AI, in the sense of machine learning approaches to automation of particular tasks, is increasingly a necessity. The pandemic is a good example of a crisis which demands systems, like track and trace, which are able to handle vast amounts of data efficiently.

When dealing with customers, Ai systems often fail because they are not able to feel and express empathy in the way that humans are. As Peter van der Putten commented, 

“People view companies as if it is a single organism or as a ‘person’. This requires not just intelligence but also empathy. Sense my emotions in the moment, learn from interactions to understand my needs, but more fundamentally, put yourself as the company in the shoes of the consumers: say if we want to put some message or nudge in front of a customer, promote what’s right for the customer not just what’s right for the company.”

Customers are, for the most part, sceptical about organisations’ willingness to do the right thing for customers, beyond what they’re legally required to, and that creates opportunities for companies who can show customers that they do.

Why do customers think of organisations as if they are people? Because they spend millions of pounds on advertising to position themselves in that way. One of the biggest causes of customer dissatisfaction is the disconnect between the friendly, personal, brand they’re promised in the adverts and the impersonal treatment they often receive in practice. Dr Harris commented,

“The most important thing is that when companies use AI, they must balance their self-interests with those of the consumer. When deciding how they want to use AI they need to consider whether it will impact their brand and their reputation.”

“People typically aren't very trustful of their information with companies and AI presumably can help smooth some of that transition if used appropriately. I don't think that perception of AI in general is that it's evil. I think that comes out when it is used in ways that sort of threaten things that people have, like their privacy, for instance. So it's really about the goal of the company.”

Algorithmic bias is not inevitable, but something which comes about because of the way we build and train AI models. Those biases reflect tendencies in the data, in other words they may recapitulate systematic biases in society, but they may also be exacerbated by who works in tech and the way they think.

So what does cause these biases, and is it something that we can easily take steps to prevent? Peter highlights three fundamental, related, problems:

• “In the same way that humans see bias in society which then reinforces our own biases, it is the same for AI.

• Through the data that we use to train models, through bias in decision logic driving automated decisions, through more mundane problems such as data issues.

• That’s not an excuse to blame it on the data – the more systemic issue is not having an eye open for the bias that could occur or not having the tools to detect and fix it.”

People within tech often feel that all this is a bit unfair, after all machines are, by definition, free from bias themselves. If a computer learns to replicate decisions which are biased, based on a stack of data about how humans have made decisions in the past, then that’s hardly the computer’s fault, is it? And yet we seem to be suggesting that computer decisions should be more heavily scrutinised than their human colleagues. Is that right?

To some extent that’s our pro-human bias speaking, but there is also the question of scale. A biased human makes far fewer decisions than a biased machine. Nonetheless, it’s important to remember that, however flawed a computer’s decisions, the fault remains with humans, as Lasana comments:

“Humans are sometimes eager to push responsibility to an AI algorithm, which is not correct. AI algorithms are built and trained by humans, based on a range of choices made by humans.”