Artificial Intelligence For Ethiopia Opportunities And Challenges

The Information Technologist: An International Journal of Information and Communication Technology (ICT) Vol. 16 No. 1, June 2019
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ARTIFICIAL INTELLIGENCE FOR ETHIOPIA:
OPPORTUNITIES AND CHALLENGES
Frehiwot Gebrekrstos Girmay*
*Frehiwot Gebrekrstos Girmay is currently a Lecturer at Aksum University College of Engineering and Technology, Department of Computing
Technology. She holds a Bachelor of Computer Science degree from Mekelle University and a Masters of Information Technology (MScIT)
degree from Aksum University Ethiopia. Her areas of research interest include: Artificial Intelligence, Machine Learning, Human Computer
Interaction, Mobile Computing and Data mining. E-mail: marsii143@gmail.com
Abstract
Artificial Intelligence (AI) has wide range of applications in all areas and is gaining the
understanding of the society as necessity instead of luxury. AI start ups are working to improve the
quality of social interactions (social good), Education, Agriculture, manufacturing, health and
medicine and public services. Hence the cost of not developing AI or developing it late is enormous.
Despite the opportunities AI technologies may offer, there is a real risk that without thoughtful
intervention it may in fact exacerbate structural, economic, social, and political imbalances, and
further reinforce entrenched inequalities. For regulators and policymakers around the world, uneven
access to technology remains a major concern because of its potential impacts on social and
economic inequality. The author has conducted an exploratory research by reviewing related
literatures on AI opportunities and challenges from experiences of the developed world and
provided a discussion to identify the potential opportunities and expected challenges for AI adoption
and implementation in Ethiopia. Finally the author has recommended what should be done.
Key Words: Artificial, Intelligence,, Exploratory, Research, Skills, Infrastructure, Data, Privacy
Introduction

Artificial Intelligence For Ethiopia: Opportunities and Challenges
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AI is an area of computer science devoted to
developing systems that can be taught or
learn to make decisions and predictions
within specific contexts. AI applications can
perform a wide range of intelligent
behaviors: optimization (e.g., supply chains);
pattern recognition and detection (e.g., facial
recognition); prediction and hypothesis
testing (e.g., predicting disease outbreaks);
natural language processing; and machine
translation. AI technologies are poised to
have a significant impact on society because
they leverage existing infrastructure (the
internet, large datasets) to dramatically
reduce the costs of activities (both new and
old, good and bad) on a large scale.
(Matthew Smith, 2018).
Although AI is not new, there has been a
recent explosion of activity and interest in
the field, which has largely been driven by
advances in ‗machine learning‘, and the
related field of ‗deep learning‘. These are
computer programs that learn and improve
with experience. Progress in machine
learning has allowed more versatile AI
systems to be developed that can perform
well at a range of tasks, particularly those
that involve sorting data, finding patterns,
and making predictions (see Figure 1). The
training process can involve in practice
adjusting for millions of parameters relative
to computational efforts of the machine
being used with ―astronomically more
possible outcomes than any algorithm could
ever hope to try
Fig. 1: Artificial intelligence, machine learning and deep learning (World Wide Web Foundation, 2017)
AI in Ethiopia
There are some initiatives to make Ethiopia a
giant center of excellence for Artificial
Intelligence in Africa. According to some,
the AI sector is burgeoning in Ethiopia, with
various reports indicating that the country is
becoming a thriving center for AI research
and development and robotics. This might be
culminated with the involvement of
Ethiopians (more than 50 percent) in the
software programming of Sophia - a human-
like robot powered by artificial intelligence
(AI) and capable of over 60 different facial
mechanisms to create natural-looking
expressions (Robel yohannes,2018) and the
establishment of AI and Robotics centers of
excellence which is identified by the
ministry of science and technology to be
established in Addis Ababa Science and
Technology University with the aim of
establishing state of the art center of
excellence that could serve the university
community, researchers at the national and
global levels in the fields of AI and robotics.
It could also be the hub for researchers to
perform real problem solving research
projects in the field of AI and robotics.
Related Work
Plenty of litratures have been written on the
pros and cons of AI in the developed
countries and few in the developing ones.

