Artificial Intelligence (AI) has been around for a while now; however, the advent of generative AI has brought the conversations from boardrooms to dinner tables. AI is disrupting almost every industry, and most organizations will eventually become an AI business with a specific vertical. Industries as varied as medicine, marketing and agriculture will actively embrace this to introduce efficiency, precision, and customization.

AI holds fantastic promises to increase our productivity, improve our wellbeing and help us resolve some key issues of our time. However, it also brings risks, and that is why countries around the world are grappling with the best way to minimize them. Governance of AI comes in many different levels, and in this piece, I want to focus on what businesses need to do to ensure that they harness the power of AI while safeguarding their reputation and protecting their customers, employees as well as society and the environment we live in.

For this, it is imperative that companies take a moment to evaluate guardrails and embed responsible AI at the inception, not as an afterthought. Enterprises that combine innovation with ethics and privacy will have a competitive edge as they will be able to navigate times of enhanced scrutiny from regulators (think of the data protection bill) and customers alike.

In a nutshell, companies need to decide how do they want to govern their AI transformation journey. This is not an easy task – as businesses want to get the most of what AI has to offer but need to do so in a way that is long term, robust and ensures sustainable growth.

The challenges with AI

AI has been in the spotlight over recent months and, with it, customers and citizens have come to appreciate both its potential and the consequences of its unfettered deployment and use. What are the risks that we need to safeguard our companies against?

• Bias: AI tools are not neutral, but an inextricable bundle of people, subjective parameters, code, and data and as such are open to biases at any point of the AI lifecycle. AI can code existing inequalities and power structures and that could come from something as simple as gender representation among the coders. It could then extend that bias into the decision making about our future. It crystallizes the world of today into predictions, allocation of resources and therefore it could perpetuate society as it is, with its flaws.
• Transparency: AI can be difficult to understand as it self-learns. Explainability is a tricky challenge, and one that companies have to embrace if they want to create trust amongst clients and people.
• Privacy and confidentiality: Enterprise use of GenAI may result in access and processing of sensitive information, intellectual property, source code, trade secrets, and other data, through direct user input or the API, including customer or private information and confidential information. It is also important to note that using GenAI as part of enterprise processing of PII must be compliant with data privacy regulations.
• Security: There are risks using third party applications, and if the GenAI platform’s own systems and infrastructure are not secure, potential data breaches, as we have seen in recent time, may expose sensitive information such as customer data, financial information, and proprietary business information.

With this in mind, companies must prioritize a discussion around governance, and whether to embed it within the existing governance construct or to create a separate structure. Either way, the conversation must happen at the earliest opportunity.

Building a culture and practice of responsible AI

Responsible AI implementation requires a comprehensive strategy that takes into consideration all stakeholders. A foundational component is defining purpose, understanding the benefits of AI, carefully collecting required data, and examining its application. While defining AI usage policies, there are four dimensions to consider:

• Individual dimension: Policies must safeguard privacy and champion the security of personal information. They must not discriminate or undermine one´s characteristics, language, or background. And it means that companies must be open and transparent, specify which data they use, the due diligence they have undergone. In this regard, in addition to privacy controls (privacy by design, DPIAs…), companies may adopt AI impact assessment to widen the scope and zoom into issues such as fairness, transparency of outputs and human control.
• Technical dimension: AI systems must be robust and safe and protect both personal and company data. Embracing a Security by Design approach ensures that security is embedded at the onset. Install strong protection against attacks that can undermine privacy, pollute outcomes, and lead to unfairness and discrimination. Robustness must be part of the assessment of the AI product, whether it is developed in-house or purchased from a vendor. It is important to have the right skills set to evaluate the safety of an AI application.
• Societal dimension: Compliance with laws and regulations of the land is of utmost importance to avoid financial and reputational damage. Regulation prioritizes privacy with technological advancements, enabling companies to benefit while reducing risks. Clients will trust transparent AI products because they will know how it was made, where the data comes from and that it was handled responsibly. This dimension also encompasses an evaluation of the AI system on wider society – the fact that something is technically possible does not mean that a company would necessarily want to do it. This depends on the values, goals and ambition of an organisation with respect to the environment it operates within.
• Environmental dimension: Another aspect to consider is that AI systems limit their footprint on the environment. A push for synthetic data to limit data extraction and a focus on AI systems could help tackle data pollution and the greatest environmental challenges of our time, including the optimisation of resources. A smart data processing approach can also reduce the cost of data handling.

In the dynamic landscape of AI, the call for responsible practices resounds louder than ever. As industries navigate the transformative potential of AI, a balanced approach encompassing ethical considerations, vigilant governance, and strategic integration is paramount. While challenges persist, the commitment to uphold privacy, security, and fairness in AI outcomes should be unwavering. Collaboratively, through comprehensive strategies and global coordination, we are poised to harness AI's power to foster innovation that is ethically grounded and beneficial for society.

• Photo by julien Tromeur on Unsplash