What is Bias in AI ?

The issue of bias in Artificial Intelligence is one that every organisation and individual needs to take seriously as this can set the stage for various prejudiced outcomes and poor decision making. AI systems make decisions based on the data that has been fed into the system. The system does this by learning the pattern from the fed data. The results generated from the system is based on the quality of the data that  the system has worked with. Biased data fed into the system will generate poor outcomes and lead to poor decision making processes.

Bias in Artificial Intelligence is also known as Machine learning bias or Algorithm bias. Machine learning bias has been described by BMC Software as a systematic error that occurs in the machine learning model itself due to incorrect assumptions in the Machine Learning process. Technically, we can define bias as the error between average model prediction and the ground truth. Other sources have also described bias in AI to be the occurrence of untruthful, unfair or prejudiced outcomes brought about by algorithms which happens as a result of biased design choices or biased data that’s fed into the system.

There are various forms of bias which includes but is not limited to : Sampling bias, Label bias, Data bias, Prejudicial bias, algorithmic bias and lots of others. Sampling bias for example can lead to the reduced representation or the increased representation of certain groups in data. Results generated from AI models trained by these biased data will result in poor choices and poor decision making. These poor choices could sometimes mean the marginalisation of certain groups of people, race, gender or ethnicity. Sampling bias on the other hand is very different from data bias which is the most common type of bias. In the case of data bias, AI systems that learn patterns or generate outcomes from biased data for example could give results that reflect or erroneously project unfair outcomes or advantages in the society. These erroneous results could lead to the stigmatisation of certain groups as well as the mislabelling of certain professions, gender or race. These stigmatisation could further slow down the adoption of causes such as equality - which the society has worked towards achieving for many years.

AI biases can lead to unequal access to medical resources, biased outcomes in court cases, reduced public trust, discriminatory and legal consequences, poor decision making, ethical concerns, marginalisation of groups, suppression of the voice of some marginalised groups, stifled diversity of opinion and other discriminatory outcomes. However, the continuous audit of AI systems to understand if biases are present, the building of transparent systems that explains how decisions are made to users, the use of bias mitigation algorithms to reduce bias in machine learning models - are a few of the ways through which AI bias can be mitigated.

Developers, users, machine learning experts as well as industry experts should all collaborate to understand data source, ensure that clean data is fed into AI systems, ensure that all groups are properly represented and also ensure that present biases are tackled from diverse perspectives - in order to be able to reduce biases in AI.