Avoiding Bias in Machine Learning

Avoiding Bias in Machine Learning

0 0

Depending upon the stakes of the decisions that are based on findings by Artificial Intelligence, legal liabilities that the companies face may be different. Here are three ways companies and data scientist can work to reduce bias in data findings.

Focus on the problems-Design a relevant solution

Mainly because all problems are different, require different solutions and use different data resources, all AI models are unique. So there’s no single method to use that could help avoid biases in results. However, certain parameters can be used to inform data teams as the bias builds within the system
Data scientists need to be informed about the best way to create or identify the right model for the situation in question. Working proactively to ensure no problems arise may take longer but may be better than having regulators finding them later.

Diverse data set

When working with data, it is essential to have diversity in the training data which can be obtained by acquiring real-time data from different segments. The same model should apply to the complete training data set.
In case data for a group may appear insufficient, using weighting may solve the issue. The use of weights, however, should be monitored as it may pick up random noise as data and end up creating new biases!

Use of real time data

Companies are not intentionally creating bias in their artificial intelligence, of course! Their models may have worked fine in a controlled environment. However, to reduce bias in AI findings, it is essential to incorporate real-time applications as much as possible when constructing algorithms.
Statistical methods should use real-time data to test results whenever possible! This may provide the accuracy needed; the data team should be using simple test question to check for responses and search for reasons regarding any bias that may appear in results.
When examining data, two types of equalities should be considered; equality of outcome (people having the same economic conditions) and equality of opportunity (people should compete on equal terms) and although the equality of opportunity is harder to prove it tends to hold an ethical nature.
Outcome opportunity may be easier to prove, but it comes with the acceptance of skewed data. Often impossible to find both equalities in data, testing out these models in the real world and oversight should help with the accuracy of results.


Considering the attempts being made to regulate and refine algorithms it won't be a surprise if the governments become involved in the development processes, and monitoring of the outcomes of AI.
The use of proper models and modelling principles, bias can be reduced, and AI representatives can work to expose any unethical bias that may cause them to end up in legal altercations.