According to Gartner, IT spending is projected to grow 8% this year, and the rise of investments in AI is noted as one of the main drivers. Machine learning platforms and other forms of AI are being incorporated into our everyday lives, leaving consumers wary of the ethical implications when it comes to the use of their data. As behemoths, such as Google and Amazon, along with many other companies of all sizes, plan to incorporate more AI capabilities this year, it’s important to ensure new AI capabilities are integrated responsibly and ethically to avoid data security issues, general public backlash, and/or lawsuits such as the New York Times lawsuit filed against OpenAI/Microsoft.

Let’s explore ethical challenges, solutions, and key business strategies to empower organizations to implement AI into their IT systems effectively while being ethically sound.

Integrating AI into IT systems — ethical challenges and solutions

Integrating AI into IT systems and processes poses several challenging considerations and ethical dilemmas that must be addressed. Among the most difficult are bias and fairness, privacy infringement, and ethical decision-making.

  • Bias and fairness — To adequately address bias and fairness, careful consideration around the data used to train AI models is paramount. Amazon’s failed AI recruiting tool highlights the importance of fully understanding the underlying data in training models and ensuring alignment with the application of AI.
  • Privacy infringement — When we don’t consider the data used to train AI models or even the output, given the application of AI, we may inadvertently infringe on the privacy of the individuals we seek to serve with the application of AI. While well-intentioned to provide predictive insights based on purchase history, Target’s use of consumer shopping data inadvertently revealed customer pregnancies to household members.
  • Ethical decision-making — Organizations must also consider the potential consequences of integrating AI into IT systems and processes, ensuring alignment with their values and ethical principles by establishing ethical guidelines for AI development and use and ensuring all stakeholders are aware of and adhere to such guidelines.

Overcoming these challenges requires interdisciplinary collaboration among human resources, technologists, cybersecurity, and compliance. Implementing ethical AI development frameworks, conducting regular audits, and encouraging ongoing discussions about AI ethics are all crucial steps toward navigating these complex ethical considerations when integrating AI into IT systems and processes.

  • Interdisciplinary collaboration — Key stakeholders understand implications and can help an organization build governance guardrails to reduce the risk of mishaps like in the Amazon and Target examples. While our understanding of AI continues to evolve, eliminating all risks associated with integrating AI into systems and processes is not possible, but interdisciplinary collaboration will help demonstrate that genuine care and forethought remain integral to the foundation of new AI initiatives.
  • Implementing ethical AI development frameworks — While our knowledge of AI is still evolving, there are frameworks that can be leveraged to provide guidance on key ethical principles, such as transparency, accountability, and fairness, to ensure AI systems are developed and used in an ethical manner.
  • Conducting regular audits — Similar to financial, security, and other compliance measures and controls, we must conduct regular reviews to quickly identify deviations from established standards as well as verify the effectiveness of the integrated AI.

Practical recommendations for businesses

While AI can be transformational, we are still learning from past and current applications, which demands a thoughtful approach and clearly defined boundaries. Here are some practical recommendations to those who are evaluating the use of AI or are in the early stages of implementing AI.

  • Identify processes within your business that are well-suited for AI. Conduct a comprehensive risk assessment to pinpoint potential hazards and develop mitigation strategies to counteract these risks effectively.
  • Implement a governance framework that includes protocols and policies that define the roles and responsibilities for developing, deploying, and monitoring AI systems. More specifically, the framework should include procedures centered around privacy preservation, fostering inclusivity, safeguarding intellectual property, and ensuring the responsible deployment of AI. The framework should also establish ethical guidelines for the use of AI. Be sure to outline the data that can be used, how it can be used, and the permitted applications of AI.
  • Consider setting up an interdisciplinary committee that reviews all new AI projects to ensure they're aligned with your ethical guidelines.
  • Educate your workforce about AI's utility and boundaries. Help them understand where AI applications are appropriate, where AI is intended to augment human capability, and where the risks outweigh the benefits. Make AI education a part of onboarding and ongoing training. This will help to ensure that all employees have a basic understanding of AI and its ethical implications.
  • Monitor your AI systems for bias and other ethical risks. This can be done through regular audits and by collecting feedback from users.
  • Work with partners who share your commitment to ethical AI and integrate both administrative and technical measures to ensure adherence to ethical principles.

Closing thoughts

It's not enough to simply establish policies for the ethical use of AI. Ensuring its ethical application is an ongoing process that requires continuous learning and adaptation while adhering to principles of privacy, inclusivity, and responsible AI deployment. It’s imperative to recognize the potential of AI to transform our world but also acknowledge the complexities and challenges associated with it. Therefore, we must remain steadfast in our commitment to define clear boundaries, educate our workforce, and rigorously monitor AI systems to ensure they comply with ethical guidelines.

Gaudy Jandron is the chief information officer at US Signal, where she brings over 25 years of diverse industry experience in spearheading complex technology initiatives and delivering business transformation and growth. Formerly the executive vice president of information technology at EnergyUnited, Jandron is known for her exceptional ability to build and inspire high-performing teams, fostering a culture of innovation and collaboration. A true advocate for access to affordable technology, her commitment to excellence and transformative outcomes makes her a valuable asset to US Signal.