The article describes that one of the inherent limitations of AI is its inability to make correct decisions when encountering situations not covered in its training data. In such cases, an AI system is likely to fail or make incorrect predictions. This limitation is significant because AI systems are trained using data points from the past, and if they come across something new that is not similar to anything in the training data, they cannot adapt or improvise like humans. Instead, they rely solely on the knowledge they have gained from their training. The article highlights that there is not much that can be done about this problem, apart from trying to train the AI for all possible circumstances that we know of, which can sometimes be an insurmountable task.
The article provides several specific examples of AI failures in real-world applications:
These examples highlight the potential dangers and shortcomings of AI systems, such as inaccuracy in real-world settings, bias in training data, and being out of date with the dynamics of the problem they are meant to address.
Bias in AI training data refers to the presence of prejudiced assumptions or discriminatory patterns in the data used to train machine learning algorithms. This bias can stem from various sources, such as incomplete or unrepresentative data, historical prejudices, or even the algorithms themselves. When an AI system is trained on biased data, it can lead to skewed results and decision-making processes that perpetuate or amplify existing biases.
For example, if an AI model is trained on historical data that contains biases against certain groups, it may learn to make decisions based on those biases rather than objective factors. This can result in discriminatory outcomes, such as denying opportunities to qualified individuals or perpetuating harmful stereotypes.
Bias in AI training data can manifest in several ways, including:
Data sampling bias: This occurs when the training data is not representative of the real-world population it is intended to represent. For instance, if a dataset only includes data from a specific geographical region or demographic, the AI model may not generalize well to other regions or demographics.
Measurement bias: This arises when the data collection process itself is flawed or biased. For example, if the data is collected using subjective measures or biased instruments, it can lead to biased training data.
Prejudice bias: This occurs when societal prejudices or stereotypes find their way into the training data. For instance, if the data contains assumptions about certain groups, such as associating certain occupations with specific genders, the AI model may learn and perpetuate these biases.
Cognitive bias: This refers to the inherent biases in human decision-making that can be inadvertently introduced into the training data. For example, if the individuals labeling the data have their own biases, it can influence the training data and subsequently the AI model's decision-making process.
To mitigate bias in AI training data, it is crucial to ensure the data is complete, representative, and free from prejudices. This can be achieved through careful data collection, balancing the dataset, and using techniques like synthetic data to augment the training data. Additionally, ongoing monitoring and regular audits of the AI system can help identify and address biases that may arise over time.