Mitigate Machine Bias

Control machine bias to prevent discriminating against your consumers and damaging your organization.

Onsite Workshop

Unmitigated machine biases:

  • Are harmful to consumers of your AI-enabled product or service. Such products/services may discriminate against consumers by providing poor quality of service, denying access, or maybe not even recognizing them as customers.
  • Are enterprise risk: you are exposing the organization to reputational risk, missed business opportunity, and potentially, litigation and regulatory sanctions.

By identifying and mitigating machine biases:

  • You minimize and control their impact, scope, and associated risks.
  • You may not be able to fully eradicate machine biases, but you will at least reduce your organization’s exposure and liability while reaping the benefits of AI.

Module 1: Prepare

The Purpose

  • Understand your organization’s maturity with respect to data and analytics in order to maximize workshop value.

Key Benefits Achieved

  • Workshop content aligned to your organization’s level of maturity and business objectives.

Activities: Outputs:
1.1 Execute Data Culture Diagnostic.
  • Data Culture Diagnostic report.
1.2 Review current analytics strategy.
1.3 Review organization's business and IT strategy.
1.4 Review other supporting documentation.
1.5 Confirm participant list for workshop.

Module 2: Understand Machine Biases

The Purpose

  • Develop a good understanding of machine biases and how they emerge from human cognitive and societal biases. Learn about the machine learning process and how it relates to machine bias.
  • Select an ML/AI project and complete a bias risk assessment.

Key Benefits Achieved

  • A solid understanding of algorithmic biases and the need to mitigate them.
  • Increased insight into how new technologies such as ML and AI impact organizational risk.
  • Customized bias risk assessment template.
  • Completed bias risk assessment for selected project.

Activities: Outputs:
2.1 Review primer on AI and machine learning (ML).
2.2 Review primer on human and machine biases.
2.3 Understand business context and objective for AI in your organization.
2.4 Discuss selected AI/ML/data science project or use case.
2.5 Review and modify bias risk assessment.
  • Bias risk assessment template customized for your organization.
2.6 Complete bias risk assessment for selected project.
  • Completed bias risk assessment for selected project.

Module 3: Identify Data Biases

The Purpose

  • Learn about data biases: what they are and where they originate.
  • Learn how to address or mitigate data biases.
  • Identify data biases in selected project.

Key Benefits Achieved

  • A solid understanding of data biases and how to mitigate them.
  • Customized Datasheets for Data Sets Template.
  • Completed datasheet for data sets for selected project.

Activities: Outputs:
3.1 Review machine learning process.
3.2 Review examples of data biases and why and how they happen.
3.3 Identify possible data biases in selected project.
3.4 Discuss “Datasheets for Datasets” framework.
3.5 Modify Datasheets for Data Sets Template for your organization.
  • Datasheets for Data Sets Template customized for your organization.
3.6 Complete datasheet for data sets for selected project.
  • Completed datasheet for data sets for selected project.

Module 4: Identify Model Biases

The Purpose

  • Learn about model biases: what they are and where they originate.
  • Learn how to address or mitigate model biases.
  • Identify model biases in selected project.

Key Benefits Achieved

  • A solid understanding of model biases and how to mitigate them.
  • Customized Model Cards for Model Reporting Template.
  • Completed model card for selected project.

Activities: Outputs:
4.1 Review machine learning process.
4.2 Review examples of model biases and why and how they happen.
4.3 Identify potential model biases in selected project.
4.4 Discuss Model Cards For Model Reporting framework.
4.5 Modify Model Cards for Model Reporting Template for your organization.
  • Model Cards for Model Reporting Template customized for your organization.
4.6 Complete model card for selected project.
  • Completed model card for selected project.

Module 5: Create Mitigation Plan

The Purpose

  • Review mitigation approach and best practices to control machine bias.
  • Create mitigation plan to address machine biases in selected project. Align with enterprise risk management (ERM).

Key Benefits Achieved

  • A solid understanding of the cultural dimension of algorithmic bias prevention and mitigation and best practices.
  • Drafted plan to mitigate machine biases in selected project.

Activities: Outputs:
5.1 Review and discuss lessons learned.
  • Summary of challenges and recommendations to systematically identify and mitigate machine biases.
5.2 Create mitigation plan to address machine biases in selected project.
5.3 Review mitigation approach and best practices to control machine bias.
5.4 Identify gaps and discuss remediation.
  • Plan to mitigate machine biases in selected project.

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