Being biassed is alright — sometimes

We don’t really refer to people as being biased. We all just have our references and preferences. It’s actually what makes us human. It just means that the information that we have isn’t objective but rather formed by opinions or influences from other people. We look at information through glasses that are colored by our opinion. If two people hear the same story, they will react to it in different ways. They still might share the same emotion, for example when hearing about a tragedy, but they will both associate different feelings and memories to that event. Your own experience, upbringing, and many other factors will determine what that information means to you and how you wish to deal with it. Someone might find the news devastating, while someone else who’s been through a lot, might not be as shocked. This means that world events don’t mean the same to everyone.

We load AI with clean data — or do we?

So how does our opinion affect data when looking at the technical side? We feed applications with historical data related to the topic we want it to learn about. Next, we tell it what type of results we would call “a success”. In other words, we tell it what to look for and what to learn. The algorithm starts scanning the data and runs searches to find patterns. Once it has run its tasks, it spits out the results and adds these results into its “historical data”. This is how an algorithm learns. It gets a starting point and then stacks information coming from its results to broaden its knowledge. But the starting point is determined based on the data that was available before the system was invented, hence it’s data that humans created.

What does AI use to determine success?

It all starts with the historical data, so basically everything that we have gathered up until this point. That could be employee profiles, customer email, number of houses sold per year in a certain area….everything we gather data on. Then we use that entire set of data — the historical data — and teach the algorithm what type of results we are looking for. That’s what we call “a success”. In other words: if you can find us an outcome and X and Y we would be happy.

Can AI un-bias us?

So let’s say: a former recruiter had a personal preference for hiring men for technical jobs. That could be a personal preference or maybe even a sexual bias. No worries! We could assume that AI would just look for the best candidates based upon their resumé. But that’s not entirely true. Even if we strip the resumé’s from characteristics like a name, sex, and photos, the AI would still pick out male resumes. Not because we told it to. We only put in raw data about their experience and previous jobs. The reason AI can still separate men from women without know who’s who is because Artificial Intelligence looks for patterns.

What do you think?

Will we reach a point of super clean data in which we can let AI run autonomously or will be always be dealing with biassed data?