Machine learning is regarded as the most innovative variant of Artificial Intelligence services and solutions, using data and algorithms to replicate and anticipate the way humans think and act. And although it’s been a game-changer for many industries, in the aftermath of recent global events, machine learning has come under scrutiny for struggling to adapt when called upon to add value in a rapidly changing environment.

This all comes back to traditional vs. non-traditional machine learning (ML). Most organizations today that utilize ML are using a traditional model, in which the ML adds value by studying and learning patterns observed in the past and using that data to predict human behavior. However in the aftermath of the events of 2020, we now know that traditional ML failed to add value when plunged into uncharted territory like a global pandemic.

What causes machine learning to fail?

There are a number of elements that contribute to machine learning failures. At the core of this is often a lack of understanding of exactly what ML is—and what it is not. Traditional ML is not designed to answer the “why” questions; it can only provide answers for the “what,” the “how long,” or the “yes/no,” such as:

  • What will happen if/when my user does XYZ?
  • How long will this customer be on my website?
  • Is this activity suspicious?

To avoid the pitfalls of a ML failure, it’s important to do three things:

  • Begin with an eye on the end game: Always keep your final ROI in mind. Make sure you don’t get so mired in the technology that you lose sight of your final goal.
  • Remember that it’s a process: Building a successful ML project means gathering data, cleaning, visualizing. . .and then repeating the process again and again to fine-tune what is known.
  • Get crisp on your goals and success metrics: If you haven’t set down clear, firm, measurable guidelines for what a successful implementation will look like, it will be impossible to achieve it.

The impact of 2020 on machine learning

We know that traditional ML does a great job when the data it receives looks similar to the data it has been trained on in the past. In the real world, however, conditions (and the resulting data) can be wildly inconsistent. This became abundantly clear as the coronavirus pandemic took hold around the world.

The bottom line? We now understand that current, traditional ML modeling is unable to understand cause and effect and is therefore unable to adapt in order to add value during a crisis.

The paradigm shift to causal AI

A significant shift is required to enable traditional machine learning paradigms to efficiently adapt to unforeseen events and situations, and understanding causality is a key element in achieving this goal. As a solution, causal AI is designed to uncover and identify underlaying reasons for an event or behavior. This means that causal AI can deliver more relevant and accurate insight than traditional ML.

How Causal AI works

Causal AI takes traditional ML to the next level in three basic ways, by improving data quality, rendering more through insight, and dovetailing AI and human intelligence:

  • Improving data quality and use
    In traditional ML, data is frequently fragmented and of inconsistent quality. Connecting divergent data sets can also be problematic. By assigning common indicators across data harvesting activities usually generates the best outcomes from linked data sets. Designing common indicators to be used in all data-collection efforts in a country would help get the best from data sets once they’re linked.
  • Delivering more thorough insights
    Being armed with a full understanding of all the variables that can be driving behaviors (policies, laws, influencers, personal beliefs, inherent bias, and unique individual motivators) can result in more accurate and relevant outcomes. If there are an abundance of prior assumptions around what constitutes “important” or “business-critical” data, the underlying variables are often overlooked, and inaccurate connections may be assumed.
  • Supporting a dovetail of AI and human intelligence
    Adopting a strict data-driven approach without allowing and accounting for expert knowledge makes it impossible to solve development problems. Incorporating the input of experts has been found to vastly improve causal AI performance by utilizing practical knowledge of real-world applications and uncovering confounding variable gaps in data.

Making the move to causal AI

While Machine Learning has been revolutionary for business, recent global crises have shown that artificial intelligence is falling short. Machine learning needs to become more flexible and be able to do more in order to adapt to a rapidly changing world, and that means making the move to causal AI.

Need more information on how Causal AI can take ML to the next level for your organization?

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