A Recipe for Success or Disaster

Emotional intelligence, deep domain expertise, and a set of “soft skills” cannot yet be automated by current AI technologies.

Whilst AI is not yet equipped with emotional intelligence, there have been a lot of reports and studies conducted around affect recognition. According to the AI Now Institute, affect recognition is a subclass of facial recognition. It is a phenomenon said to be capable of detecting our personality, emotional state, mental health and our “worker engagement” based on images or videos of faces that it sees. The problem here, however, is that this is just a claim. There is currently no scientific evidence to back up statements that AI is capable of doing this.

Let’s take recruitment as an example. Often, we hear of companies utilising AI technologies to help with the recruitment process. AI biases including race and gender aside, there are major concerns around this. Without substantial evidence to confirm that the technology and affect recognition can detect the right skills such as emotion intelligence, it’s not a technology we can confidently put into play.

AI+EI: What’s next?

Whilst emotional intelligence is seen as a critical factor to success in the workplace; it is not an easy skill to teach. As human beings, each day, we are presented with a variety of decisions to make – many of which rely on our emotional competence to relate to individual scenarios. The next step in the development of this technology sits with those who build and design it. This will be an interdisciplinary endeavor, and technologists will need to be able to collaborate with colleagues from other disciplines (social sciences, humanities, etc). Engineers building AI also need to be representative of everyone. Cross-functional teams are required to ensure its design is representative of all. This means working with male and female engineers of different ages, races, and backgrounds. To be truly emotive, AI needs to understand different types of people.

The next step in the development of this technology sits with those who build and design it. Engineers building AI need to be representative of everyone. This means working with male and female engineers, engineers of different ages, races, and backgrounds. Cross-functional teams will also be required to ensure its design is representative of all. Security, privacy, ethics, and compliance issues will increasingly require that companies set up cross-functional teams when they build AI and machine learning systems and products.

AI and machine learning are appearing in many of the products and systems we interact with. You could call it a success already with its ability to remove us from more mundane and repetitive tasks. However, at present, AI lacks the ability to understand human emotions. Current technologies require large amounts of data and compute, transparency and explainability can sometimes be insufficient, and causal reasoning remains a missing component. Given the massive investments in R&D, expect continued progress in AI technologies in the year ahead. But, it’s not something that’s going to happen overnight. Brooker may have predicted the future – but it is further away than we realise.