Defining Your Company’s AI Ethics Protects and Pays Off

If data governance is the final frontier, then artificial intelligence (AI) and machine learning (ML) are the methods by which we can settle and develop that fertile ground. But for companies navigating the complex and growing world of AI/ML, getting buy-in and gaining momentum can be a challenge.

The best place to start is with a company’s values; those values are important in guiding the development and growth of using AI/ML in a company’s processes. Many companies tout their values, but few believe they have the resources and bandwidth to commit to implementing them effectively with AI/ML.

Companies must have deep, consistent discussions about these technologies, down to the level of the algorithms and datasets, and their impact on the company and the people they serve. The first steps to guiding these discussions include establishing an AI ethics committee and clarifying and sharing the organization’s working definition of ethics.

Defining ethics by understanding its roots

The word “ethic” is derived from Greek, originally meaning “custom” or “character.” Just as the term “ethics” has evolved over time, companies should establish core customs that every team in a company understands, acknowledges, and accepts as they grow. Those customs should also guide how a company expresses its character, especially when leveraging customer data.
That common understanding can also evolve and serve as a parlance that can lead to faster innovation in AI/ML, helping teams discover and develop efficiencies as they grow.

Most leaders and executives understand the significance of data governance–managing, using, and protecting the integrity of data is critical. But establishing an AI ethics committee provides a platform for developing good AI habits that can accelerate innovation. Creating those processes requires a kind of deconstruction.

That deconstruction process isn’t necessarily intuitive; while executives and leaders may recognize the need, securing buy-in can be an uphill battle. However, committing to the process can build the muscle of coordination and yield exponential dividends: deeper thought processes, encouraging questions, and empathy modeling.

Establishing governance best practices for ethics

Creating an AI ethics committee is just the beginning of building a solid foundation. Consider these best practices to ensure both short-term gains and long-term success.

  • An appetite for risk: Not every company is ready to address the potential for unethical uses of AI/ML and data. That hesitation may be a calculation of risk appetite, or how much capacity a company is willing to give to risk.

    Begin by weighing those negative outcomes with the potential benefits of introducing AI/ML into data governance. Once a company identifies which risks can be addressed, its teams can address the potential positive outcomes.

  • One step at a time: The data governance process will never be short, and introducing an AI ethics committee will take even more time. That’s why it’s important to prioritize goals by taking a tiered approach to implementation.

    Categorize AI/ML processes based on their impact, their prevalence in the market, and the knowledge required to maintain those processes. An effective AI ethics committee can help collect and leverage that knowledge by working directly with an organization’s key stakeholders.

  • One size doesn’t fit all: Just as AI/ML can affect companies in different ways, it can also impact a company’s teams and departments differently. Leaders should take the time to determine whether AI/ML should be a company-wide initiative or should start within a single team or department.

Begin your journey toward ethical AI

The world of ethical AI can be a scary one, but companies don’t have to go through it alone. Reach out to Resultant and tap into our governance expertise as the first step toward a strong and successful journey to ethical AI/ML.

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