Data Bias in Artificial Intelligence
This is a guide on data bias in artificial intelligence.
So what is data bias? Data bias occurs when a dataset has unfair inaccuracies or prejudices, causing skewed or discriminatory outcomes in artificial intelligence decision-making. It often occurs due to human prejudice seeping into the data process.
An error in data, produces an under or overweight representation of a population. This error produces outcomes that are misleading and either skewed due to a lack of data in one group versus another, or skewed due to prejudice and negative systemic beliefs. Data bias is an issue that occurs when working with data that has been curated or generated by humans.
Humans have biases that stem from a multitude of places. When working with data, the biases infiltrate the inputs and impact the outputs. These biases have the potential to sway decisions that are reinforcing negative human perspectives, or that are considered harmful to a group or groups of people.
When humans generate or prepare a dataset for a model, often their unconscious biases enter the model. This allows those biases to be perpetuated and amplified. Now let us discuss how data bias occurs. Data bias can enter a model from the very beginning of data collection. Models need a large amount of data points as references.
The amount of data that goes into the model has an impact on the quality of insights that can be made from the data. Poor quality, incomplete, non-diverse, and biased data, will produce an outcome that is also low quality, inaccurate, or biased. Data bias has an impact on the insights we gain from the model and how we interact with the outcomes. When the data is low quality, our business practices, customer service, and reporting suffer as a result.
Having data bias can also lead to activity that is unethical or even unlawful. Biased data can strengthen harmful stereotypes present in training data, affecting AI outputs and contributing to social and cultural biases. Biased data can lead to a lack of trust in AI systems and their reliability and fairness. The use of biased AI raises ethical issues, especially in critical areas like healthcare, finance, and criminal justice, where biased decisions can have serious real-world consequences.
Reducing data bias in AI requires ethical and responsible practices in collecting, pre-processing, and developing models. Essential steps include using fairness-aware algorithms, ensuring datasets are diverse and representative, and continuously monitoring and evaluating for bias.
Let us discuss some of the most common types of data bias. Algorithm bias: occurs when there is a bias in the code or programming within the algorithm. Sample bias: occurs when there is a bias in the dataset, either too little data about a group within the model, or a prejudice that exists from the gathering of the sample.
Prejudice bias: the model contains prejudices, social stereotyping, and other negative assumptions based upon a social or cultural identifier. Measurement bias: using data that prepositions the model to measure more positive qualities, and manipulating the data to have other skewed measurements. Exclusion bias: excluding large amounts of data due to the data points not being valued by the creators of the model.
Recall bias: this happens when labels are not consistently or accurately applied throughout the data in the model. Labels assigned to data points can be subjective or carry inherent biases impacting the training and performance of the model. Please note this is not an all-encompassing list, and other types of bias exist.