Before providing data for an algorithm to learn, scientists and researchers must ensure that the data they use is fair, balanced, and unbiased. Machines do not have the capacity to question the data provided in the same way humans do. If the data being used is biased from the start, the machine has no way of recognizing the problem. To minimize AI bias, it is important to test data and algorithms and develop AI systems taking into account the principles of responsible AI. Created by humans, machine learning algorithms can easily capture the biases of their creators.
A machine learning model that uses historical data to predict outcomes will inadvertently reinforce any biases found in previous decisions, metrics, or parameters. It is important to note that the smaller the group of people responsible for decisions, the greater the risk of bias. When it comes to AI bias, everyone thinks about the bias of training data, which is the data used to develop an algorithm before it is tested around the world. However, this is just the tip of the iceberg.
First, all detection approaches must begin with careful handling of sensitive user information, including data that identifies a person's membership in a group protected by federal law (e.g., a job search algorithm may not receive the gender field as input, but it can produce different matching scores for two resumes that only differ in the replacement of the name “Mary” with Mark, because the algorithm is trained to make these distinctions over time). It is wrong to think that once a machine learning model is trained and put into practice, it no longer needs human supervision. People with different backgrounds and life experiences will provide a new and even unexpected perspective on the problem at hand, helping to balance the training data set and make it more neutral. When this occurs, a bias is more likely to occur due to the combination of complex systems, but it is also less easy to identify and prevent. Based on that training data, it learns a model that can be applied to other people or objects and makes predictions about what the correct results should be for them.
But do these searches really fit our preferences or those of someone else, such as a provider? The same applies to all systems. Algorithm operators must also consider the role of diversity within their work teams, training data, and decision-making processes. AI bias is an anomaly in the outcome of machine learning algorithms due to biased assumptions made during algorithm development or biases in training data. Trained with poor and biased data, a machine learning algorithm cannot provide an accurate forecast. While the immediate consequences of biases in these areas may be small, the enormous amount of digital interactions and inferences may constitute a new form of systemic bias. These problematic results should lead to greater debate and greater awareness about how algorithms work in managing confidential information and trade-offs surrounding fairness and accuracy of models. For example, in Buolamwini's facial analysis experiments, poor recognition of darker-skinned faces was largely due to their statistical underrepresentation in training data.
An algorithm that predicts house prices will require regular retraining with new and updated data since prices tend to change all the time and predictions become inaccurate before you know it. AI is also having an impact on democracy and governance as computerized systems are implemented to improve accuracy and boost objectivity in government functions. Algorithm operators must consider diversity within their work teams, training data, and decision-making processes in order to reduce AI bias.
