How companies can responsibly embrace generative AI
Generative AI is one of the most transformative technologies of the modern era, with the potential to fundamentally change how we do business. From boosting productivity and innovation to ushering in an era of augmented work where human capabilities are enhanced by AI technology, the possibilities are endless. But these possibilities also come with risks. We’ve all heard stories of AI hallucinations that present fictional data as fact, and experts warning of potential cybersecurity vulnerabilities.
These stories highlight the numerous ethical issues that businesses must address to ensure this powerful technology is used responsibly and benefits society. Fully understanding how AI systems work can be challenging. Addressing these issues and building trusted and ethical AI has never been more important. To ensure responsible adoption of the technology, businesses must embed both ethical and security considerations at every stage of the journey – from identifying potential AI use cases and their impact on the organization, to actual AI development and adoption.
UK Chief Technology & Innovation Officer at Capgemini UK.
Responding to AI risks with caution
Many organizations are taking a cautious approach to AI adoption. Our recent research revealed that despite 96% of business leaders considering generative AI a hot boardroom topic, a significant portion of companies (39%) are taking a ‘wait and see’ approach. This is not surprising, given that the technology is still in its infancy.
But leveraging AI also offers a strong competitive advantage, so first movers in this space have much to gain if they get it right. Responsible adoption of generative AI starts with understanding and addressing the associated risks. Issues like bias, fairness, and transparency should be considered from the outset, when exploring use cases. After conducting a thorough risk assessment, organizations should devise clear strategies to mitigate the risks identified.
For example, implementing safeguards, ensuring the governance framework for overseeing AI operations is in place, and addressing intellectual property rights issues. Generative AI models can produce unexpected and unintended outcomes, so continuous monitoring, evaluation, and feedback loops are essential to stop hallucinations that could cause harm or injury to individuals or organizations.
AI is only as good as the data that powers it
With Large Language Model (LLM), there is always the risk that biased or inaccurate data will compromise the quality of the output, creating ethical risks. To address this, companies need to put in place robust validation mechanisms to check AI outputs against trusted data sources. Implementing a layered approach where AI outputs are reviewed and verified by human experts can add an extra layer of security and prevent the spread of false or biased information.
Ensuring that private corporate data remains secure is another key challenge. Establishing guardrails to prevent unauthorized access to sensitive data or data breaches is essential. Companies should use encryption, access controls, and regular security audits to protect sensitive information. By establishing guardrails and orchestration layers, AI models will operate within secure and ethical boundaries. Additionally, using synthetic data (artificially generated data that mimics real data) can help maintain data privacy while enabling AI model training.
Transparency is key to understanding AI
Since the introduction of generative AI, one of the biggest challenges to its safe adoption has been the lack of broader understanding that LLMs are pre-trained on massive amounts of data, and the potential for human bias as part of this training. Transparency around how these models make decisions is essential to building trust among users and stakeholders.
There needs to be clear communication about how LLMs work, what data they use, and what decisions they make. Companies need to document their AI processes and provide stakeholders with understandable explanations of AI operations and decisions. This transparency not only builds trust, it also ensures accountability and continuous improvement.
Additionally, it is crucial to create a layer of trust around AI models. This layer involves continuously monitoring for potential anomalies in AI behavior and ensuring that AI tools are pre-tested and used safely. By doing so, companies can maintain the integrity and reliability of AI output and build trust among users and stakeholders.
Finally, developing industry-wide standards for AI use through collaboration among stakeholders can ensure responsible AI implementation. These standards should include ethical guidelines, best practices for model training and deployment, and protocols for dealing with AI-related issues. Such collaboration can lead to a more uniform and effective approach to managing the societal impact of AI.
The Future of Responsible AI
The potential of AI cannot be overstated. It allows us to solve complex business problems, predict scenarios and analyse vast amounts of information that can give us a better understanding of the world around us, accelerate innovation and aid scientific discovery. However, as with any emerging technology, we are still in the learning curve and there is a lack of regulation. Therefore, proper care and consideration must be taken when implementing it.
Moving forward, it is critical that companies have a clear strategy for safely adopting generative AI, which involves building guardrails into every phase of the process and continuously monitoring risks. Only then can organizations fully realize its benefits while mitigating its potential pitfalls.
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