Episode 8: AI Governance
Transcription:
Hello and welcome to Speedtalks, the podcast that drives deep into the latest trends in technology, especially how it’s reshaping the financial industry. I’m Tim Olszewski and today we’re diving into a crucial topic at the intersection of artificial intelligence and organisational management – AI governance. As companies increasingly integrate AI into their operations, the question of responsible oversight and ethical management of AI systems becomes crucial. Join us as we explore what AI governance entails, why it’s essential for modern businesses, and what are the key elements that contribute to a mature, responsible AI governance.
The Promise and Challenges of AI in Business
Let’s start with a brief story. Imagine an organisation that decides to implement an AI driven system to enhance customer interactions and decision making. These kinds of AI systems are one of the most adopted use cases on the market at the moment. Such systems promise to improve efficiency, personalise customer experiences and streamline operational workflows. However, behind these promises lies an important question. How will the system handle sensitive data? What if the AI makes biassed decisions? And, crucially, who in the organisation is responsible for overseeing these potential risks? These questions lie at the heart of AI governance, a structured approach to managing AI’s impact within a business.
Why AI Governance Is Crucial
As AI evolves from a novelty to an essential tool lately, companies are learning that treating AI as a curiosity without robust governance can lead to severe consequences. Poorly managed AI systems can introduce biases, compromised data privacy, and even damage a company’s reputation. To navigate this landscape, organisations need a structured governance model that can oversee the design, testing, implementation, and continuous monitoring of AI systems. This model, known as AI governance, isn’t just about compliance. It’s about establishing a workflow, a framework that guides ethical AI use, assesses risks, and ensures that AI systems align with both organisational goals and societal values.
Building an AI Governance Model
In developing an AI governance model, experts point out that businesses should consider a few core elements. First and foremost, they must stay aligned with both regulatory requirements and ethical standards. As systems, especially AI systems that interact with users or influence decision making needs a governance model to maintain transparency, explainability and accountability. This means that organisations must clearly document how these systems work and make their decision making processes understandable for all stakeholders, for everyone. Everyone, users, especially users. Today, compliance verification is mainly a manual work. A process with high risk exposure, as mistakes happen. This problem, together with compliance monitoring necessity implies an urgent need for automation. At Speednet, we invest heavily in compliance automation, precisely, with solutions like AI Auditor that allows risk management and compliance departments to transform and adapt to new reality.
Additionally, companies need to implement security protocols to protect both. The AI system itself and those interacting with it. With explainability at the forefront, businesses can ensure that their AI systems are not some black boxes, but instead transparent, interpretable and trustworthy.
By maintaining this clarity, organisations can make informed decisions about when and how to leverage AI. Particularly, in critical areas like customer interactions or automated decision making. So let’s think about what actually makes a mature AI governance model because we talk about these high level conceptual workflows and frameworks. So what makes an AI governance model a mature one? So a mature AI governance model considers the specific needs and scale of business. Some companies may choose to purchase ready-made AI models that simply need training and calibration, while others may invest in building custom AI solutions. Each path requires a tailored approach to governance. For instance, smaller companies might only need a basic level of governance, while larger multi dimensional corporations must adopt a more sophisticated model that considers international laws, ethical standards and operational impacts.
Practical Steps for Effective AI Governance
In practise, guys, let’s focus on the practise. Let’s go over some practical steps for organisations to consider and currently implementing AI in their departments. Let’s talk about data management. You as an organisation have to ensure you have high quality, relevant data that aligns with your AI systems objectives. You have to implement robust data control and management mechanisms to safeguard against biases and inaccuracies. This is one of the most crucial parts of the AI governance model and every kind of a system that you know uses AI models.
Another aspect is model and algorithm supervision itself. An organisation has to establish some kind of an oversight for the development and deployment of AI models. You have to maintain transparency in process and account for potential algorithmic biases. The outcome is very crucial in this supervision process. So outcome verification, you have to regularly assess AI generated outcomes. You have to ensure that they meet the intended objectives. This continuous verification is really the key to maintain system accuracy and reliability and of course your own compliance.
Another thing is the monitoring bodies itself within the organisation. You have to assign dedicated oversight team to monitor AI operations. Their role is to catch and address potential issues before they can escalate and ruin or damage your PR or even worse. The machine learning oversight itself is really tricky.
Another thing an organisation has to consider is the leadership. The leadership has to understand the AI systems training methods and applications. This specific oversight should encompass the entire life cycle of machine learning processes and the key thing here is training. Upskilling, so, well, leadership has to learn some new things about AI.
And the last but not least is the business impact and reporting itself. The organisation has to recognise that data models influence everything from financial reporting to customer interactions. Be proactive in preparing the organisational challenges and changes that AI integration may bring to your organisation. By addressing these areas, you can lay a very solid foundation for responsible AI management. You can prepare your organisation for both current challenges and future developments which are very, very likely to come quite soon.
Common Questions About AI Governance
For the final part of our podcast, let’s tackle some questions that organisations often have about the AI governance itself.
Question #1 is, well, is a governance model really necessary for all AI applications? And the answer that we provide always is yes. Even if AI is only a small component in your organisation, a governance model can ensure that AI is used responsibly and up to any regulations, especially those internal. It mitigates risks and sets up a framework for continuous monitoring and improvement.
Another question is what role does data quality play in AI governance? This is the point number one that we underlined in the previous section. Well, data quality is foundational. Again, high quality, well managed data allows for accurate, reliable AI predictions. Without strict data governance you risk introducing biases which can lead to flawed AI decision making.
Another question is how can AI governance align with ESG principles? This is very often brought up question during conversations. The thing is, AI governance supports ESG by managing energy consumptions very often. It ensures fair and ethical stakeholder interactions in social areas. It can safeguard corporate reputation through governance so a responsible AI governance model considers all three areas. It does contribute to an organisational broader sustainability goals. So the answer is, it definitely does align with ESG principles if it’s managed properly.
What are the consequences of neglecting AI governance? Guys, this is very important question and the answer is neglecting AI governance can lead to biassed AI outcomes, data breaches, reputational damage. Over time, this can reduce customer trust and impact business valuation.
The last question I want to bring here is people often ask are there some universal regulations for AI governance and the answer is partially yes. We have some standards being developed right now like ISO 42,000 or NIST framework. AI Act is one of the regulations that you know imply some of the key areas that, you know, involve AI governance. But for now through legislation, you know, legislation is sometimes being considered as developing. However, companies should be proactive in establishing governance practises as regulations will likely impose stricter requirements in the future. So it’s a work in progress, you can say. Companies should should, for now, use those standards in development as well as legislation documents as navigation points, much more than universal regulations.
The Strategic Value of Responsible AI
As a summary, AI is revolutionising the way organisations operate. But with innovation comes responsibility, a great one. As regulations evolve, organisations that have already laid a strong governance foundation will be well positioned to lead with confidence and integrity.
This has been another episode of Speedtalks where we keep you up to date on everything tech in the financial world. I am Tim Olszewski and remember – a well thought out AI governance model is not just a safeguard, it’s a strategic asset that enables responsible AI use, fosters stakeholder trust and alliance with corporate values. Thanks for listening and I’ll see you in the next episode.
