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Mitigating Bias in AI Systems
Artificial Intelligence bias is the result of human biases, as people select the training data for machine learning algorithms
and determine how the algorithm’s results should be applied. Therefore, people must advocate for ethical AI and find
ways to mitigate bias. Data science leaders, policymakers, and other key stakeholders are now prioritising ways to
mitigate bias and limit the potentially negative impacts of automated decisions in real-world applications.
There are several reasons to alleviate bias in AI systems. First, when you have a bias in your AI system, you amplify
whatever problems you have. Unfairness and discrimination are bad on their own, and when you add an AI system
on top of them, they get worse. For instance, using biased algorithms for hiring can disadvantage certain groups and
perpetuate systemic discrimination. Second, when AI systems are biased, people lose trust in technology. If you don’t
trust your AI system to behave fairly towards you, you may choose not to interact with it at all. And this can be detrimental
not only to you but to everyone. Finally, we strive to tackle bias because it’s inherent to certain ethical principles and we
want to ensure AI systems are developed and used responsibly.
Strategies for Mitigating Bias
There are several strategies and techniques for mitigating bias in AI systems:
• Ethical Guidelines and Policies: Formulate ethical guidelines and policies Ethical Guidelines and Policies
to ensure fairness and mitigate biases in decision-making. Always keep
provisions for regular review and update of guidelines to address new issues. Diverse Data
• Diverse Data: To reduce bias, we should use lots of different kinds of
information to teach AI. So, AI can learn from many different examples and Data Transparency and Quality
viewpoints, making it less likely to be biased.
• Data Transparency and Quality: By maintaining transparency of the data Bias Testing and Auditing
inputs that are fed into the decision-making process and maintaining data
quality by continuously checking data sources for potential biases. Algorithmic Fairness
• Bias Testing and Auditing: Regularly conduct testing and auditing to
identify biases in algorithms, models, or decision-making processes. This Accountability and Transparency
should be done regularly, and systems updated to mitigate biases detected.
Feedback Mechanisms
• Algorithmic Fairness: We can make AI systems fairer by using special
algorithms that are designed to be fair. These algorithms make sure to Continuous Improvement
consider fairness when making decisions, helping to reduce bias.
• Accountability and Transparency: Ensure accountability of decision makers for their decisions and potential biases.
• Feedback Mechanisms: Create feedback mechanisms that allow stakeholders to flag potential biases in the
decision-making process. Actively seek and consider feedback from stakeholders to mitigate biases and ensure
fairness and inclusiveness.
• Continuous Improvement: Regularly evaluate decision-making processes for biases and establish mechanisms for
continuous improvement based on feedback, data, and emerging research.
Developing AI Policies
Creating rules for AI is crucial to ensure its ethical and fair use. Clear guidelines are necessary regarding the deployment
of AI, with consideration given to everyone's input in the rule-making process. Before using AI, we should check for
any problems and have plans to fix them.
AI Ethics and Values 409

