Ethical Considerations in Algorithms and Models
Bias in data science refers to systematic inaccuracies or distortions in data collection, analysis, or interpretation that result in unfair, discriminatory, or skewed outcomes. This can manifest in various ways, such as selection bias, where the data sample is not representative of the population; algorithmic bias, where machine learning algorithms perpetuate existing societal biases present in the training data; or confirmation bias, where researchers selectively focus on evidence that confirms their preconceived beliefs. Addressing bias in data science involves recognizing and mitigating these biases to ensure that data-driven insights and decisions are fair, reliable, and unbiased. This requires careful consideration of data sources, assumptions validation, algorithmic fairness evaluation, and ongoing monitoring and adjustment of processes to minimize bias and promote equity in data analysis and decision-making. What are some examples of this that you can identify from your experience?
Source: Emily Rothenberg, https://www.youtube.com/watch?v=cuIyc3oCVB0
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