As companies increasingly apply artificial intelligence, they must address concerns about trust. Here are 10 practical interventions for companies to employ to ensure AI fairness. They include creating an AI fairness charter and implementing training and testing. Data-driven technologies and artificial intelligence (AI) are powering our world today -- from predicting where the next COVID-19 variant will arise, to helping us travel on the most efficient route. In many domains, the general public has a high amount of trust that the algorithms that are powering these experiences are being developed in a fair manner.

MassMutual, a $30 billion per year life insurance company, had a problem. It was 2013 and, along with the rest of the insurance industry, it was bedeviled by fraud. According to FBI estimates, fraud sets the U.S. insurance industry (and policyholders) back by $40 billion a year. "We had to get much better at detecting fraud in real time," says Sears Merritt, MassMutual's chief of technology strategy and data science. So MassMutual launched an innovative collaboration between the company's data scientists and its line managers.

The reality is that no matter how advanced it appears; artificial intelligence will always need to be managed to some degree. Artificial intelligence is no longer only being used by the minority. As automated technology becomes increasingly sophisticated, more and more businesses are assessing their AI readiness, writes Wayne Butterfield, Director at Information Services Group. As we enter an age where intelligent machines can more efficiently do the jobs of many humans, businesses face a key challenge – how to prepare their workplace, and just as importantly their workforce, for this seismic shift in business operations and working culture. Research from Microsoft has recently revealed that although 1 in 4 business leaders believe they will have to dramatically change the way they work in the next 5 years because of AI, less than half have an AI strategy in place to address the challenges new technologies present to the workplace.

In today's business landscape, the value of a diverse workforce has become indisputable. Hard numbers support its positive impact on businesses' top and bottom lines. A recent survey revealed that 62% of participating executives rated diversity and inclusion as a high or top priority. Diverse organizations are often more successful than others. For example, McKinsey & Company found that gender-diverse organization are 15% more likely to outperform their peers, while ethnically-diverse organizations are 34% more likely to do so. With many of the world's leading economies facing an increasingly aging population, it is encouraging to see that retaining older workers can increase business productivity.

These words of wisdom found their way to that part of my brain responsible with long-term memory after watching RIO 2. There was something about that toucan that made this phrase stick. Couple this philosophy with the idea that interviewing and recruiting talent is just like dating and the fact that full time employees spend more time at work than with their spouses at home, and I think you'll agree that working relationships between employees and employers are a lot like marriage. It's not uncommon for companies to take employees for granted and think that just because they are on payroll they will always give 110% or that they will be loyal forever and ever. When all companies want is to take, take, take and think that employees should only give, give, give, their relationship will eventually suffer and their employee turnover rate will get big, big, big. But when both parties treat each other right, and play with all cards on the table, things change.

Over the years, much has been written about the myth of "self-service" business intelligence (BI). Every eighteen months or so, I seem to revisit it. Unfortunately, things have not changed for the better. BI tools are still the arena of the analyst. Line managers are able to look at visualizations in a dashboard, they even have a limited ability to investigate the data underneath it, but a "what if?" thought almost always requires a loop back to an analyst or a developer.