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Daniel and Joshua (2016) wrote a paper on
promises of AI in different domains like
education, agriculture, healthcare, business
and industry, governance etc .The limitation
of this paper is it didn`t try to deal with the
negative sides(challenges) of AI. Another
research by Gerlind & Blandine etl.(2017)
presented the influence of AI and robotics on
the work force focusing only on the risks this
technology will bring on employment. The
last and closely related paper explores the
opportunities and challenges for AI in India
(Shivaram & Rahul, 2017). The paper added
up another opportunity of AI in Scaling up
NLP for Indian society as more than 700
languages are spoken in India. It also gives
insights that developing countries can also
build AI based economy but this doesn`t
mean that all developing countries have
exactly same challenge in adopting and
implementing AI. All opportunities of AI
might be closely similar for every country
but the potential it requires to adopt and
implement it (challenge) highly differs
especially the skill, infrastructure, data and
level of security required to build and control
it . This paper will explore AI in Ethiopia.
Methods and Procedures
The paper has followed exploratory research
method. Exploratory research design does
not aim to provide the final and conclusive
answers to the research questions, but merely
explores the research topic with varying
levels of depth (John Dudovskiy,2016) and
tends to tackle new problems on which little
or no previous research has been done
(Brown, R.B., 2006).So far no published
researches are available regarding Artificial
intelligence in Ethiopia and this paper aims
to contribute some knowledge as a bases to
explore the opportunities and challenges of
AI in Ethiopia. The author elaborated the
ideas stated in the paper by mentioning
related litratures written from other countries
experiencing the adoption and
implementation of AI and some initiatives
began in Ethiopia.
Discussion
AI is a vast body of knowledge and can be
useful in various activities of our lives. AI
provides a greater opportunity of sustainable
development in the areas of communication,
education, agriculture, medicine and
healthcare, manufacturing industry,
governance, national defense and security.
On the other hand, AI Like any other
technology comes with its own challenges
and risks such as lack of STEM skills, data,
IT infrastructure, privacy issues and
eliminating some types of jobs especially for
developing countries like Ethiopia. Below is
a discussion of what opportunities Ethiopia
will benefit and what a challenge has to face
in the long run in the adoption and
implementation of AI as one of the
developing countries.
Opportunities

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Communication
Scaling up NLP/ASR for Ethiopian
Languages
Ethiopia‘s population is highly diverse,
containing over 80 different ethnic groups.
Most people in Ethiopia speak Afro-Asiatic
languages, mainly of
the Cushitic and Semitic branches. The
former includes the Oromo and Somali, and
the latter includes the Amharic and Tigray.
Together these four groups make up three-
quarters of the population. The country also
has Omotic ethnic minorities who
speak Afro-Asiatic languages of
the Omotic branch. They inhabit the
southern regions of the country, particularly
the Southern Nations, Nationalities and
Peoples' Region. Among these are
the Welayta and Gamo. Nilo-Saharan-
speaking Nilotic ethnic minorities also
inhabit the southwestern regions of the
country, particularly in the Gambela Region.
Among these are the Nuer and Anuak who
are also found in South Sudan which borders
the Gambela Region (Ethiopian census,
2007). Large number of this population is
either monolingual or bilingual, language
becomes naturally a barrier for
communication and accessing information.
People from all the ethnic groups are
expected to speak in Amharic which is the
national language of the country and this
creates competition to dominate one
language over another so as to use mother
language for official purposes too. This can
be one reason for ethnic conflicts. AI is
working towards everyone should be
understood goal. Natural Language
Processing (NLP) and Automatic Speech
Recognition (ASR) have a long history as
research topics within AI. Substantive
progress made on these topics has resulted in
viable systems for machine translations,
spoken dialogue, sentiment analysis, and
social media analysis (Hirschberg and
Manning,2015).To provide an illustration the
author experimented with Google
translation, widely considered to be a leading
service for machine translation. (Figure 2)
shows the translation returned for relatively
simple English word to Amharic.
Fig. 2: simple Google translation from English to Amharic
There might happen several obvious
mistakes in translation but the path to robust
digital local language library could be paved
by bringing more content in each language
into the digital domain, providing services
such as search and speech interfaces, and
same language subtitling in videos to
improve functional literacy(Shivaram &
Rahul, 2017).

