What are the best ways to identify and mitigate predictive analytics bias?

Predictive analytics is the use of data, algorithms, and machine learning to forecast future outcomes and behaviors. It can help businesses optimize their strategies, improve customer satisfaction, and reduce costs and risks. However, predictive analytics is not flawless. It can be affected by bias, which is the systematic distortion or error in the data, the algorithm, or the interpretation of the results. Bias can lead to inaccurate, unfair, or unethical decisions that can harm your business and your customers. Therefore, it is essential to identify and mitigate predictive analytics bias as much as possible. In this article, you will learn some of the best ways to do that.

1 Understand the sources of bias

Bias can come from different sources, such as the data collection, the data processing, the algorithm design, the algorithm implementation, or the algorithm evaluation. For example, the data collection can be biased if it does not represent the target population, if it contains errors or outliers, or if it is influenced by external factors. The data processing can be biased if it involves inappropriate cleaning, transformation, or aggregation of the data. The algorithm design can be biased if it relies on incorrect assumptions, inappropriate features, or hidden variables. The algorithm implementation can be biased if it suffers from overfitting, underfitting, or confounding. The algorithm evaluation can be biased if it uses inadequate metrics, benchmarks, or validation methods. To understand the sources of bias, you need to examine each step of the predictive analytics process and ask critical questions about the data, the algorithm, and the results.

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Joshua Suulola

Presentation Designer for Executives & Startup Founders | Strategic Executive Positioning on LinkedIn that saves you time and attracts high-value opportunities | Certified Learning Facilitator (CLF)
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One simple but powerful method of understanding and detecting sources of bias is the 4W1H method. What, Why, When, Where, How In this case; - What trace of bias can be detected from a set of collected data? - Why was such data omitted? - When was the data overfitted or underfitted? - Where was the source data extracted from? - How can the hidden variables be detected and corrected?

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2 Apply best practices and standards

One way to identify and mitigate predictive analytics bias is to apply best practices and standards throughout the predictive analytics process. For example, you can use data quality frameworks, such as the FAIR principles (Findable, Accessible, Interoperable, and Reusable), to ensure that your data is reliable, complete, and consistent. You can use data governance policies, such as the GDPR (General Data Protection Regulation), to ensure that your data is collected, stored, and used in a transparent, ethical, and legal way. You can use algorithm design guidelines, such as the FATML principles (Fairness, Accountability, Transparency, and Machine Learning), to ensure that your algorithm is fair, accountable, and transparent. You can use algorithm implementation tools, such as the scikit-learn library, to ensure that your algorithm is robust, efficient, and scalable. You can use algorithm evaluation methods, such as the ROC curve (Receiver Operating Characteristic curve), to ensure that your algorithm is accurate, balanced, and generalizable.

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Jason Sirois

Driving Innovation, Digital Transformation & Compliance Solutions | Strategic Partnerships | Product Innovation | SaaS | Technical Advocacy | Policy
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FAIR principles were applied to our AI-Driven EHS Software, "Citation Manage," which determines relevant EHS regulations via machine learning and user input. Challenge: Update AI amidst evolving global EHS rules. FAIR Application: Findable: Unique IDs for quick AI reference. Accessible: Centralized cloud database for AI. Interoperable: Uniform data structure. Reusable: User feedback refines AI. Benefits: User-Centric: Direct AI-driven EHS guidance. Current: Up-to-date AI queries via FAIR. Efficient: Manages multi-jurisdictional data. Adaptive: AI improves with feedback. Distinctive: Citation Manage's AI and FAIR data management sets it apart. Overall, FAIR principles bolstered Citation Manage's efficacy in the EHS software sector.

