What Does an Ethical Approach to A.I. Look Like?

The spirit of innovation is mostly positive. After all, innovation  leaps humanity forward. The wheel enabled transportation, space exploration led to the internet, the smartphone has connected the world.

These are just some examples.  

And the innovation wheel keeps turning. These days there is no doubt that  

For our brief history of breakthrough innovation, we’ve rarely had to discuss or think about the ethics behind those efforts like we do with the next breakthrough; Artificial Intelligence and Machine Learning.

A.I and Machine learning  are widely considered to be the tools that will leap humanity forward into the future, but there’s a catch...

… who decides the ethics of AI and ML? Who tells the computer how it should think? What should the computer value? What is ethical? And at what point have we gone too far?

“This technology is not fully developed, It's not an end state. So we don't know all of the consequences of using this technology.”

When we think about innovation, we think about all the good that will come from it, and rarely think about the consequences that innovation leaves in its wake. Someone is thinking about that, though, and that someone is Beena Ammanath. As the Executive Director at Deloitte’s Global A.I. Institute., It’s her job to go through all the “what ifs” of A.I.  

On this episode of IT Visionaries, Beena explains how she weighs all the outcomes, and she discusses the three paths companies are currently pursuing ethically A.I. .  

She also talks about why trustworthy A.I. and machine learning will be the secret to all successful technology breakthroughs. Enjoy!

Main Takeaways

• Can I Trust You?: Trust must be at the center of all your A.I. models, which means you need a clear understanding of the data sets you are using and if the data you are using to build your algorithms is reliable. When using third party data, make sure you have a clear understanding of how that data was collected, what the subject matter was and if it truly fits into your modeling. When you can’t trust your data  sources, you end up with biases in your algorithms.
• The Secret Sauce: A.I. and machine learning continue to be the two big underlying pieces of technology that businesses are using today because of their ability to consistently digest data and learn on the fly. Traditional software used to rely on updates that may arrive every six months, now machines can continually be evaluated and taught new techniques at a moment’s notice.
• Driving Adoption: Getting people to use your product is always goal number one, but with A.I., and really any new form of technology, consumer adoption is key because in order for A.I. and machine learning to be successful, it’s reliant on the continuous feedback loops it gains from its users. When you’re designing UX, you need to think about how you are going to drive adoption upfront, and not just about how the technology is going to be deployed.

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