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futureI&M continued
trends in

entirely on its own but is used as an aid to human decisionmaking. The AI crunches data and makes recommendations.
These smart machines are helping humans expand our abilities in three ways. They can amplify our cognitive strengths;
embody human skills to extend our physical capabilities; and
interact with customers and employees to free us for higherlevel tasks.
Human-machine co-creation addresses the pressing issue
of transparency. Each step that incorporates human interaction
demands the system be designed to be understood by humans
to take the next action, and that there be some human agency in
determining the critical steps. It incorporates human judgment
in effective ways. At the end of the day, AI systems are built to
help humans. The value of such systems lies not solely in efficiency or correctness, but also in human preference and agency.
A humans-in-the-loop system put humans in the decision loop.
It shifts pressure away from building perfect algorithms. By incorporating human intelligence, judgment, and interaction into
the loop, the automated aspects of the system are exempted
from getting everything right all at once. Because the system is
built around human guidance, the system only needs to make
meaningful progress to the next interaction point.
In human-machine co-creation, when the AI models disagree or lack confidence which is often the case in practice, the
decision will be delegated to human experts who handle the
difficult edge cases. Choices made by experts are fed back to
the system to iterate on training the AI models. Humans and
machines can work together flawlessly, complementing each
other. Machines will learn to carry out easier tasks such as following processes or crunching data. They will also realize when
humans are having difficulty and will be ready to step in to assist or to request help from a human if the job is beyond their
capabilities. Best performance, in fact, will be achieved through
co-creation between humans and machines. According to Harvard research, AI can read diagnostic scans with a 92% accuracy.
Humans can do it with a 96% accuracy. Together, 99%!
The human-machine co-creation system can also be used
to educate junior level employees. The system can create
suggestions for junior level doctors working on X-ray imagery, pointing out suggested anomalies that might have been
missed without prior experience. Even still, the junior doctor
retains the power to either accept or reject the suggestion, using their own expert intuition to make the final call.
Some time ago, Bill Gates said that the robot that takes your
job should pay taxes. There is another angle to this. When human workers perform their daily jobs, they leave important
traces. These traces are the training examples that AI can use
to learn. Hence, AI is exploiting these workers' knowledge by
learning from their interactions. The exploited knowledge is
going to produce never-ending revenues for companies for
years. This is perceived as a major problem since only that
very small fraction of the population who own shares of these
72	

companies can benefit from this never-ending revenue source
and the real owners of the knowledge are not participating
in this redistribution of wealth. Human-machine co-creation
seeks to give back part of the revenues to the real owners-the
knowledge producers. The key idea is that any profit-making
interaction a machine does has to constantly repay whoever
has produced the original knowledge used to do that interaction. Assigning rewards per decision can be an incentive to
produce better services today and to have better services in
the future. In fact, humans have an incentive to work better
knowing that their future revenues depend on how they treat
difficult and odd cases today.
Essentially, the human-machine co-creation approach
reframes an automation problem as a human-computer interaction design problem. Researchers broadened the question of
"How do we build a smarter system?" to "How do we incorporate useful, meaningful human interaction into the system?" In
human-machine co-creation, each of the training-explainingsustaining actions comprises a continuous feedback loop. The
machines-assisting-humans system takes each of these tasks
and feeds them back into the algorithm so it gets smarter, more
confident, and more accurate. This can be especially effective
when the model selects what it needs to learn next.
With the advance of AI, the advent of the unemployable
and the risk of increased income inequality can result in social
and political tensions. Delaying or blocking the adoption of AI
will not help to address these challenges. Focus should be on
facilitating workforce transitions. Additionally, a new focus is
required on creativity, critical thinking, as well as social and
emotional skills. There is also the need for rethinking social
policies. Many workers will need assistance and safety nets
to adjust to the changes AI is bringing in and to find new jobs.
AI will create new roles and opportunities. Some roles
will come to an end, as it has happened in the history of humanity every time there has been a technological revolution.
However, the changes toward human and machine co-creation require the creation of new roles and it is not just a matter
of implementing AI technology. There is no evolution without change. In the book Human + Machine: Reimagining Work
in the Age of AI, Daugherty and Wilson identified a list of fusion skills:
Re-humanizing time: Humans will have more time to dedicate
toward more human activities, such as increasing interpersonal interactions and creativity.
Responsible normalizing: Humans rethink and responsibly shape the purpose and perception of human-machine
interaction of how it relates to individuals, processes and society as a whole. Humans learn to be skillfully responsible for
themselves and AI for any actions co-created from this collaboration. New paradigm of social responsibility will emerge.

IEEE Instrumentation & Measurement Magazine	

April 2020



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