Good Intentions, Bad Outcomes: The Path to AI Runs Through Strong Governance

Good Intentions, Bad Outcomes: The Path to AI Runs Through Strong Governance

Good Intentions, Bad Outcomes: The Path to AI Runs Through Strong Governance

Written by Itay Inbar, Principal

In the world of artificial intelligence, the road from concept to deployment is fraught with challenges – oftentimes posed by the models themselves, especially when they produce outcomes that deviate from historical accuracy and ethical standards.

Google's recent controversy with its AI image generator, Gemini, serves as a case in point. Intending to promote diversity (a noble cause), Gemini generated images that inaccurately portrayed historical figures, sparking significant backlash that highlighted the delicate balance between inclusivity and accuracy.

This incident underscores a broader issue within AI development: the journey from good intentions to successful outcomes necessitates robust governance and rigorous evaluation mechanisms. Without these, even well-intentioned AI models can lead to outcomes that not only fail to meet their objectives, but also potentially cause harm or misrepresentation.

While the largest companies like Google have sufficiently strong brand power to withstand events like this, the potential ramifications of such an event happening at another enterprise may have a long-term devastating impact. Ensuring appropriate responses and avoiding potentially harmful ones is a key barrier to widespread enterprise AI adoption, particularly in customer-facing applications.

Navigating the path from concept to impactful deployment demands a comprehensive framework grounded in high-quality data, adherence to ethical and accurate prompting guidelines, and robust governance and evaluation strategies – both before and after deployment. Together, these components ensure that models not only output valuable results but are also ethically sound and socially responsible, underpinned by continuous monitoring to adapt and improve over time.

This framework creates opportunities for exciting new startups to emerge, which we are already seeing both in Israel and across the globe – from optimizing and governing data quality, through model testing and evaluation, real-time guardrails, and other areas, there exists a tremendous need to improve the toolkit that enables enterprise AI deployment, creating ample opportunity for startup innovation.

The Blueprint: Model Guidelines

Clear, comprehensive model guidelines act as the blueprint for AI development, delineating the boundaries within which AI must operate. These guidelines should specify not only the technical standards but also the ethical and historical accuracy standards that models should adhere to. In the case of Gemini, the lack of nuanced guidelines led to the generation of historically inaccurate images, revealing a gap in the model's governance framework.

The practice of "Prompt Engineering," a term that has gained traction lately, plays a crucial role in setting these guidelines. Done correctly, it can steer AI models towards desired outcomes; however, missteps in this area can lead to incidents like Google's recent controversy. In response to these challenges, we anticipate the introduction of more sophisticated tools aimed at refining the foundational root prompts of enterprise models. These improvements aim to enhance the quality of AI responses while ensuring compliance with ethical standards and regulatory requirements.

The Key: Governance and Evaluation

Ensuring these strategies yield the intended outcomes necessitates a comprehensive set of evaluation tools to assess performance before launch. Like in the software development lifecycle, a stringent testing framework, complemented by a robust toolset for implementation, is indispensable prior to deployment in a production environment. For LLMs, these evaluative processes are still in their early innings.

The landscape is ripe for innovation, with opportunities ranging from tools that assess performance KPIs—not just for accuracy but also for ethical and regulatory compliance—to those that streamline manual evaluations, enhancing feedback and iteration cycles for refining prompt engineering. Moreover, the potential for leveraging AI to automate these processes, despite the current cost barriers, suggests fertile ground for new companies and platforms to emerge. We anticipate significant advancements to be made in this domain.

Google's response to the Gemini backlash—pausing the service and promising to refine the model—highlights the necessity of having mechanisms in place to identify issues of this sort in advance before they meet customers.

Continuous Monitoring for Sustainable AI

Even if all seems to be in-line at first, ongoing monitoring and user feedback mechanisms post-deployment are essential to ensure that AI models remain aligned with changing ethical standards and societal expectations over time, uncovering fringe cases that elude identification. These mechanisms enable developers to quickly identify and correct any biases or inaccuracies, maintaining trust and reliability in the long run.

Just as observability is key in traditional software, LLMs necessitate going beyond just monitoring of technical performance to real-time guardrails that identify adherence to model governance guidelines. We are already seeing an abundance of new contenders vying for a leadership position in this domain, which is becoming of great importance.

The Emerging Opportunities

In summary, the Gemini controversy not only highlights the challenges in AI development but also underscores a significant opportunity for startups. In this evolving landscape, there's a burgeoning domain for companies specializing in AI governance, evaluation tools, and data quality management.

Each area presents a massive opportunity to provide needed tools and expertise to ensure AI models are developed with ethical considerations and historical accuracy at the forefront.

From offering sophisticated data auditing services to developing advanced bias detection algorithms and evaluation platforms, startups have the agility to innovate rapidly. By filling these critical gaps, startups can provide enterprises with the much needed confidence to deploy AI at scale without unintended consequences.

As AI continues to evolve, the lessons learned from incidents like the Gemini controversy are clear: good intentions are not enough. The future of AI deployment must be built on a foundation of strong governance which will only materialize through new enabling solutions.



As an active investor in data infrastructure and AI applications, we look forward to continuing to engage with up-and-coming market leaders in the space and taking part in enabling its growth. If you’re building in this space, please don’t hesitate to reach out.