Four Steps to Operationalize AI Ethics in B2B

Four Steps to Operationalize AI Ethics in B2B

Have you thought about what AI ethics might look like in your organization? This article explores an important topic and provides four guidelines.

Artificial intelligence (AI) has gained wide popularity in today’s B2B environment. With the potential to improve buyer and seller relationships, increase revenue, and create personalized marketing, it has become a powerful technology for B2B marketers.

However, with great power comes great responsibilities. Rapid, unchecked development in AI creates ethical risks, including loss of privacy, transparency, security and potential biases in decision-making. Failing to address these ethical concerns can pose a threat, exposing your business to reputational, regulatory and legal risks. Hence, developing a robust AI ethics platform is an important task for B2B leaders.

Four Steps to Create a Successful AI Ethics Program

In this article, we look at four steps to help you create an effective program to address AI ethical concerns. Be alert and focus on these four areas when developing AI practices and policies.

#1 Establish the Right Framework

The key to creating a successful AI ethics program is use of a robust framework, such as a data governance board, that places your organization’s core values, ethical guardrails and regulatory constraints at the center. The aim is to guide your organization and ensure every participant involved in the design and development of AI systems is educated, trained and empowered to prioritize ethical considerations. It brings your business leaders together to prioritize and discuss privacy, cyber, compliance and other data-related risks.

#2 Design AI Applications with Trust

Today, AI has been applied to many processes and questions. It helps streamline digital advertising, improve customer experience, and increase sales and revenue. Despite these transformative perks, 88% of respondents say they don’t trust AI-based decisions. They believe the more you depend on AI for decisions, the more a business becomes exposed to risks to reputation, regulation, employment and privacy. 

Designing a trustworthy AI solution is a crucial step towards successful AI ethics policies. This involves being able to explain how AI systems interact with customers, what information the system gathers, and from what sources. Moreover, it helps avoid unintended consequences caused by poorly considered AI applications and protects your company from reputational, regulatory and legal risks.

#3 Monitor Frequently

Though AI can enable advanced and automated products and services, these may fail in unusual and unpredictable ways. For example, Amazon engineers spent years working on AI and invented a recruiting tool. But after one year of deployment, the company scrapped the program, because they couldn’t understand the process to create a model that didn’t systematically discriminate against women.

To avoid such failures, AI applications need supervision. An application is never one-and-done. It requires the development team to monitor practices and observe how the software performs. Moreover, AI applications need regular auditing to measure value-driven metrics, such as accountability, bias and cybersecurity.

#4 Offer Proper Training

With technology-led changes accelerating business operations, investing in continuous learning and training to maintain a qualified workforce must be a top priority. However, this needs an integrated approach, including:

  • Educate teams on how AI will be integrated into operations and why
  • Inform them where and how AI can improve day-to-day roles
  • Engage employees to learn how people, processes and AI-driven technology collaborate
  • Develop skill sets in employees to seize AI advantages for better outcomes

Ready to Learn More About Ethics for AI ?

Register for the True Influence April Summit today and hear live from panelist John C Havens. He’s Executive Director of the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (IEEE AIS Ethics Initiative.)

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