03.27.2023 Executive Data Bytes – Building Safe and Responsible AI Systems Through AI Governance

03.27.2023 Executive Data Bytes – Building Safe and Responsible AI Systems Through AI Governance

Executive Data Bytes

Tech analysis for the busy executive.

Welcome to another edition of Executive Data Bytes! This week, we are talking about AI governance, and its role in building safe and responsible AI systems.

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Focus piece: “The Strategic Building Blocks Of Ethical AI: Representation, Governance And Leadership

Executive Summary

The rules, policies, processes, and framework that govern the development, deployment, and use of artificial intelligence are referred to as AI governance. These policies apply the company's strategies, objectives, and values to the development of AI systems, as well as ensuring that legal and ethical guidelines are followed in a way that benefits society as a whole while minimizing risks and negative consequences. In this piece by Forbes, Chaitra Vedullapalli explains three building blocks that help ensure ethical AI systems work.

Key Takeaways

  • The first pillar is leadership, as they are accountable for every strategic change required in the workplace. Executive leaders must be willing and ready to drive rapid change in the workplace, as well as develop policies that ensure AI system representation and accountability. They must also enforce consequences when these policies and laws are broken.
  • Building an ethical AI system requires you to be open to diversity in the workplace and to include women and underrepresented groups in the development and deployment of AI-related products and services. There have been studies that show that unrepresented people are frequently misclassified, and having them on your team helps them spot the bias in your data, and these get fixed on time.
  • The excitement of building and developing new and life-changing technologies must be balanced with the need for accountability and transparency throughout the construction process from start to finish. Companies should be willing to explain how their AI systems work and the data used in the training process. They should also be willing to be held accountable if things go wrong.
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Focus piece: “Challenges and best practices in corporate AI governance: Lessons from the biopharmaceutical industry

Executive Summary

Artificial intelligence is a rapidly expanding field of technology that promises enormous social and economic value, but it also comes with a slew of risks to handle and manage, which, if not done correctly, may result in unintended consequences. To help mitigate these risks, businesses must embrace AI governance and learn the proper way to implement it. Frontier's article outlines extensive research on AstraZeneca, the challenges, and best practices in corporate AI governance.

Key Takeaways

  • The ability to strike a balance between risk management and innovation is a challenge for organizations looking to implement AI governance. The real question here is how companies can "err on the side of caution." When should they feel they have crossed the line and are now posing dangerous risks, or are these risks only undermining their ability to innovate? There is no one-size-fits-all solution, but organizations should strive to incorporate the AI governance mindset, in which individual teams can decide when it is a risk or an opportunity to innovate based on the scope of individual projects.
  • A second challenge in implementing AI governance is determining how to harmonize standards across all organizational divisions. Organizations have distinct areas that operate independently, and while policies and standards may not impede growth in some areas, they will undoubtedly impact the rate of innovation. As a result, organizations draw lines of what is too much based on project preferences and extensive research.
  • A question that arises when attempting to implement AI governance across organizations is how we measure our actions in order to demonstrate the success of these policies and rules. And if you decide to measure the effects of your policies on the main goals of AI governance, which are fairness, justice, ethics, and transparency, you'll find that these goals are also extremely difficult to quantify. If you set these metrics as targets, you may end up justifying Goodhart's Law, which states that "when a metric becomes a target, it becomes a bad measure." So, what's the solution? According to the study, KPIs should not be used to question the ethical nature of the organization, but rather to spark ethical debates that inform design decisions.
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Focus piece: “Artificial Intelligence Governance Strategy and Best Practices” 

Executive Summary

When fully implemented and best practices are followed, the potential benefits of AI governance will help corporations reap both the benefits of AI systems and properly manage the risks that come with them. After all of the time and effort put into developing these policies and ensuring compliance, it's only natural to wonder what's in it for your organization.

Key Takeaways

  • A well-implemented AI governance system has the potential to save businesses money. This can be seen from a variety of angles, but let's start with a regulatory one. Building safe and fair AI systems ensures that you are working in accordance with the law, allowing you to avoid large fines imposed by regulatory bodies for developing unethical systems.
  • Another advantage of AI systems is that they increase trust within and outside of the organization, as well as among employees and customers. Employees gain confidence when making decisions based on company data because of its high quality as a result of AI governance. Customers will also trust organizations if they know their data is secure and that the systems built on it are accurate, fair, and ethical. In general, this leads to corporations having a more positive reputation, which is beneficial to business.
  • AI governance also helps institutions avoid taking unnecessary and unneeded risks that could harm the company's reputation, operations, customer service, and, inadvertently, its bottom line. It assists businesses in determining which risks are worth taking, when to draw the line, and how to build AI systems in a safer and more transparent manner.
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