Ethics of AI Adoption

Ethics is understanding and applying the moral principles of right and wrong that govern a person’s behavior or actions.

Because so much of what we are doing with AI is mathematics-based and technologically exciting, we sometimes tend to forget the bigger mission of AI – to deliver meaningful, relevant, responsible, and ethical outcomes. Ethics needs to underpin the design, development, and deployment of all AI models, which means everyone needs to understand what is necessary to embed responsible and ethical choices into our AI models. To prepare Citizens of Data Science for those all-important ethical conversations and design decisions, we’ll discuss the following topics in this chapter:

• A foundational understanding of what ethics is
• Exploring the relationship between economics and ethics
• Leveraging...

Understanding ethics

Ethics are the moral principles governing a person’s behavior or actions, the principles of right and wrong generally accepted by an individual or a social group. Or as my mom used to say, “Ethics is what you do when no one is watching.”

AI ethics, on the other hand, is a field of study that focuses on AI’s ethical development, application, and management. AI ethics involves identifying and exploring the potential unintended consequences of AI and considering how AI can be used fairly and responsibly to benefit society.

Ethics is proactive, not passive

Ethics is fundamentally proactive rather than passive. It demands taking appropriate actions in alignment with society’s moral standards, instead of merely delegating such responsibility to others. Understanding the distinction between passive and proactive ethics is crucial, and one can find a valuable lesson in the timeless Parable of the Good Samaritan from the Bible...

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Value Engineering Competency understands how organizations can leverage data (Big Data) and advanced analytics (AI / ML) to create value.

AI possesses the potential to drive systematic improvements across a broad range of industries and business functions. The number of business and operational use cases around which organizations can apply AI to create new sources of value is almost unbounded, and that’s the problem.Organizations don’t fail because of a lack of use cases; they fail because they have too many.While many universities and organizations are focused on training more data engineers, data scientists, and ML engineers, we need more folks who can drive organizational alignment and consensus on identifying, validating, valuing, and prioritizing the business and operational use cases that deliver meaningful, relevant, ethical outcomes.We need more business professionals who can translate...

What is economics? What is “value”?

Economics is the branch of knowledge concerned with the production, consumption, and transfer of wealth or value. Defining value, however, is a tricky proposition. Most organizations have historically relied upon traditional financial measures of value creation, such as:

• Revenue
• Gross Profit Margin
• Operating Profit Margin
• Net Present Value (NPV)
• Internal Rate of Return (IRR)
• Return on Investment (ROI)
• Return on assets (ROA)
• Debt-to-equity ratio
• Earnings per Share (EPS)

As discussed in Chapter 5, many of these financial measures are lagging indicators; their results depend on the performance of related independent or leading indicators. Organizations must embrace a broader range of measures that define value than just financial measures. If not, these organizations risk creating AI models that result in severe confirmation bias and potentially dangerous unintended consequences.Organizations that want to embrace AI as an engine of meaningful...

Data and AI Analytics Business Model Maturity Index

The Data and Analytics Business Maturity Index provides a roadmap for helping organizations become more effective at leveraging data and analytics to power their business and operational models. It provides a benchmark against which organizations can measure their data and analytics progress and effectiveness to understand what good execution looks like from a value creation perspective.ADDLet’s review the different stages of the Data & AI Analytics Business Model Maturity Index, the key features and capabilities of each stage, and understand the action plan for navigating the maturity index from retrospective business monitoring to creating a cultural of continuous learning and adapting.

Stages

ADDWhile I was teaching at the University of San Francisco several years ago (where I was an Executive Fellow of their business school), we decided to launch a research project to understand how effective organizations were in leveraging...

Value Engineering Framework

One cannot determine the value of one’s data in isolation of the business.The Value Engineering Framework deconstructs an organization’s Strategic Business Initiative or business challenge into its supporting business components (including stakeholders, decisions, KPIs, and use cases) and supporting data and AI analytics requirements. It starts by understanding how your organization creates value, identifying the internal and external stakeholders involved in that value-creation process, pinpointing the Key Performance Indicators (KPIs) and metrics against which value-creation effectiveness will be measured, and prioritizing the decisions that the stakeholders need to make to support the organization’s value creation processes (Figure 4).

Figure 7.4: Value Engineering Framework

The Value Engineering Framework is a simple process that requires close collaboration across the broad spectrum of internal and external stakeholders who either...

Summary

Yep, I said this would be a long chapter, and it was. This chapter represents the culmination of ultimately how organizations will measure the effectiveness of their AI and Data literacy programs. Ultimately, driving value is why all organizations – large corporations, small businesses, non-profit organizations, educational and healthcare institutions, and government agencies – exist. We covered much ground about how organizations leverage their data to create value. And that data-driven value creation conversation starts by understanding how the organization defines value and identifying the KPIs and metrics against which these organizations measure their value creation effectiveness. Once the organization understands how it defines and measures value creation, we move into the concepts of nanoeconomics, analytic profiles, and business and operational use cases to realize or create value for the organization.The process is straightforward and pragmatic: If you don...