Human or cognitive bias represents a systematic error affecting judgments and decisions made by individuals. Such an error occurs during processing and interpretation of data and information. The nature of bias relates to the simplification approach of our brain.

On the contrary, machine learning is a subfield of artificial intelligence that generates statistical models and should help to exclude this error. Let's review if this claim is solid and machine learning can indeed become a useful tool in overcoming human bias.

Sources and Nature of Cognitive Bias

By definition, machines do not have bias. At least not at their current development stage. However, human bias occurs in machine learning between the stages of algorithm creation and data interpretation. The research has identified an overwhelming number of the cognitive bias types, such as conjunction fallacy and representativeness heuristic, misunderstanding of “and”, averaging heuristic, disjunction fallacy, and many others.

These types of cognitive bias impair machine learning. For instance, confirmation bias implying acceptance of beliefs based on confirmation of the previous beliefs, and availability basis suggesting an emphasis on information relevant to an individual, negate interpretation of machine data learning.

Cognitive bias becoming a part of a machine learning model nullifies its effectiveness in the long term. The complexity of resolving the issues related to human bias in machine learning stems from the need to connect the domains of cognitive psychology and machine learning. Therefore, the preliminary research unifying the conclusive evidence from both fields would address the main system design questions.

Consequences of Human Bias for Machine Learning

The negative consequences from human bias imply a wide range of issues for machine learning. It is possible to categorize these consequences into the two main categories:

Influence: The results produced by modern technology receive are commonly synonymous to facts in their accuracy and the levels of trust from the general public. However, the results produced via machine learning with the human bias would contain significant errors. Thus, they would compound over time because of their most likely universal implementation.

Automation: Further automation of AI models would imply integration of the underlying cognitive bias implemented at the machine learning stage.

These consequences require adequate solutions, including careful assessment of the existing bias types for the implementation of the respective preventative mechanisms.

Avoidance of Human Bias

The consequences of human bias in machine learning would range from ethical concerns to potentially significant financial damages for the companies. Therefore, the main system design questions should reflect the solutions managing bias in machine learning.

The first solution is selection of the appropriate learning model. The models are commonly unique in case-by-case application basis. However, it is possible to identify specific parameters that could increase the risk of human bias presence. For instance, supervised and unsupervised learning models have their benefits and drawbacks. Supervised models may allow a higher degree of control over data selection while also exhibiting the higher degree of risk related to appearance of cognitive bias. Non-bias ignorance is a part of the first solution that requires excluding sensitive information from the model. Its vulnerability stems from the variability of the issues. Communication with the data scientists at the early stages to exclude cognitive bias facilitates selection of the appropriate learning model.

The second solution requires selection of a representative dataset. When selecting data for training, it is necessary to make sure that it is sufficiently diverse. The model should incorporate various groups, while supporting data segmentation. The solution might require development of the separate models for different groups.

The third solution suggests monitoring performance using real data. It is impossible to test the machine learning model for bias solely in controlled environment. The approach would be unable to address the main system design questions. Simulation of the real-world applications when building algorithms lowers the risks related to human bias.

Regulatory Framework

In addition to companies and researchers attempting to minimize human bias in machine learning, various committees and organizations are forming international bodies setting standards for artificial intelligence. The International Organization for Standardization (IOS) and the International Electrotechnical Commission (IEC) have formed a joint technical committee ISO/IEC JTC 1. The committee focuses specifically on security, safety, privacy, accuracy, reliability, resilience, and robustness of artificial intelligence. Such standardization efforts should support the efforts, minimizing the presence of human bias. Besides, IEC is a founding member of OCEANIS which oversees ethics in autonomous systems. The main goal of the organization is ensuring that machines follow human values and logic, while avoiding their bias.

Future of Machine Learning

It is becoming evident that machine learning will continue advancing with the introduction of novel technologies. At the same time, it is no longer only Google, Facebook and the other comparable tech giants that are able to afford developing artificial intelligence utilizing machine learning. The smaller companies, such as Scale AI, receive funding for developing their own artificial intelligence. These tendencies imply the need for further standardization in the industry, since the potential for the negative consequences would be significant.

Machine learning will need to overcome human bias for their successful implementation in various fields. Artificial intelligence finds its application in the fields that have a direct impact on human life, such as medicine. In this light, machine learning should not contain cognitive bias elements during their full-scope incorporation. The future of artificial intelligence depends on the successful outcome of the combined efforts.