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Agriculture
85% of Ethiopian population depends on
agriculture for livelihood. It is estimated that
795 million people of the world still live
without an adequate food supply (FAO
2015), and by 2050 there will be two billion
more people to feed (Dodd‘s and Bartram
2016). Ending hunger and improving food
security are primary goals in the 2030
Agenda for Sustainable Development of the
United Nations (United Nations 2015). A
central challenge to address food security
issues is yield estimation, namely being able
to predict crop yields well before harvesting.
Agricultural monitoring, in particular in
developing countries, can improve food
production and support humanitarian efforts
in light of climate change and droughts
(Dodds and Bartram 2016).Remote sensing
has recently garnered much interest than the
traditional survey based approaches because
of absence of available data about weather,
soil properties, and precise land cover in
developing countries which is highly needed
for reliable crop yield prediction
Forecasting Crop Yields from Space
Yield prediction, one of the most
significant topics in precision agriculture, is
of high importance for yield mapping, yield
estimation, matching of crop supply with
demand, and crop management to increase
productivity. Examples of ML applications
include in those in the works of (Ramos &
Prieto. etl.) an efficient, low-cost, and non-
destructive method that automatically
counted coffee fruits on a branch. The
method calculates the coffee fruits in three
categories: harvestable, not harvestable, and
fruits with disregarded maturation stage. In
addition, the method estimated the weight
and the maturation percentage of the coffee
fruits. The aim of this work was to provide
information to coffee growers to optimize
economic benefits and plan their agricultural
work. Descartes Labs has adapted deep-
learning image-analysis software to analyze
satellite photos of farmland to forecast crop
yields faster and more accurately than

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official government estimates. The software
can produce estimates of crop production on
a weekly basis by comparing daily
photographs of 3 million square kilometers
of corn farms with less than a one percent
margin of error, allowing farmers, insurers,
commodities traders, and governments to
make more informed decisions. (Elizabeth
borneman, 2017)
Spot-Treating Crops
Another most significant concern in
agriculture is pest and disease control in
open-air (arable farming) and greenhouse
conditions. The most widely used practice in
pest and disease control is to uniformly spray
pesticides over the cropping area. This
practice, although effective, has a high
financial and significant environmental cost.
Environmental impacts can be residues in
crop products, side effects on ground water
contamination, impacts on local wildlife and
eco-systems, and so on. ML is an integrated
part of precision agriculture management,
where agro-chemicals input is targeted in
terms of time and place (Konstantinos G.
and Dionysis et.l, 2018)
A company called Blue River
Technology has developed a robotic system
called LettuceBot that can drive through a
field, take 5,000 photos of plants per minute,
and use computer-vision algorithms to
identify weeds and spray targeted bursts of
herbicide directly on them, rather than the
whole field. LettuceBot‘s algorithms can
differentiate between photos of lettuce
sprouts and weeds in 0.02 seconds (leanna
garfield, 2016)
Education
Education is widely accepted to be a
fundamental resource, both for individuals
and societies. Regarding the consequences of
education, a growing body of empirical
research suggests that better education yields
higher individual income and contributes
towards the construction of social capital and
long-term economic growth. (Max and

