What are the Man Machine Teaming Jobs that AI will popularize ?

With the speed of technological progress, it seems difficult to visualize how much the workplace as we know it will change. Yet, as the application of AI becomes more and more concrete, we are getting a clearer sense of the jobs of the future.

As factories and offices will be filled with smart devices prone to mistakes and bias, they will need skills such as empathy, imagination, and ethics in tomorrow’s economy. According to Paul Daugherty and H. James R. Wilson in Human + Machine, human/machine collaborative jobs will especially be the primary source of value and productivity for companies, by leveraging complementary capabilities of empathic workers and productive robots.

Here are the most interesting new job titles that AI-based technologies will push to design and invent.

The Engineering Empathy of Robot Trainers

Endurance’s chatbot 

High-tech factories are proudly promoting autonomous robots that perceive and handle objects through reinforcement learning just like a human. Yet, ironically, while they seem to be replacing humans in factories, these machines need more than ever human assistance.

For example, they still have difficulties to grasp complex objects and to interact properly with their teammates. And they won’t learn that only by carefully curated sets of data and self-experience. They will need a human that will show them how to do it, and ensure they won’t learn the wrong movement or behavior. That’s where what we can call “robot trainers’ will help companies monitor their AI infrastructure, as permanent positions dedicated to their learning.

This applies to chatbots like Domino’s or HubSpot, which are increasingly used by companies. Being in constant interaction with users, they have to assimilate the specific codes and values of human communication. Chatbot must show the most empathy and understanding when customers complain about the product service or are frustrated by the delay of a delivery. These interactions especially require constant understanding and benevolence. 

The robot must be able to frame answers that express the right emotions and make the user feel heard in any situation. Things get even more complex when dealing with special cases (people in psychological distress) or being able to translate this service to different societies and cultures around the world. This is the hard case of Chatbot like Endurance designed for Alzheimer patients or UNICEF’s chatbot dealing with kid’s difficult questions.

So if companies don’t invest on the personality and the empathy of their AI bots, they risk causing dramatic situations or PR crises for which they will be held responsible. 

That’s why hiring trainer robots full of empathy will become essential in a few years for functioning customer service. They learn them the basic communication feature, and revise their algorithms to face extreme human cases, until they can speak by themselves. This investment in robot trainers will grant companies the human touch needed by their brand to reach an even larger audience at scale.

Interaction Designers Reimagining bots

Julie Shash’s human like robot

For machines to work in harmony with their supervisor, they need also to communicate and interact well with their users. And the best way to ensure that is to build them in worker’s image.

The role of the interaction designer is to be inspired by the worker’s look and habits to design resembling robots. By showing alike behavior, tney will send him signals that will be more understandable, and they will be able to work together. For example, Julie Shash, a professor of robotics at MIT, watch all the day the movements and attitudes of exemplary workers in her factory. Based on their social and body cues, she optimizes the look and operation style of here robots. 

Similarly, the designers at Init.ai rely on conversations between customers and employees to create chatbots with the most spontaneous conversation. The interaction designer is all the more important for companies as this interaction is complex, and deal with difficult subjects.

In fact, these experiments can also raise psychological and ethical issues. As designers are creating human-like robots, they can especially trigger mistrust in workers or discourage them from working with them (the Uncanny Valley, which defines this feeling of discomfort and uneasiness when faced with an all too human robot).

Even more seriously, robot interactions can sometimes turn into drama, as when a robot in a Volkswagen factory has outright picked up and crushed a worker. The designer and his system capabilities are then even more essential to avoid tragic misunderstandings. He must be able to consider all facets of the work and its process, but also the communication between user and machine, which can be different depending on the profile, culture and expectations of the workers. 

For all this, the interaction designer will decisively define the future of factories.

How data hygienists will reinvent algorithms

All these questions also arise at the level of data usage. Robots learn only as well as the data they are given. With the multiplication of data sources of all kinds (usage data, content, location, etc.), companies find themselves with huge data pools, to the point of not knowing what to do with it.

To optimize the results of their algorithms, companies need data scientists to sort through this data. With their statistical expertise, they can identify truly useful data and eliminate the biases and noise inherent in gathered data. More than that, they can also illuminate the behaviors and results of machine learning-based models.

While these smart applications learn by themselves to compute their data, it has never been more difficult to explain what led them to their judgments and recommendations. For example, the company ZestFinance uses an intelligent algorithm to define the status of borrowers and decide whether to give them loans or not. This is not without concern for its clients, who have no visibility into the decisions that have been made. 

Algorithm experts are therefore more than ever essential to indicate which factors were decisive in algorithmic allocation processes. This will be as much to the benefit of the company (which can thus modify the model to adapt it to its values and objectives) as to the employees, who will have more trust in the corporate system (in addition to the fact that the right to explanation has become a responsibility under the RGPD).

Ethics officer and transparency

Related to this issue of data transparency, ethics will become of primary concern for big companies. 

Numerous customer and worker database gather biases and values drawn from random models. As a result, employees and clients grow increasing suspicion towards AI systems implemented in companies (less than one company out of three trusts the fairness of algorithms, and 1 person out of 3 is afraid of the impact of AI on their private life and society). To ease these worries, companies need to know how to conduct a compliance strategy. They need to tune their AI and data processes to their norms and values. 

This is where the new position of ethics officer comes in, helping them provide transparency and concrete visibility into all algorithm and data-driven processes.

These officers will monitor and ensure that the algorithm models used in the company do not base their decisions on factors that discriminate or favor certain groups over others. Researchers may facilitate this work by having invented a robot, Quixote, that can learn ethical values by listening to stories and learning their pattern. 

Ethics officers will be the teachers of these responsible and enlightened algorithms that will carry the values of companies loud and clear!

Here are 4 job titles that AI-infrastructure will need and encourage to be created. They show the potential of collaboration between humans and AI to make the workplace more empathetic, secure, efficient and fair. It’s time to learn skills that mix human qualities with technical and robotic expertise!

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