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3 Test and monitor for bias

Another way to identify and mitigate predictive analytics bias is to test and monitor for bias throughout the predictive analytics process. For example, you can use data analysis techniques, such as descriptive statistics, visualizations, or correlation analysis, to explore your data and detect any anomalies, outliers, or patterns that may indicate bias. You can use algorithm analysis techniques, such as feature importance, sensitivity analysis, or counterfactual analysis, to examine your algorithm and understand how it makes predictions and what factors influence them. You can use bias detection techniques, such as statistical tests, fairness metrics, or discrimination measures, to measure the extent of bias in your algorithm and its impact on different groups or outcomes. You can use bias mitigation techniques, such as data augmentation, algorithm regularization, or post-processing adjustments, to reduce or eliminate the bias in your algorithm and improve its performance and fairness.

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Deepak Chopra

Staff Data Scientist @ Meta | ex-dunnhumby | ex-Target
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A vigilant and iterative approach to bias detection and mitigation ensures predictive analytics remain equitable and accurate.. To combat predictive analytics bias, continuous testing and monitoring are paramount. - Employ data analysis tools like descriptive statistics and visualizations to unearth anomalies and patterns signaling potential bias. - Dive into algorithm analysis through methods like sensitivity analysis to understand prediction influences. - Gauge bias impact using statistical tests assessing disparities among groups. - Finally, implement bias mitigation strategies like data augmentation and post-processing adjustments top enhance performance.

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Joshua Suulola

Presentation Designer for Executives & Startup Founders | Strategic Executive Positioning on LinkedIn that saves you time and attracts high-value opportunities | Certified Learning Facilitator (CLF)
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Sometimes, biased information could be covered with facts; especially facts personally sourced. Statistical tests could help to detect different forms of bias that may have been consciously or unconsciously omitted.

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4 Engage with stakeholders and experts

A final way to identify and mitigate predictive analytics bias is to engage with stakeholders and experts throughout the predictive analytics process. For example, you can involve your customers, employees, partners, or regulators in the data collection, the algorithm design, or the algorithm evaluation, to ensure that you capture their needs, preferences, feedback, and expectations. You can consult with domain experts, such as industry professionals, researchers, or consultants, in the data processing, the algorithm implementation, or the algorithm evaluation, to ensure that you leverage their knowledge, experience, or insights. You can collaborate with ethics experts, such as ethicists, lawyers, or auditors, in the data governance, the algorithm design, or the algorithm evaluation, to ensure that you comply with ethical principles, legal regulations, or audit standards. By engaging with stakeholders and experts, you can increase the trust, credibility, and value of your predictive analytics.

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Daniel Alejandro Sanchez-Martin

Business Analyst | Data Analyst | Business Intelligence Analyst | Power BI | DAX | Power Query | SQL | Tableau | ETL | Advanced Excel | Jira | BRD
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I believe it's crucial to maintain clear communication with stakeholders and, if possible, understand the history of similar projects and the bias they faced before. This approach helps in swiftly identifying any biases. Furthermore, seeking a quick review from a teammate can be invaluable. After working for several hours, we might overlook "weird" values, but a fresh pair of eyes can easily spot them.

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Joshua Suulola

Presentation Designer for Executives & Startup Founders | Strategic Executive Positioning on LinkedIn that saves you time and attracts high-value opportunities | Certified Learning Facilitator (CLF)
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Engagement with stakeholders and experts throughout the execution process is key. During this engagement period, it is important to pay attention to their pain points, core desire and their expectations. Ensure you understand them in detail before proceeding to execute. Make sure you also consult experts in relevant fields that can offer diverse but impactful contribution towards the mitigation and execution stage.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best ways to identify and mitigate predictive analytics bias?

1

2

3

4

5

1 Understand the sources of bias

2 Apply best practices and standards

3 Test and monitor for bias

4 Engage with stakeholders and experts

5 Here’s what else to consider

Critical Thinking

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What are the best ways to identify and mitigate predictive analytics bias?

1

2

3

4

5

1 Understand the sources of bias

2 Apply best practices and standards

3 Test and monitor for bias

4 Engage with stakeholders and experts

5 Here’s what else to consider

Critical Thinking

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