Getting to confidence in AIGetting to confidence in AI
Getting to confidence in AI
March 6, 2020
by Jennifer Chase, SAS 6 March 2020
The transformative potential of AI becomes more self-evident every day. AI has reshaped how we interact with the world – from the daily comforts of recommendation systems and voice-activated assistants to lifesaving insights driving medical and scientific discoveries. There’s no sign of slowing down.
Today, AI enables organizations to do more with data, automate time-consuming tasks and improve the results of decisions. Still, there remains a lack of confidence about the capabilities of AI and its results. It’s clear that AI success doesn’t lie in algorithms, neural networks or predictive models, but rather something much more fundamental: trust.
Yet waiting for AI to be perfect is not an option. That’s why the key to mitigating risks is understanding them. By being able to discern the real risks from the perceived ones, and assessing your risk tolerance, you can build a clear-eyed strategy that realizes the full potential of AI for your organization's needs.
So how can you drive confidence in AI? Perhaps most importantly, you need to have trust in your data. After all, the output is only going to be as good as the data you input. If your data contains inherent biases, this will undoubtedly affect your model to produce biased outcomes. And in order to achieve the best results, you need to be able to access, integrate, cleanse and prepare data for analysis; otherwise, incomplete data sets or inaccurate data will produce – you guessed it – incomplete or inaccurate results.
Transparency of the models and
interpretability of the results too, is critically important. Unfortunately –
and ironically – the same AI system that provides extraordinary predictive
abilities can also be extremely opaque. Besides requiring enormous amounts of
data, the algorithms inside the black box models generally do not provide a
clear explanation of why they made a certain prediction.
That’s why properly implemented governance is crucial. Analytical models need to be backed by business rules that promote objective, repeatable actions so that you can work faster while also safeguarding the integrity of information.
SAS defines AI as the science of training systems to emulate human tasks through learning and automation. From commerce to science to education to health care, the true strength of AI is in humans and machines working together. What makes AI so promising is its ability to enhance human creativity, endeavors and decisions.
Confidence in AI may not come easy. No matter the industry or application, there will need to be safeguards to ensure that AI systems’ decisions are not only accurate, but ethical. There will be undoubtedly growing pains, missteps and miscalculations. That goes with the territory when exploring the unknown.
But if you create an AI strategy using clearly defined tactics, you can usher in a new era of decisive clarity for your organization and your stakeholders. Because AI’s potential for good is unmistakable. And as more of that potential comes to life, trust in it will follow.
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