Artificial intelligence (AI) was once the stuff of science fiction. Today, however, it is woven into our everyday experiences in the form of chatbots, voice assistants and even Google Maps. In fact, according to Statista, 84% of global business organisations now believe that AI will give them a competitive advantage.

AI may be fairly commonplace (at least at a simplistic level), but developing it to maturity is proving more elusive. Training a machine to learn, respond and act like a human takes massive amounts of data inputs across countless scenarios.

Managing this process alone is tough for organisations as they face many potential issues. The most common, and potentially the most dangerous, is the issue of biased data. If an organisation plans to excel with AI, combating this bias should be its number one priority. Otherwise, the company risks the algorithm delivering inaccurate results and potentially alienating large portions of customers.

The first step to tackling this problem is to understand how algorithms become biased in the first place. Every developer (and every person, for that matter) has conscious and unconscious biases that feed into their initial development, and because an algorithm is only as smart as the data used to train it, this can set a dangerous precedent. Bad data has the potential to cause biased AI to make decisions that actively harm people and populations. But while humans are the root of all bias, they are also the key to removing it.

Today’s consumers want AI to be more natural and more human, but to achieve this the data that goes into the algorithms must be more representative of the real world.

Collecting diversified training data at scale from real people is the way to do this. Using a vetted global community that covers numerous countries, ages, genders, races, cultures, political affiliations, ideologies, socioeconomic and education levels, and more, allows organisations to validate that their algorithms are producing accurate, human-like and truly useful results. This applies to sourcing the baseline sets of training data, and to the ongoing collection of data, so it is advisable to introduce a structure which allows for continual feedback and modification.

It may be that some users report difficulties with certain aspects of the product, for example voice or facial recognition, and this feedback could then be incorporated into the next version of the algorithm to improve it for future users.

The reality is that despite limitless technical implementations, AI can only ever be as good as the humans who programme it. This raises considerable issues when we factor in all of the intentional and unintentional biases that each person carries. To some extent bias will always exist within artificial intelligence, but by collecting real human interactions before release, businesses can train their algorithm and achieve results that provide real value to their customers.

We are reaching a point where AI has begun to influence the decisions which govern the individual and collective future of our society, so it is vital that the companies developing these algorithms take an active role in making AI more reflective of society, and fairer for all.