Cherry Picking: Selective Truths and the Dilemma of Bias?
Cherry picking behaviour has surged in relevance in the realm of artificial intelligence research. It is the act of choosing examples of the best or most desirable predictions. Its main issue is the right, or wrong bias that is reflected in it. The term "best or most desirable" is subjective, reflecting what the cherry-picker deems advantageous. Whether selecting positive or negative predictions, the act remains cherry-picking.
The term is based on the perceived process of harvesting fruit, such as cherries. Just as a picker selects the ripest and healthiest fruits, cherry pickers focus on data or information that aligns with their beliefs, potentially skewing perceptions. ?Observers who see only the selected fruits may thus wrongly conclude that most, or even all, of the tree's fruits are in a likewise good condition.
In an era, rife with fake news, alternative facts, and hyper-partisan politics, combating flawed thinking patterns has become imperative. Developing critical thinking skills is key, as individuals tend to reinforce their beliefs despite contradictory evidence.
Several prominent theories underpin cherry-picking behaviour:
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Cherry picking often intertwines with other logical fallacies, such as strawman arguments, which misrepresent opposing views to facilitate attacks. This technique, known as quote mining, involves cherry-picking quotes out of context to distort original intentions.
Cherry-picking behaviour permeates diverse domains, from politics to religions to business and daily life, amplified by social media's influence. It underscores the urgent need for critical thinking integration into societal and educational frameworks. Critical thinking acts as a beacon, illuminating pathways to reasoned discourse and informed decision-making, essential in navigating complex narratives and biases.
Head of Asset Management at Al Qattara Company
10 个月Well described.