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AI models may inherit biases from inputted data, leading to unfair or unbalanced outcomes. But AI models can be trained to identify and mitigate biases, improving fairness in research findings. |
| By focusing on bias mitigation, we can develop AI models that are not only fairer and more trustworthy but also more effective. This approach helps prevent discrimination and fosters a more inclusive environment, which in turn builds user trust. |
Steps to mitigate bias:
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Diverse Data Collection |
Ensure that the data used to train AI models is diverse and representative of the entire population. |
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Bias Detection and Correction |
Implement techniques to detect and correct biases in AI models. |
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Transparency and Explainability |
Make AI decision-making processes transparent and explainable. This helps users understand how decisions are made and identify potential biases. |
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Continuous Monitoring |
Regularly monitor AI systems to identify and address any emerging biases. This includes updating models with new, unbiased data and refining algorithms to mitigate bias. |
How does it happen?
The methods used to collect and utilise data in AI systems can introduce bias, and data generated by users can create output loops that perpetuate this bias (Naik et al., 2022).
Types of bias:
Sampling Bias: When the data collected is not representative of the population. For example, if a dataset used to train an AI model predominantly includes data from one demographic group, the model may not perform well for other groups (Chen et al., 2023).
Prejudice Bias: When the training data reflects societal stereotypes or prejudices. For instance, if historical data shows a preference for certain genders or ethnicities, an AI model trained on this data might perpetuate these biases (Chen et al., 2023).
When biased AI data is quoted as fact, it can (Ryan et al., 2023):
- Damage your reputation, along with the standing of your institution
- Hinder the projects of others, wasting time, resources and funds
- Negatively impact healthcare treatments/law and outcomes
- Contribute to the spread of misinformation
- Reduce public trust in science
- Result in article corrections, or even retractions