3 Strategies to Minimize Artificial Intelligence (AI) and Machine Learning (ML) Algorithm Bias
Bias in AI and ML algorithms is a complex issue, but here, you will find a simple way to understand and address it.
In business, technology, and society, at the moment, few topics elicit more interest than Artificial Intelligence (AI), and the related Machine Learning (ML) algorithms.
While the high level of captivation is well justified given the transformational potential of AI, a critical risk area, bias, is not receiving proportional attention.
The Risk of AI and ML Algorithm Bias
Bias in AI and ML algorithms has the potential to quickly erode reputations, putting brands, and social stability at stake. The public easily becomes uneasy when bias in algorithms used for critical services such as recruitment, credit risk scoring, and good conduct records, are exposed.
It is clear, bias in AI and ML algorithms needs addressing from the start.
Research in AI and Algorithm bias is still growing. I present three filtered strategies that you can use in any AI and ML environment to reduce the possible negative impacts.
#1 Conduct an Impact Assessment
To get started with addressing bias in the deployment of AI and ML algorithms, you need to understand the scope of the problem. You do this through a rigorous assessment of the potential impact of bias in your specific environment.
An example is where the recruitment algorithm applied by Amazon, the online retailer, downgraded women applications as compared with those of men. Amazon took corrective measures.
A detailed impact assessment should be aimed at bringing forward all potential risks you would face as an individual or organization from AI and ML deployment. The assessment would therefore be a good basis for robust technology implementation.
#2 Account for Historical Human Biases
AI and ML power lie in their ability to perform some intelligence tasks that would otherwise require humans to perform, except at a much higher speed and scale. In this sense, such technologies speed up and amplify any bias that may already exist in a data set extracted from a given culture.
Historical biases may be against groups of people, or towards practices detrimental to environmental sustainability.
You can minimize this challenge by having a multidisciplinary approach to AI and ML development. Ensuring the development team has input from social and environmental sciences, contributes to the success of the strategy.
#3 Account for Incomplete and Unrepresentative Data
All AI and ML algorithms are driven by the data they train on. Incomplete, or unrepresentative data, as a consequence, poses a critical challenge. This is often seen as a compromise problem as data capture, and cleaning tools are still developing.
Incompleteness can easily mask biases and lack of representation amplifies them.
Accounting for incomplete and unrepresentative data is best done as a continuous process. Deployment in public should be well-calibrated to the level of completeness and representativeness of the data used.
Conclusion
AI and ML algorithms are here to stay, and their importance will only increase. If well applied, these technologies have transformational potential. To take full advantage of them, the issue of bias must be addressed as part of the development process, not after deployment.
Exhaustive impact assessment, a multidisciplinary approach, and weighing implementation based on data integrity provide a starting point in understanding and addressing bias in AI and ML technologies.
A lot of growth is expected.
