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The Elephant in your Dataset: Addressing Bias in Machine Learning - Michelle Frost
Addressing bias in machine learning models with techniques for mitigating unfair outcomes, considering biases and ensuring ethics and stakeholder involvement.
- Bias is a systematic error: in the model itself, due to the presence of sensitive characteristics.
- Predictive equality is important: not only in terms of accuracy, but also fairness.
- Equalized odds is a stricter concept: of fairness, which requires equal true positive and false positive rates across all groups.
- Data representation bias: occurs when data does not accurately represent the population or group being modeled.
- Learning bias: occurs when the model learns to prioritize one objective over another, potentially creating disparities.
- Historical bias: occurs when historical trends or biases are present in the data.
- Pre-processing mitigation techniques: can be used to correct bias, such as removing sensitive attributes or using pre-processing algorithms.
- Post-processing mitigation techniques: can also be used, such as reweighting or re-sampling.
- Equal opportunity is another definition: of fairness, which requires equal true positive rates across all groups.
- Fairness metrics: include accuracy, precision, recall, F1 score, and demographic parity.
- Biased data can lead to biased models: even if the model is really good at accurately predicting outcomes, it can still be biased.
- Fairness is important: because it can have a direct impact on individuals and society.
- Mitigating bias: is an ongoing process that requires continuous monitoring and improvement.
- Ethics plays a role: in mitigating bias, as it requires considering the potential impact on individuals and society.
- Stakeholders must be involved: in the process of developing and deploying AI systems to ensure fairness and accountability.
- Bias can be intentional or unintentional: and can occur through various means, such as flawed data or biased modeling assumptions.
- Fairness is complex: and requires considering multiple factors, including accuracy, precision, recall, F1 score, and demographic parity.
- Bias can be mitigated: through various means, such as pre-processing, post-processing, and re-sampling.
- AI systems can be biased: against certain groups or individuals, leading to unfair outcomes.
- biases in AI systems can be hidden: and may not be immediately apparent.
- Biases in AI systems need to be addressed: in order to ensure fairness and accountability.