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Mitigate Machine Bias

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

  • 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.

Mitigate Machine Bias 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.

Mitigate Machine Bias – Executive Brief
Mitigate Machine Bias – Phases 1-4

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.

Mitigate Machine Bias – Phase 1: Understand AI Biases

2. Identify data biases

Learn about data biases and how to mitigate them.

Mitigate Machine Bias – Phase 2: Identify Data Biases
Datasheets for Data Sets Template

3. Identify model biases

Learn about model biases and how to mitigate them.

Mitigate Machine Bias – Phase 3: Identify Model Biases
Model Cards for Model Reporting Template

4. Mitigate machine biases and risk

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

Mitigate Machine Bias – Phase 4: Mitigate Machine Biases and Risk
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About Info-Tech

Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.

We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.

What Is a Blueprint?

A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.

Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.

Need Extra Help?
Speak With An Analyst

Get the help you need in this 4-phase advisory process. You'll receive 8 touchpoints with our researchers, all included in your membership.

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.

Author

Natalia Modjeska

Related Content: Business Intelligence Strategy

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