Currently, most businesses are relying on artificial intelligence (AI) to solve multiple problems facing the world to improve the quality of life. Some of the sectors using AI include crisis prediction, crisis response, economic modelling, the healthcare sector, and more, which is affecting the way we operate. Despite having many benefits, AI has several flaws that require confrontation to help solve global challenges effectively.
When developing AI to eliminate global problems, various sectors inherit the bias of constructed algorithms, which makes AI and machine learning to have flaws when solving challenges after analyzing collected data. You can eliminate these flaws by incorporating the diversity of our thoughts with more perspectives to develop a unique approach that is unbiased to solve global problems. Here are four common machine learning bias that we should tackle to solve the majority of global issues.
1. Sample Bias
Sample bias occurs when the model developed is applied to a different environment to the one where data was initially collected. The algorithm used in AI is a subset of trained data on a particular field and cannot help solve the entire universe problems. An example of machine bias is when developing security cameras where data collected factors in daytime only, but you need to perform 24-hour surveillance in a day.
Sample bias is eliminated by examining all necessary features and training your model to cover every scenario that may arise when tackling a particular issue. Whenever you fail to consider all the essential hallmarks, you will develop an erroneous model that cannot solve the problems at hand.
2. Exclusion Bias
Arises from the exclusion of some features that are deemed irrelevant usually when cleaning data. When developing a model to solve a particular problem, it is easier to overlook data that you can predict to have minimal influence on your results, which can affect your problem-solving ability. You can eliminate or reduce exclusion bias by sufficiently investigating and analyzing all features of a particular model before discarding anything.
3. Prejudice Bias
Stereotypes or cultural influences are the cause of prejudice bias. This form of bias arises from our way of assessing things that we hate in life such as judging by social class, gender, appearances, status, and more by failing to incorporate them in our machine learning model. You can eliminate or reduce prejudice bias by exposing your algorithm to evenly distributed and less biased data.
4. Measurement Bias
When developing your model, avoid systematic value distortion by using quality devices to observe or measure data. Measurement bias will mess up your training data, which will lead to skewed results. Using several measuring devices, as well as hiring experts to compare your device's output, eliminates or reduces measurement bias. For more information on machine learning bias and their mitigation on AI systems, contact us now!