Organizations are increasingly turning to Howso platform for bias mitigation by implementing four different strategies.

Data analytics tools, including artificial intelligence (AI) and machine learning (ML) models, only create value if they use quality data. As organizations begin to explore strategies for harnessing these tools, they often encounter issues with poor data quality which could make their analyses unethical and problematic. Often, poor data quality and subsequent unequitable analysis results arise from biases in the data. Bias may arise from lack of diversity of the populations contained in the data (sampling and representation bias) or choice of information contained in the data (measurement and omitted variable bias). These issues are compounded when data is generalized across populations (aggregation bias) or various datasets are combined (linking bias).  

Lack of Transparency

Biases in data may propagate through analysis and AI/ML modeling leading to biased results upon which predictions and decisions are made. This potentially harms various groups of people and reduces the overall utility of data analysis. Additionally, these biased results may be projected into the future and lead to missed opportunities to create true value.  

For example, there is a historical lack of diversity among leadership at large organizations. If data representing leadership demographics at large organizations is used for AI/ML modeling, this lack of representation would be captured and understood to be normal by the models. Predictions from the models might suggest this lack of diversity will or should continue in the future. This harms a variety of populations, but also leads to missed opportunities to diversify leadership which may result in better outcomes for organizations. Finally, analytics and modeling results that use biased data are increasingly falling under regulations. Decisions that are based on insights gained from biased data might lead to breaches of these regulations, further necessitating the need for bias mitigation strategies. 

It is obvious that biased data leads to challenges for organizations trying to create value with their data. However, mitigating biases in data may not always be a straightforward fix. Often, data biases are handled on an ad hoc basis, i.e., when the bias is observed as the data is being used for modeling and analysis. This creates organizational bottlenecks as bias mitigation requires time, resources, and expertise to implement effectively.  

Howso’s Bias Mitigation Strategies

To overcome this issue, organizations are turning to the unique capabilities of Howso, including Howso’s synthetic data and inference with attribution, which enable bias mitigation in four ways. These four strategies offer a variety of techniques to gain the most utility for an organization’s data analysis capabilities. For example, bias mitigation strategies can be applied solely for data, but also for evaluating and fine-tuning bias in analysis and modeling applications. Howso’s bias mitigation strategies provide organizations opportunities to customize their data to create value for each use case, empowering true, ethical data insights. 

1. Harness Howso Platform for Bias Detection + Implement Synthetic Data for Bias Mitigation 

Utilizing Howso’s inference with attribution enables users to identify any potential bias in data. Howso detects which protected attributes in a dataset contribute the most to model predictions, showing biases users may or may not have recognized. Once these potential biases are identified, they can be addressed using our synthetic data’s balancing or fined-tuned approach to bias mitigation, discussed below. Utilizing Howso platform’s full suite of solutions is an option for users who want to use insights from their original data to make the most informed data synthesis decisions. Additionally, this method leads to more explainable fairness through awareness of potential biases, providing additional value for organizations. 

2. Balancing Approach

Howso’s general approach to bias mitigation adapts our synthetic data’s ID-based privacy tool to improve or reduce representation bias. ID-based privacy ensures that the privacy of an individual is not leaked because of the frequency with which the individual occurs in the dataset. Thus, the ID-based privacy tool balances the number of records in the synthetic dataset per individual. To mitigate bias, ID-based privacy can be applied to potentially biased features, resulting in balanced synthetic data for that feature. For example, if ID-based privacy were enabled for a gender feature in an original dataset that contained more male records than female, the resulting synthetic data would be balanced between male and female records. Howso’s synthetic data approach to bias mitigation is useful for when there is known bias in the data that needs to be dealt with, but users may not have specific needs for how bias mitigation should be implemented. 

3. Fine-Tuned Approach

Howso’s fined-tuned approach to bias mitigation involves applying custom distributions to generated synthetic data. Users can specify what the bias mitigation strategy should be for each feature of the dataset, depending on the use case. For example, should gender always have an equal male-female distribution? Should different ethnicities, religions, or age groups represent different proportions of a dataset? Users can decide what these distributions should be. Howso’s fined-tuned bias mitigation approach should be implemented when users have information on what the synthetic dataset must contain to be adequately representative of the information it is meant to contain. 

4. Direct Approach

Howso’s direct approach to bias mitigation enables users to break relationships between features with known links that result in bias. Here, Howso synthesizes a single feature at a time, using ID-based privacy so that the resulting feature values in the synthetic data are evenly distributed. This breaks the relationship between the biased features in the original data set because they are not jointly considered during synthesis. For example, if there is a known relationship between gender and credit card limits, each feature can be synthesized separately, effectively severing the biased relationship and leading to evenly distributed synthetic gender and credit card limit data.  

Conclusion

In conclusion, Howso’s technology offers multiple bias mitigation strategies to empower organizations to ethically gain insights and unlock value from their data, facilitating equitable decisions for the future. 

If you’d like a deeper dive into how Howso can help your organization mitigate bias, schedule time to chat with a Howso expert here. Ready to see Howso for yourself? Access Playground here.

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