Summary of What’s the Problem, Linda? the Conjunction Fallacy As a Fairness Problem, by Jose Alvarez Colmenares
What’s the Problem, Linda? The Conjunction Fallacy as a Fairness Problem
by Jose Alvarez Colmenares
First submitted to arxiv on: 16 May 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores how artificial intelligence (AI) can learn from human cognitive biases to create more accurate automated decision-making systems. Building on work by Daniel Kahneman and Amos Tversky, researchers have discovered that humans often make irrational decisions, such as ranking a conjunction over one of its parts, violating basic probability laws. The “Linda Problem” is a classic example of this bias, where people are more likely to believe Linda is a feminist activist than just a bank teller. However, the authors argue that AI researchers have overlooked the driving force behind this bias: societal stereotypes about women like Linda. This paper reframes the Linda Problem as a fairness issue and introduces perception as a key factor using the structural causal perception framework. The proposed conceptual framework has potential applications in developing fair AI decision-making systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how artificial intelligence can learn from human mistakes to make better decisions. You might know that humans often make irrational choices, like thinking Linda is more likely to be a feminist activist than just a bank teller! This mistake is called the “conjunction fallacy.” Researchers have studied this phenomenon and found that it’s connected to how people think about women. The authors of this paper want to fix this problem by looking at things from a different perspective. They’re trying to create AI systems that make fair decisions, not biased ones. |
Keywords
» Artificial intelligence » Probability