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Esteban, 2019).Personalized learning and
Customizing /Localizing content are
mentioned below as a Contribution of AI in
Education.
Personalized learning
Perhaps the key move away from a
teacher-centered learning process is the
development of personalized learning. One
way to provide this is through intelligent
tutoring systems (ITS), also known as
cognitive tutors. An ITS is typically an
expert system that attempts to recreate one-
on-one instruction by adapting and
personalizing the learning experience to the
individual learner (Matthew Smith, 2018).
An ITS assesses each learner‘s actions
within these interactive environments and
develops a model of their knowledge, skills,
and expertise. Based on the learner model, it
can tailor instructional strategies, in terms of
both the content and style, and provides
relevant explanations, hints, examples,
demonstrations, and practice problems to
individual learners see Figure 1 below.
Fig 3: Structure of an intelligent tutor (Phobun and Vicheanpanya, 2010)
ITS is also beginning to incorporate
other AI techniques to enhance instruction.
Some ITS known as affective tutoring
systems also incorporate emotional
recognition as a means to enhance the
tutoring adaptation to the student (Petrovica
et al. 2017). ITS can also apply natural
language processing and speech recognition
to help identify language errors or interact
with students in novel ways. Let`s mention
examples where we can use AI for
personalized learning.

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Learning at scale
Providing quality learning at scale with
low cost is a challenge of the education
system. Addressing this challenge requires
increasing access to improved connectivity,
coupled with online digital learning
approaches such as massive open online
courses (MOOCs).This has improved the
capacity to provide a large number of people
with quality educational content and
experiences. While early evidence on
MOOCs shows that people With higher
levels of education and socio-economic
status tend to benefit disproportionately
(Christensen et al. 2013; Hansen and Reich
2015), some research illustrates that certain
types of MOOCs – such as those offering job
related training can benefit users from low-
and middle-income populations in the Global
South (Garrido et al.2016).AI techniques
have the potential to build-on online learning
to achieve the lofty goal of delivering high-
quality learning at scale, particularly to
marginalized populations. This overcomes
the bottlenecks that arise when engaging
with large numbers of students — chiefly the
lack of human resources to provide
individualized feedback, guidance, and
assessment of student performance. A
combination of AI techniques can help
handle high student loads. Furthermore,
developments in automated scoring using
natural language processing and other
techniques enable the mass grading of
quizzes, exams, and essays.
Customizing/Localizing Content
AI techniques can also help improve
educational content at low cost, for example
by providing automated translation of
existing works into new languages or
leveraging AI to create new content. For
example, the Pratham Books StoryWeaver
platform is working with Google.org to
leverage Google‘s AI-powered translation
tool to translate children‘s e-books into as
many as 60 different languages. Indeed, the
ability to leverage automatic translation
should greatly facilitate the localization and
use of high-quality open educational
resources (freely available, typically digital

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educational materials) in the Global South
(Smith, 2013) uses ML techniques to create
custom textbooks. Instructors feed their
syllabi and material into the AI engine and
the system creates textbooks and classroom
material based on the core concepts it
extracts. Another company, Content
Technologies, Inc., One can also envision
the automated creation of personalized study
guides, quizzes, and tests, which would be
particularly helpful in massive online
learning environments. No significant
progress has been done in incorporating AI
based education in Ethiopia so far.
Manufacturing Industry
Ethiopia is currently following
Agriculture-lead-industrialization policy
where industrial developments require an
input from the agricultural sector. It is
encouraging local and attracting foreign
investors from different countries by
building industrial parks across big cities of
the country. Even Small enterprises are
increasing from time to time. Having cheap
labor force makes Ethiopia preferable by
foreign investors while they need to be
successful by producing quality products
efficiently and this requires high level of
automation where machines can operate
without interruptions and they have to find
markets, promotion and increasing sales for
their products. Below are examples where
the industry can take advantage of the
rapidly growing technology of AI
applications.
Automating production activities
Siemens AG has built a manufacturing
facility in Amberg, Germany, that relies on
1,000
Networked manufacturing units
automatically coordinating with one another
to retrieve and fabricate components without
human oversight. The facility is
approximately 75 percent autonomous,
allowing its human employees to focus on
monitoring the factory floor and operating
computer systems. (SIEMENS Press, 2017)
Predicting machine break down before
happening
Analytics company DataRPM has developed
a tool called Cognitive Predictive
Maintenance that uses machine learning to
monitor machine-component performance

