Understanding Algorithmic Bias: Why It Happens and How to Address It

   1 December 2024  Leave a Comment on Understanding Algorithmic Bias: Why It Happens and How to Address It  Posted in 4. ‘Algorithmic Biases’ Or ‘The male gaze’

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• Define algorithmic bias: when AI systems produce unfair outcomes due to biases in data, design, or usage.
• Briefly explain why it matters: real-world consequences in hiring, lending, healthcare, and criminal justice.
• Highlight an example: e.g., facial recognition systems misidentifying minorities or biased job recommendations.

The video quickly know about what are Algorithms and how do they work?

What Is Algorithmic Bias?

Explanation: Bias arises when algorithms treat certain groups or situations unfairly.

Types of biases:

• Data bias: Incomplete or non-representative training data.
• Design bias: Flawed assumptions made during model creation.
• Outcome bias: When results reinforce systemic inequalities.

How Does Bias Enter Algorithms?

• Biased training data: Algorithms learn from historical data, which may reflect societal prejudices.
• Imbalanced datasets: Over-representing or under-representing groups.
• Modeling decisions: Simplistic metrics like accuracy can overlook nuanced fairness.
• Feedback loops: Algorithms amplify biases over time (e.g., biased policing datasets).

Real-World Examples of Algorithmic Bias

• Hiring systems: Algorithms favoring male candidates due to past hiring patterns.
• Healthcare disparities: AI systems underestimating the severity of illnesses in minority groups.
• Criminal justice: Predictive policing models disproportionately targeting marginalized communities.

Addressing Algorithmic Bias

• Diverse datasets: Collect data that represents all groups accurately.
• Transparent algorithms: Develop interpretable models and explain decisions.
• Regular audits: Periodically test for biases and adjust models accordingly.
• Multidisciplinary teams: Include ethicists, domain experts, and diverse perspectives in AI development.
• Regulations and standards: Encourage accountability through legal and ethical guidelines.

References:

  1. Jonker, A. and Rogers, J. (2024). Algorithmic bias. [online] Ibm.com. Available at: https://www.ibm.com/think/topics/algorithmic-bias.
  2. AI. “AI & Bias – What Are Algorithms and How Do They Work?” YouTube, 14 Sept. 2022, youtu.be/8tfKdxo8Rj8?si=SzmhiTkl9zOlCnPj. Accessed 1 Dec. 2024.

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