Causal AI - The future of B2B SaaS

Why Causal AI is the next step for AI adoption by SaaS Apps

The future of SaaS lies in both reinvention and building adjacencies around AI. SaaS vendors must leverage AI to reinvent their processes to automate tasks, generate insights, and offer personalized experiences. Building adjacencies through APIs by integrating into existing services like Open AI might help SaaS vendors to expand their product portfolio to increase competitiveness, attract investment and possibly revenue streams, but is this a big enough moat on innovation into AI? What are the risks in taking this approach?

In recent years, artificial intelligence (AI) has made significant strides in helping us understand complex phenomena with Gen AI tools like Chat GPT accelerating the movement. However, to fully harness the potential of AI, it is vital to address bias, promote diversity, and establish regulatory frameworks in order to ensure trust, adoption and to dissuade bad actors. In this blog, we will delve into a branch of causal AI which arguably should have come before Gen AI and explore its importance. We discuss why Causal AI has emerged as a key tool for unravelling cause and effect relationships to address bias, promote diversity, and establish regulatory frameworks.

With the growing concern about AI being a threat to humanity and AI trailblazers such as joining the growing list of experts such as Geoffrey Hinton sharing their concerns about the rapid advancement of artificial intelligence. It can feel paralysing for SaaS ventures considering how they innovate around AI without joining a hype cycle fraught with negative press. ?

“It is hard to see how you can prevent the bad actors from using it for bad things,” Hinton said in an interview?with The New York Times.

But the real concern here is that Gen AI and most Black Box AI models operate behind closed doors, unobserved, built and controlled by a monitor who are not representative of society and fed data which is clearly biased and inaccurate that fundamentally erodes trust and confidence in the technology.

At the AWS Summit in London this week I was talking on a panel about Causal AI and how AI is an evolution not a revolution, its just we skipped a step. Causal AI should have come before Gen AI. It is why many AI pioneers are so worried. Casual AI is the step that ensures AI models are observable and can be informed by interacted with by anyone. Ensuring a larger more diverse and representative community of builders and contributors can analyse how the AI works and challenge the inputs and outputs of these engines. This is essential to challenging bias in data sets, questioning the ethical principles of the applications of AI and ensuring the many, not the few benefit. Observability creates accountability.

But what is Causal AI?

Causal AI goes beyond simple correlations and explores the underlying causal relationships between different factors. By dissecting data, it helps us identify the true causes that lead to particular outcomes. This deep understanding of cause and effect can be invaluable in decision-making and problem-solving across various domains. Causal AI is a step-up from predictive analytics where models understand and visual cause-and-effect relationship. As businesses seek not just to forecast future outcomes but also to understand why certain outcomes occur.

There has been a growing dissatisfaction within industry with current augmented machine leaning [AML]. Nine in ten businesses fail to generate meaningful financial returns from AI investment, according to a recent global survey.?87% of machine learning projects never make it beyond an experimental phase into production, according to Forbes. Pandemic-related disruption has accelerated this trend, with machine learning models failing to adapt to changing conditions.

This is why the Human-machine symbiosis of Causal AI is such an important step in the evolution of AI.

Most current AI systems are black boxes that cannot be understood, even by their programmers. And they are often poorly aligned with human values. Causal AI is at the cutting edge of “XAI” (eXplainable AI) and AI fairness. Unlike conventional AI, which trades off transparency for accuracy, causality-based models deliver high performance and explainability. Causal AI also enables tech developers to maintain better control over AI systems, ensuring they behave as intended in production.

Dispelling Bias:

Bias can undermine the fairness, accuracy, and inclusivity of AI systems. In the context of causal AI, it is crucial to dispel bias for the following reasons:

1.?????Fairness and Equity: Addressing bias ensures that causal AI systems treat everyone fairly and equitably. By understanding and accounting for biases, we can work towards creating AI systems that provide objective and unbiased insights, eliminating discriminatory practices.

2.?????Accuracy and Reliability: Unbiased causal AI systems lead to more accurate and reliable insights. By disentangling biases from the training data, we can gain a deeper understanding of the true causes and effects, enabling us to make more precise and trustworthy predictions and decisions.

3.?????Inclusive Decision-making: Promoting diversity in AI development is essential to dispel biases. By including diverse perspectives and experiences, we reduce the risk of introducing unintentional biases. This ensures that AI systems are more inclusive, addressing the needs and concerns of all individuals and communities.

Regulating AI:

Causal AI can play a vital role in regulating AI systems by:

1.?????Detecting and Explaining Bias: Causal AI can analyze AI systems and detect biases by uncovering the causal relationships between input features and decision outcomes. By explaining how biases emerge, we can take steps to rectify them and ensure fairness in AI applications.

2.?????Guiding Ethical Frameworks: Causal AI can assist in the development of ethical frameworks and guidelines for AI regulation. By providing insights into the causal impact of different factors, it can inform the creation of rules and regulations that promote fairness, transparency, and accountability in AI systems.

3.?????Assessing the Impact of Regulations: Causal AI can help evaluate the effectiveness and consequences of regulatory interventions in AI. By analysing causal relationships, it can provide insights into how regulations influence outcomes, allowing policymakers to fine-tune regulatory frameworks for better results.

Conclusion:

Causal AI opens doors to a deeper understanding of cause and effect relationships, enabling better decision-making and problem-solving. By dispelling bias and promoting diversity, we create AI systems that are fair, accurate, and inclusive. Moreover, through the use of causal AI, we can regulate AI systems more effectively by detecting bias, guiding ethical frameworks, and assessing the impact of regulations. As product managers of SaaS Apps consider the competing priorities between customer needs and innovations like AI, Causal AI can help embrace the potential of AI rapidly but in an observable why so that the opportunities and risks can be understood whilst shaping the future product roadmap for of AI.

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Panintelligence is an embedded analytics platform for SaaS vendors. Rapidly deploy secure white-label real-time dashboards, scheduled reports and causal AI models in your SaaS app.