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De-Risk Your AI Applications by Proactively Identifying and Mitigating Biases

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

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Your Challenge

  • AI is the new electricity. It is fundamentally and radically changing the fabric of our world, from the way we conduct business, to how we work and live, make decisions, and engage with each other, to how we organize our society, and ultimately, to who we are. Organizations are starting to adopt AI to increase efficiency, better engage customers, and make faster, more accurate decisions.
  • Like with any new technology, there is a flip side, a dark side, to AI – machine biases. If unchecked, machine biases replicate, amplify, and systematize societal biases. Biased AI systems may treat some of your customers (or employees) differently, based on their race, gender, identity, age, etc. This is discrimination, and it is against the law. It is also bad for business, including missed opportunities, lost consumer confidence, reputational risk, regulatory sanctions, and lawsuits.

Our Advice

Critical Insight

  • Machine biases are not intentional. They reflect the cognitive biases, preconceptions, and judgement of the creators of AI systems and the societal structures encoded in the data sets used for machine learning.
  • Machine biases cannot be prevented or fully eliminated. Early identification and diversity in and by design are key. Like with privacy and security breaches, early identification and intervention – ideally at the ideation phase – is the best strategy. Forewarned is forearmed. Prevention starts with a culture of diversity, inclusivity, openness, and collaboration.
  • Machine bias is enterprise risk. Machine bias is not a technical issue. It is a social, political, and business problem. Integrate it into your enterprise risk management (ERM).

Impact and Result

  • Just because machine biases are induced by human behavior, which is also captured in data silos, they are not inevitable. By asking the right questions upfront during application design, you can prevent many of them.
  • Biases can be introduced into an AI system at any stage of the development process, from the data you collect, to the way you collect it, to which algorithms are used, to which assumptions are made, etc. Ask your data science team a lot of questions; leave no stone unturned.
  • Don’t wait until “Datasheets for Datasets” and “Model Cards for Model Reporting” (or similar frameworks) become standards. Start creating these documents now to identify and analyze biases in your apps. If using open-source data sets or libraries, you may need to create them yourself for now. If working with partners or using AI/ ML services, demand that they provide such information as part of the engagement. You, not your partners, are ultimately responsible for the AI-powered product or service you deliver to your customers or employees.
  • Build a culture of diversity, transparency, inclusivity, and collaboration – the best mechanism to prevent and address machine biases.
  • Treat machine bias as enterprise risk. Use your ERM to guide all decisions around machine biases and their mitigation.

Research & Tools

Start here – read the Executive Brief

Read our concise Executive Brief to understand the dark side of AI: algorithmic (machine) biases, how they emerge, why they are dangerous, and how to mitigate them. Review Info-Tech’s methodology and understand the four ways we can support you in completing this project.

1. Understand AI biases

Learn about machine biases, how and where they arise in AI systems, and how they relate to human cognitive and societal biases.

2. Identify data biases

Learn about data biases and how to mitigate them.

3. Identify model biases

Learn about model biases and how to mitigate them.

4. Mitigate machine biases and risk

Learn about approaches for proactive and effective bias prevention and mitigation.

Guided Implementations

This guided implementation is an eight call advisory process.

Guided Implementation #1 - Understand AI biases

Call #1 - Learn about machine biases and why they are dangerous if unchecked. Learn about human biases and their relation to machine biases. Learn about the ML process and how it aligns with machine biases.

Guided Implementation #2 - Identify data biases

Call #1 - Learn about data biases.
Call #2 - Learn about “Datasheets for Datasets” framework to combat biases in training data. Review customized datasheets template. Review completed datasheet(s) for selected project.

Guided Implementation #3 - Identify model biases

Call #1 - Learn about model biases.
Call #2 - Learn about “Model Cards For Model Reporting” framework to combat model biases. Review customized model card template. Review completed model card for selected project.

Guided Implementation #4 - Mitigate machine biases and risk

Call #1 - Discuss organizational principles to create a culture for effective bias prevention and mitigation.
Call #2 - Review completed bias risk assessment.
Call #3 - Discuss why AI biases are an enterprise risk. Assess biases identified earlier. Determine mitigation where desired.

Onsite Workshop

Discuss This Workshop

Book Your Workshop

Onsite workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn't enough, we offer low-cost onsite delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.

Module 1: 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

1.1

Review primer on AI and machine learning (ML).

1.2

Review primer on human and machine biases.

1.3

Understand business context and objective for AI in your organization.

1.4

Discuss selected AI/ML/data science project or use case.

1.5

Review and modify bias risk assessment.

  • Bias risk assessment template customized for your organization.
1.6

Complete bias risk assessment for selected project.

  • Completed bias risk assessment for selected project.

Module 2: 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

2.1

Review machine learning process.

2.2

Review examples of data biases and why and how they happen.

2.3

Identify possible data biases in selected project.

2.4

Discuss “Datasheets for Datasets” framework.

2.5

Modify Datasheets for Data Sets Template for your organization.

  • Datasheets for Data Sets Template customized for your organization.
2.6

Complete datasheet for data sets for selected project.

  • Completed datasheet for data sets for selected project.

Module 3: 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

3.1

Review machine learning process.

3.2

Review examples of model biases and why and how they happen.

3.3

Identify potential model biases in selected project.

3.4

Discuss Model Cards For Model Reporting framework.

3.5

Modify Model Cards for Model Reporting Template for your organization.

  • Model Cards for Model Reporting Template customized for your organization.
3.6

Complete model card for selected project.

  • Completed model card for selected project.

Module 4: 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

4.1

Review and discuss lessons learned.

  • Summary of challenges and recommendations to systematically identify and mitigate machine biases.
4.2

Create mitigation plan to address machine biases in selected project.

4.3

Review mitigation approach and best practices to control machine bias.

4.4

Identify gaps and discuss remediation.

  • Plan to mitigate machine biases in selected project.