AI Ethics and Bias Mitigation

February 21, 2024
min read
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AI ethics and bias mitigation is this week’s topic in our five-part "AI for Newcomers" series. We have covered AI terminology, explored the best ways to learn AI in 2024, and discussed lead scoring with AutoML so far. It seems like the right time to start a conversation on responsible AI development and usage.

The Pillars of AI Ethics

Users and practitioners alike should understand and uphold ethical principles as AI technologies become an increasing part of everyday life. The application of ethical principles, values, and norms when developing and using artificial intelligence systems is known as Ethical AI.

These principles are:

Transparency

AI systems should operate transparently, making it possible for users to understand how and why decisions are made.

Fairness

AI must be developed to avoid unfair bias, treating all individuals and groups equitably.

Accountability

There must be clear accountability for the outcomes produced by AI systems, with mechanisms in place to address any issues that arise.

Privacy

AI should protect individuals' privacy, securely handling personal data and respecting data protection laws.

Safety and Security

AI technologies should be safe and secure, preventing harm to individuals or society and safeguarding against misuse.

Beneficence

The development and use of AI should aim to benefit and enhance human well-being and societal welfare.

Autonomy

AI should support and enhance human autonomy, allowing individuals control over their interaction with AI technologies.

Mitigating Bias

Bias in AI can come from the data it's trained on, often reflecting existing societal prejudices. Addressing this bias is essential for creating fair and equitable AI systems. To navigate this terrain, we adopt several strategies aimed at ensuring our AI systems are as fair and unbiased as possible.

Bias Mitigation Strategies

You can use three practical steps you can take to mitigate the risk of bias when building and using AI systems.

  • Collect data from varied sources to ensure AI systems are trained on datasets representative of all societal sections. This is known as Diverse Data Collection.
  • Build AI with algorithms that stakeholders can understand, fostering trust and accountability. Think of this as bringing transparency to algorithms.
  • Audit AI systems regularly for biases and make necessary adjustments to ensure outcomes are fair. Continuous monitoring is how you do this.

Wrap Up

Incorporating AI ethics and diligently working towards bias mitigation are more than regulatory requirements. They are essential practices to foster trust and fairness. As AI becomes a larger part of lives, adopting these ethical pillars takes on more importance. We need to ensure AI serves as a force for good, enhancing our lives and society at large.

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