Definition and Classification of AI Bias

Artificial intelligence bias is an anomaly caused by biased assumptions in the algorithm development process or bias in the training data.

What are the types of artificial intelligence bias?

1. Cognitive bias

The cognitive bias of artificial intelligence comes from developers unconsciously imposing their own ideas on the model, or using biased data sets for training. This bias is caused by unconscious thinking errors in personal judgment and decision-making as people try to simplify when processing information.

2. Lack of complete data

If the data set is incomplete, there will be bias.

Can artificial intelligence be completely free of bias?

From a technical perspective, as long as the data for training artificial intelligence is complete and unbiased, an artificial intelligence system can be built for unbiased data-driven decision-making.

However, in the real world, AI data sets rely on human thinking, and human biases continue to increase, which makes it difficult for AI to be completely fair and unbiased.

But we can fix biases in artificial intelligence algorithms through testing data and algorithms.

Steps to repair AI system bias:

1. Understand the algorithm and data to assess the risk of bias.

For example:

Check that the training data set is representative and large enough to prevent common biases such as sampling bias.

Conduct a subgroup analysis that involves calculating model metrics for specific groups in the dataset. This can help determine whether model performance is the same across subpopulations.

Monitor the model over time to prevent bias. The results of the algorithm will change as the learning or training data changes.

2. Establish a bias elimination strategy within the overall AI strategy that encompasses a series of technical, operational and organizational actions:

Technical strategy: Involves tools that can help identify potential sources of bias and uncover characteristics in the data that impact model accuracy

Operational Strategy: Use internal and third-party auditors to improve the data collection process.

3. Improve human-driven processes when identifying biases in training data.

Model building and evaluation can highlight biases that have long been of concern. In the process of building an AI model, these biases can be identified and this knowledge used to understand the cause of the bias.

4. Identify use cases where automated decision-making should be preferred and when humans should be involved.

5. Follow a multidisciplinary approach. Research and development are key to reducing bias in data sets and algorithms. Eliminating bias is a multidisciplinary strategy.

A data-centric approach to AI development can also help minimize bias in AI systems.

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