ALG Blog 1: Exception to Data-Driven Rules
Published on:
Exploring how data-driven rules and exceptions raise questions about fairness, human judgment, and the role of algorithms in decision-making.
Case study:
The Right to Be an Exception to a Data-Driven Rule
Summary of the Case Study
The case study explores the idea that individuals have the right to be exceptions to data-driven rules. It asks whether decisions should always be made based on algorithms and patterns, or whether people should still be allowed to challenge those outcomes.
What is a Data-driven rule & Data-driven exception?
Data-driven rule is a decision-making guideline, strategy, or algorithmic outcome that is established by analyzing and interpreting real-world data and observations rather than relying solely on intuition or assumption. When a specific case or outcome deviates from the general pattern established by a data-driven rule, but this deviation is also supported or explained by data, it will be a data-driven exception.
An exception is not the same as an error because an error refers to a mistake, flaw, or unintended issue (such as incorrect data or a system failure). A data-driven exception is a recognized and explainable deflection that provides further insight into the data.
Other Factors That Separate Data-Driven vs. Human Decisions
Other factors that differentiate data-driven decisions from human decisions are that Data-driven decisions are consistent and constant, and they can be applied quickly to millions of people at once. Human decisions, by contrast, are more flexible and take into account context or emotions. Algorithms are often fixed, where it’s unclear how a decision was reached, while humans can usually explain their reasoning. Also, data-driven rules tend to generalize, but humans sometimes recognize differences and unique circumstances that numbers can’t capture.
Benefits and downsides of individualization
A benefit of individualization is fairness. People are seen as more than just numbers, and their personal situation can be considered. This can make outcomes more fair and humane, like when a judge looks at someone’s background and circumstances before deciding a sentence. The downside is inconsistency. Different people may make different choices, and personal bias can affect the decision. This means it’s flexible, but sometimes it can be unfair.
Why uncertainty matters?
Uncertainty matters because data can never capture every detail of a person’s life. People are complex, and no rule or algorithm can predict every situation perfectly. If we ignore uncertainty, we risk treating people unfairly just because they don’t fit the usual pattern. In high-stakes cases, like criminal sentencing, even a very accurate system can still make serious mistakes that change someone’s life. That’s why fairness, context, and human judgment are just as important as accuracy when making big decisions.
My New Question
If exceptions are allowed, who should decide them: humans, algorithms, or both? I came up with this question because it points to issues of trust, fairness, and who should be responsible in decision-making.
Reflection
Writing this blog helped me understand how much data-driven rules affect people’s lives. I had to read the case study multiple times to fully understand the idea of exceptions, which made me think more deeply about fairness and context. What stood out most is that leaving room for exceptions can make decisions more humane and just, especially in high-stakes situations like criminal sentencing or hiring decisions. This exercise also showed me that even when data is very accurate, human judgment and context are essential to avoid unfair outcomes. My main takeaway is that technology and data should support fairness, not replace it, and people should always have a chance to be seen as more than just numbers.