by James Duez, Rainbird
Gartner have identified AI augmentation as one of the top five emerging technology trends for 2020 and PwC are predicting a potential $15tn boost to global GDP from AI by 2030.
AI adoption continues to be scrutinized and more work is required to deliver both the transparency and the transformational value demanded by today’s enterprise. To achieve that, humans must continue to work in close tandem with machines.
skyrocketing use of algorithms
might lead us to assume that we are less dependent on humans, the
opposite is true. Effective results require a nuanced collaboration
between data scientists and subject-matter experts. The value is
still in automation that effectively leverages human expertise.
The limitations of ‘data up’
Data offers us a powerful way to scrutinize the past in order to forecast the future, but there are serious limitations. The majority of organizations are not data-ready, lacking a volume of good clean data, or the skills to work with the data optimally. Effective statistical outcomes require not just data scientists but also domain experts.
is the biggest barrier to the growth of machine-learning, especially
in regulated markets. Their murky inner workings can expose firms to
cost and risks. It can be a legal and ethical minefield. Because
machine learning is statistical, their models can only be understood
by a handful of data scientists - at best. Machine learnt
models tasked with making predictions in areas such as fraud
prevention and drug development operate on the basis of complex
probabilities and correlations beyond the comprehension of most
laypeople. If they are not careful, innovators run the risk that
these algorithms may operate in illegal or immoral ways. This may
well be without their explicit knowledge. It can be hard to defend
criticism or litigation when you cannot explain the methodology used.
Such models can create a legal ‘grey area’ and significant
For example, much is made of the fact that machine-learning systems often develop irrational prejudices, from gender-recognition cameras that only work on white men to algorithms that display ads for lower-paying jobs to women. This is because AI training data often reflects the biases of its human compilers. They are molded entirely by their learning environment, and make use of hidden variables to make their decisions.
start with human
more pragmatic and arguably ethical alternative is to build
enterprise intelligent automation around the expert knowledge that
businesses already possess - from their best people. While
statistical models can be powerful, something is lost if the corpus
of human expertise on a domain is not considered as an effective
from data-driven statistical machine learning models, it is now
possible to inject our automation models with the crucial ingredient
that has so far been missing: the best of human expertise.
Most organizations care about three things; efficiency (containing costs), effectiveness (improving customer outcomes) and innovation (generating new products and services to increase revenue), and AI has promised to transform all three.
nuanced, high-volume roles like credit risk analysis, tax and audit,
underwriting and claims - skilled human expertise is often in short
supply. What’s more, it is well established by the research of
Daniel Kahneman and others that bias
significantly disrupt human-decision making. More often than not, a
number of factors including lack of expertise, time pressures and
poor data hampers the quality of this kind of decision-making at
reasoning-intensive decision-making tasks are too complex to be
automated with Robotic Process Automation (RPA) as there are too many
permutations and variables, while the regulatory oversight in these
sectors makes machine learning poorly suited due to its lack of
So how then to find the best balance of human and machine? The key is to find ways of synthesizing a company’s human expertise and scale it, making it consistently available across the enterprise. This can only be achieved with a knowledge mapping approach that goes well beyond what has been possible with traditional decision-trees. The next generation of intelligent automation technology can enable the expert themselves to create holistic models of knowledge. Using probabilistic methods, these can be turned into human-like machine intelligence models that can get the best out of data - looking at each case through the same lens as the most experienced domain expert would, only faster, with more accuracy and with an audit trail.
This next generation of intelligent automation is becoming essential. There is huge value to increasing the speed, quality and transparency of decision-making processes enabling organizations to become more compliant, more trusted and ultimately more profitable.
same can’t be said for machine-learnt models, which produce
potentially biased unethical or incorrect results that can’t easily
be explained to regulators or consumers. Sustainable, transparent
automation should be the goal, and we won’t get there by continuing
to opt for short-termist ‘black box’ models that simply mine data
for quick and opaque results.
the human element, statistical techniques can be limited to providing
insights but not the exponential value via truly complex
decision-making that are required by today’s regulated companies.
machine intelligence from human expertise
Gartner estimates that AI augmentation will create $2.9 trillion in business value in 2021 alone. To achieve this value, enterprise must wake up and recognize what is now possible. They must look beyond the pure machine-learnt approach and also digitize the best of their human expertise.
Over the past few years, the early adopters have already deployed numerous solutions delivering high quality and transparent automated decisions in credit, tax, fraud, law, insurance and healthcare. The result is exponential - improving quality, reducing costs and heralding a generation of new digital products and services.
James Duez is CEO at Rainbird, a British company that develops an AI-powered, automated decision-making platform