June 12, 2018
Lack of emotional intelligence greater impediment to staff engagement than AI

A new Gallup report reveals the growth of AI is not seen as a disadvantage for employees. The real problem is lack of emotional intelligence in management, with managers failing to move beyond the role of “task manager” and adopt the coaching perspective they need in order to future proof the workforce. The Real Future of Work study interviewed 4,000 working adults in the UK, France, Germany and Spain to understand how employees are being managed and the subsequent impact this might have on the future. Worryingly, one in four UK employees say they only receive performance feedback from their manager once a year or less, a further 20 percent claim it’s only a “few times a year”. Almost one in five (19 percent) UK workers predict technology will increase the risk of losing their job – the highest in the European countries surveyed and more than double those concerned in Spain. When asked how technological changes will influence work in the next three years, seven out of ten workers in the UK felt it will increase their productivity followed by France (66 percent), Spain (51 percent) and Germany (37 percent).
















Following the deadline for organisations to publish their gender pay this week, it came as little surprise to find that almost 






Artificial intelligence systems need to be accountable for human bias at AI becomes more prevalent in recruitment and selection, attendees at the Employers Network for Equality & Inclusion’s annual conference have been warned. Hosted by NatWest, the conference, Diversity & Inclusion: The Changing Landscape heard from experts in ethics, psychology and computing. They explained that AIs learnt from existing data, and highlighted how information such as performance review scores and employee grading was being fed in to machines after being subjected to human unconscious bias. Dr David Snelling, the programme director for artificial intelligence at technology giant Fujitsu, illustrated how artificial intelligence is taught through human feedback. Describing how huge data sets were fed into the program, David explained that humans corrected the AI when it used that data to come to an incorrect conclusion, using this feedback to teach the AI to work correctly. However, as this feedback is subject to human error and bias, this can become embedded in the machine.


