AI bias

Machine learning outputs often include anomalies that take place because of AI bias. The use of AI is growing in all areas starting from automotive, healthcare, manufacturing to criminal justice, and hiring. This has given birth to a debate about AI bias and fairness. AI bias occurs from the partial presumption made during the algorithm development process and training data. 

The success of any AI implementation is tied to its training data. It is not just having the right data volume or right quality data, it is also important for organizations and companies to ensure that AI engineers are not partial to their creations. When engineers pass their own biases and assumptions to influence data sets, implementation depending on AI becomes biased which is inaccurate and unuseful. Biases in data sets lead to data supply which is restricted to certain focal points and demographics. 

There are several methods to ensure fairness in AI models. The first one is the preliminary processing of data to retain accuracy. The second one is post-processing methods. It includes the transformation of the AI model’s assumptions after they are made in order to generate fairness.

How to identify AI bias?

Cognitive biases

These are productive feelings towards a person or a group based on their perceived group membership. This occurs from human developers that influence machine learning models and training data sets.

Lack of complete data

Incomplete data also produces biases. If data is not complete, it may not be emblematic and therefore it may include bias. 

How to maximize fairness and minimize bias from AI?

Stay up to date

Organizations must be conscious of contexts in which they can reduce AI bias and increase fairness. Organizations will need to stay up to date to see how and where AI can improve fairness and reduce biases.

Operational Strategies 

Operational strategies include enhancing data clusters through more apprised sampling and using internal third parties to audit data and models. Clear and transparent processes and metrics can help in understanding the steps that are to be taken to boost fairness and any associated trade-offs.

Improve Human-Driven Processes

Organizations must look into biases that occur from human decisions. AI application is based on human decision making so it is important to ensure the removal of long-standing biases from the past. When models instructed on recent human decisions or behavior show bias, organizations should consider how human-driven processes might be enhanced in the future to boost fairness.

Make More Data Available

Organizations can also reduce biases and increase fairness by making more data available to researchers and practitioners across the organizations working on these issues while being aware of privacy concerns and effective risks. Organizations must consider and evaluate the role of AI models and decision-making.

Diversified AI

A more varying AI community will better anticipate, spot, and review issues of unfair bias and better be able to hold communities likely affected by bias. This requires investments on multiple fronts, but especially in AI education and access to tools and opportunities.

Organizations need potential frameworks, toolkits, processes, and policies for identifying and alleviating AI bias. There is open source tooling available that serves to test AI implementations for specific biases, issues, and blind spots in data.