The Integrity of the Future

by Emily Wolfteich – Senior Industry Analyst

How are we teaching AI to shape our future?

Maybe I asked it the wrong question, but what interests me about this answer is that it doesn’t mention much about the integrity of the data that it learns from. The volume, yes – processing enormous amounts of sometimes conflicting data and being asked to form logical pathways and conclusions from it can sometimes lead to mistakes or unpredictability. But that’s more about the system processing mechanism. What about the data itself? 

The AI Gold Rush

This type of investment is important. It’s expensive to develop the natural language models that AI relies on – some investors estimate around $500 million – and to power the computing that allows the system to learn from the data. However, a key component of this investment must be that it funds rigorous analysis to ensure data quality. 

Without a rich, contextual, and accurate data fertilizer, what kind of flowers will we be growing?

Quality versus Quantity

1 – Accuracy and Quality

More data exists now than ever before, and the growth of Internet of Things (IoT) devices, 5G, and cloud computing means that volume is exponentially expanding. With this avalanche of data, the likelihood of inaccurate or bad data also grows – and when analyzed at scale, small mistakes can become big problems. 

2 – Enterprise-wide Integration

3 – Location Intelligence

4 – Data Enrichment

Fertilizing “1,000 Flowers”

Flowers in a field

Imagine if you were asked to describe trees, but were only given information about trees that grow in Florida. You could accurately and in detail describe the taxonomy, appearance, uses and origins of all the trees that fall under that dataset. But what would be missing? What would you not know? And, importantly, how would you identify what it is that you don’t know?

If you were only being asked about trees in Florida, of course, your knowledge would be more than sufficient. But without a complete data set, the conclusions miss the mark. 

This is one of the biggest problems facing AI and ML developers. These systems are learning from the worldview that we are providing to them. How do we know where our own blinders are? How do we ensure that our own biases are not becoming the baseline of the decision-making of the future?

Silicon Valley’s model is “move fast and break things,” but we cannot afford to let this cavalier attitude build the language of the future. The models, programs, and applications that will come out in the next few years are likely the building blocks of what we will all use going forward, from governments to businesses to high school students. We will be using it to hire people, to communicate with each other, to make funding decisions and write opinions and triage organ recipients and determine likelihood of incarcerated people to re-offend and estimate threat levels from our adversaries. If we do not act now, to ensure that these models learn and train from quality data that is an accurate and contextual reflection of what our world looks like, we will not only replicate but enshrine inequity and discrimination.

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