Algorithmic bias in artificial intelligence (AI) and machine learning (ML) has received much scrutiny in the industry over the past few years. Although most discussions focus on the negative effects of bias, marketers can still improve Leverage biases for positive impact. To do this, you must first recognize and understand bias, and understanding how bias arises is the first step to taking action.
Essentially, AI bias refers to the bias of AI and ML based on specific training data, or relying on some features to make decisions. A common example is facial recognition systems. Usually the training data of facial recognition systems is mainly white faces. Therefore, when facing different cultural groups, the system cannot make accurate judgments.
A model that makes decisions based on some features cannot reflect the overall data; the model may also perform poorly on this type of data because it has not received a certain type of data during training.
Where does AI bias come from
AI and ML systems are trained using data obtained from various mechanisms; these data include: input data or features to make decisions, and the ideal output of the system (often also called labels) to guide decision rules. Certain training materials may bias certain results. In this case, the system will perform poorly on data it has not seen during training and produce unoptimized results.
It’s important to note that the ML model itself is morocco mobile number finder not the source of bias, the bias comes from the data used to train the model. In some cases, a system will produce good results with some types of data but perform poorly with other types of data.
Not all prejudice is bad
However, there is value for marketers in training AI models with some biased data.
Bias is often considered negative, but in fact, when KH Lists people train a model, it is better to let the model evolve in a specific direction than to be completely neutral. If everything is neutral, the training of the model will be more difficult.
This is because it takes a long time for an absolutely neutral model to combine the correct training data set and produce results. If you want to serve a specific customer group, it will be more advantageous to train the model with data from that customer group; and making good use of biases can help your AI model achieve value from the beginning of deployment.
