What is AI Ethics?

I want to start off with talking to you about  three things that keep me up at night, right? Three things: the first, and it may be, you  know, very common for you too, is climate change. Climate change absolutely keeps me up at night.

The second thing that keeps me up at night is that people may have no idea that an artificial intelligence is making a decision that directly impacts their lives - what percentage interest  rate you get on your loan, whether you get that job that you applied for, whether your kid gets  into that college that they really want to go to. Today AI is making decisions  that directly impact you. The third thing that keeps me up at night  is: even when people know that an AI is making a decision about them, they may assume  that because it's not a fallible human with bias, that somehow the AI is going to make a decision that's morally or ethically squeaky clean, and that could not be farther from the truth.

So, if you think about organizations  and what happens over 80% of the time proof of concepts associated with artificial  intelligence actually gets stalled in testing and more often than not it is because people do not trust the results from that AI model. So, we're going to talk a lot about trust, and when thinking about trust (I’m going to  switch colors here) there's actually five pillars. OK, when you're thinking about what does it  take to earn trust in an artificial intelligence that's being made by your organization or being procured by your organization: five pillars.

The first thing to be thinking about  is fairness. How can you ensure that the AI model is fair towards everybody in particular historically underrepresented groups. OK, the second is explainable is your AI model explainable such that you'd be able to tell somebody, an end user, what data sets were being  used in order to curate that model, what methods, what expertise was the data lineage in provenance  associated with, how that model was trained.

The third: robustness. Can you assure end users  that nobody can hack such an AI model such that a person could disadvantage  willfully other people and or make the results of that model benefit  one particular person over another? The fourth is transparency.

Are you telling  people, right off the bat, that the AI model is indeed being used to make  that decision and are you giving people access to a fact sheet or metadata so  that they can learn more about that model? And the fifth one is: are you  assuring people's data privacy? So, those are the five pillars.

OK, now  IBM has come up with three principles when thinking about AI in an organization. The first being that the purpose of artificial intelligence is really meant to be to augment  human intelligence not to replace it. The second is that data and the insights from  those data belong to the creator alone OK, and the third is that AI systems, and I would  opine the entire AI life cycle, really should be transparent and explainable, right?

So, so, those are the five pillars. Now, the next thing I want you to remember as  you're thinking about this space of earning trust and artificial intelligence is that this is  not a technological challenge. It can't be solved with just throwing tools and tech over some kind  of fence.

This is a socio-technological challenge. "Social" meaning people, people, people.  Socio-technological challenges because it's a socio-technological challenge it  must be addressed holistically, okay?

"Holistically" meaning there's three major things  that you should think about. I mentioned people, people the culture of your organization, right?  Thinking about the diversity of your teams, you know, your data science team.

Who is curating  that data to train that model? How many women are on that team? How many minorities are on that  team, right?

Think about diversity. I don't know if you've ever heard of the the "wisdom of  crowds". That's actually a proven mathematical theory: the more diverse your group of people,  the less chance for error, and that is absolutely true in the realm of artificial intelligence.

The second thing is process or governance, right? What is it that use your organization  what are you going to promise your both your employees as well as the market with  respect to what standards you're going to stand by for your AI model in terms of things like fairness  and explainability accountability, etc. , right?

And the third area is tooling, right? What are  the tools, AI engineering methods, frameworks that you can use in order to ensure these  things, ensure those five pillars, and we're gonna do a deep dive into that as well, but the  next show that I’m going to be running with you we're actually going to be talking about this  one. About people and culture.

So, stay tuned. If you like this video and series, please  comment below stay tuned for more videos that are part of this series and to  get updates please like and subscribe.