The 10 Most Advanced QA & Testing Companies of 2019 QA & Testing-compressed | Page 24

Experts’ Views
A
rtificial Intelligence (AI) systems are
proliferating across industries, moving away
from the buzz to industrialised
implementations. Financial services, telecom, retail,
logistics, media are examples of industries where AI has
begun to be embedded into mainstream applications.
Examples like fraud detection and fraud management,
revenue assurance, rendering the right content through
search, already have AI built into it, without us realising
it.
Here are 5 risks one needs to worry about:
Technology Risks
The risks brought to fore by the diversity of platforms
(Google AI, Azure AI, IBM Watson or Amazon AI),
model marketplaces (Acumos, Kaggle amongst others),
and lack of wide scale expertise and knowledge of AI
technologies.
Data Risks
However, as it gets more mature, and easier to
implement, and as industries get adventurous with trying
out newer use cases for AI implementations, there comes
the accompanying risks of releasing untested AI systems.
International businesses and regulators, whilst realising
the benefits, are also mindful of the potential risks &
unintended consequences.
24
AI deals with data, and the ability to ’learn’ from
existing data. In most cases risks around data hinge
around, the integrity of the data, sufficiency of data to
train and possible biases in the available datasets. An
example could be deploying AI systems with poorly
trained samples (skewed data sets) or insufficiently
trained with possible real-world examples.