems become more advanced in “finding solutions” or “making decisions,” an important concept reminder: AI does not care whether it’s right or wrong.
AI does not have a built-in sense of morality or concern for being "right" in a human sense. AI systems are designed to process data, identify patterns, and generate outputs based on available algorithms. The results are neither correct nor incorrect. Any evaluation of being right or wrong is a matter of objective evaluation based on agreed-upon criteria. As we begin to rely more on AI, it is worth understanding the nature of it, how it produces outputs, and what that means about how much we can rely on it as a tool.
The Lack of Sentience in AI
AI, as it stands today, does not possess consciousness or self-awareness. It is not sentient, meaning it has no inner life, desires, emotions, or subjective experiences. Unlike humans, who have complex cognitive processes, emotions, and ethical frameworks that guide their decisions, AI systems are built around mathematical models, algorithms, and data processing. These systems are engineered to follow specific instructions and optimize for a given objective, but they do so without any understanding of what they are doing or the consequences of their actions in an emotional or ethical sense.
AI and the Problem of Right vs. Wrong
When we speak of AI being "right" or "wrong," we often refer to whether the machine produces a desirable or undesirable result. Whether it is “desirable” or “undesirable” is something only a human can determine. So, the lack of moral or even evaluative consideration is what separates AI from humans. Humans care about being right or wrong because we experience consequences. AI has no ability to feel anything. It is not vain or humble. It is not confident or unsure. So, if we are to use AI as a tool, we should know from what information it draws, and we should be able to evaluate the accuracy of outputs.
The Role of Training Data and Objective Functions
AI systems are usually trained on large datasets. These datasets come with certain biases, assumptions, and sometimes imperfections—whether intentional or not. The algorithms built into AI models learn patterns from this data and make predictions or decisions based on those patterns. If the data is flawed or biased, the AI's conclusions will be flawed or biased as well. However, this does not indicate that the AI "cares" about being wrong. Instead, it means that the AI is following the instructions it was given during training, regardless of whether those instructions lead to a correct or ethical outcome.
AI as a Tool, Not a Moral Agent
The essential point here is that AI is a tool, not an autonomous human agent with moral reasoning. Humans are responsible for defining the rules, training the models, and setting the objectives that guide AI decision-making. When AI systems produce undesirable outcomes, it is a reflection of the limitations and biases in the data or in the design of the algorithm—not a moral failing on the part of the machine.
This distinction is critical as we consider the ethical implications of AI in areas like criminal justice, healthcare, and employment. While AI can be a powerful tool for making decisions and optimizing processes, it cannot be trusted to automatically align with human ethical standards unless those standards are explicitly encoded into the system's design.
In practice, AI systems are often evaluated based on objective measures such as accuracy, efficiency, or profitability, not on ethical considerations. In some cases, this might result in "correct" outcomes from a purely functional perspective, but these outcomes might conflict with broader societal values. For example, an algorithm designed to optimize for profit might end up recommending business strategies that exploit vulnerable populations without any awareness or care on the part of the AI.
The Consequences of AI’s Indifference to Right and Wrong
One of the primary concerns with AI systems not caring about being right or wrong is the potential for negative consequences when they are deployed in high-stakes domains. For instance:
- Bias and discrimination: If an AI system is trained on biased data, it can perpetuate or even amplify those biases, leading to unfair outcomes in areas like hiring, criminal sentencing, or lending decisions.
- Lack of accountability: When an AI makes a mistake, it is not yet clearly defined who should be held accountable—the developers, the organizations that deploy the technology, or the technology itself. This lack of accountability can be problematic, especially in critical areas like healthcare or autonomous driving, where mistakes can lead to harm.
- Unintended consequences: Because AI systems do not understand the broader context in which they operate, they may inadvertently create harmful outcomes. For example, an AI algorithm optimized to maximize engagement on social media platforms may promote sensationalist content, even if that content contributes to misinformation or societal polarization.
To mitigate these risks, it is essential for developers and regulators to implement safeguards, ethical frameworks, and transparency mechanisms. The AI itself, however, cannot be expected to self-correct or act in accordance with ethical principles unless those principles are programmed into its design.
Conclusion: AI in Financial Planning
As AI continues to play a larger role in our lives, it is crucial to remember that AI is not an independent moral agent. Our responsibility is to ensure that AI is used in ways that align with ethical principles, human values, and accuracy. In this sense, we must focus not only on improving the performance of AI systems but also on embedding human oversight, fairness, and transparency into their design and deployment. AI may not care if it is right or wrong, but we must.
A financial advisor does care about being right or wrong. A financial advisor has training, experience, and expertise related to financial planning. Related to finance, an advisor can understand the difference between what is plausible and what is accurate because they are a knowledgeable editor of the material. Without a human to fact-check, a tool like AI can theoretically do more harm than good.
We may want to believe that artificial intelligence can provide us with the answers we need so we won’t have to do our own time-consuming analysis. But there needs to be arbiters. The ramifications of this are clear in all areas of life. But in finance the impacts can be great.
It is helpful to have an expert verify and analyze before relying on information. Someone could be creating a poor investment strategy using AI and convince many people to adopt it. Soon enough, the algorithm will point toward said investment scheme. If it’s wrong, there is no one to discuss what went wrong or how to fix it. That is where a qualified advisor comes in. A fiduciary has an obligation to care and to make decisions in his/her clients' best interest. A good advisor develops a relationship with clients to ensure that appropriate risks are being considered in light of the diverse and intricate requirements of each individual client.
Written by Doug Thalhammer