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through networked sensors in real time to
detect early warning signs that a machine
might be breaking down to prompt
preventative maintenance. Servicing
machines before they break down is more
cost effective than fixing them after they
break and DataRPM can predict when
maintenance is needed 300 percent more
accurately and 30 times faster than
traditional methods, saving its customers 30
percent in maintenance costs (Anoop
Premachandran, 2017)
Figure 4: The predictive maintenance process (Deloitte university press, 2017)
Market forecasting
Italian dairy producer Granarolo
implemented a machine-learning tool that
analyzes data about sales estimates and
planned promotions, which can increase
sales 30-fold,to forecast how much its dairy
farms should produce and when. Because
dairy is perishable, overestimating demand
for a particular period can result in large
quantities of wasted products, while
underestimating demand can cause diaries to
miss out on potential sales. With its
forecasting tool, which learns to identify the
relationship between Granarolo‘s thousands
of promotions per year and demand
fluctuations, Granarolo was able to
significantly increase its forecast reliability,
reduce inventory levels and delivery times
by 50 percent, and increase sales (Supply
Chain Technology, 2019)
Health Care and Medicine

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Continuous technological developments
in healthcare have saved countless lives and
improved the quality of life for even more.
Not only has technology changed
experiences for patients and their families,
but it‘s also had a huge impact on medical
processes and the practices of healthcare
professionals (Bianca Banova, 2018).In the
case of Ethiopia, the ministry of health
announced that Ethiopia is getting ready to
use drone technology in medical supply
aimed at assisting the effort to realize
equitable and accessible health service
across the country and Among the
companies that are working together with the
Ministry is Zipline which has successfully
tested the technology in Rwanda (Yohanes
Jemaneh, 2018). A more advanced form of
technology (AI) is contributing more to
Global health and some examples are
mentioned below.
Prevention and screening disease
An automated microscope called Auto
scope uses an artificial neural network to
rapidly analyze blood samples in the field
and diagnose malaria with 90 percent
accuracy. Traditional rapid diagnostic
methods can only determine if the malaria
parasite is present in a blood sample, which
does not necessarily mean a person will
contract the disease Auto scope can
differentiate between specific particles in a
blood sample to quantify the number of
malaria parasites to determine if a diagnosis
is warranted (Daniel and Joshua, 2016).
Treatment & Monitoring

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Glooko is a global diabetes data
management company which provides
HIPAA-compliant and widely compatible
mobile and web apps. These apps
synchronize with diabetes care devices and
activity trackers to collect data like insulin,
blood pressure, blood glucose, diet, and
weight. Glooko collects data from over 180
exercise and diabetes care devices and then
correlates it with exercise, food, medication,
and other relevant data to deliver insights
with clinical care and self-
management.These apps will contribute a lot
to self-management and also sizeable
improvement can be made in patient
outcomes (GoodWorkLabs, 2018)
Public Services
Public sector organizations are increasingly
interested in using data science and artificial
intelligence capabilities to deliver policy and
generate efficiencies in high-uncertainty
environments. Ethiopia has experienced the
use of AI to gather violent social media
information in 2018. Some applications of
AI where the public sector can take
advantage of are listed below.
E-governance
Researchers at Stanford University have
developed a prototype machine-learning
algorithm that can analyze satellite imagery
to map impoverished areas in Africa, which
can help governments and development
organizations improve aid efforts.
Researchers have previously analyzed
nighttime satellite images that use light
levels as a measure of economic activity, but
this approach is not reliable for low-light or
densely populated areas. The Stanford
researchers‘ algorithm learned to identify
4,096 markers of economic activity in
daylight images, such as roads and
waterways, and by combining this data with
light-level analysis, the algorithm was able
to predict impoverished areas more reliably
than traditional methods (Michelle horton,
2016)

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Transportations
The first self-driving electric shuttle for use
on public roads has taken to the road in
the Netherlands. The "WEpod" took six
passengers down a 200m stretch of street in
the first trial of its kind. Autonomous public
transportation does exist in other parts of the
world, such as the ParkShuttle bus in
Rotterdam, the Heathrow Pod in London and
the LUTZ Pathfinder in Milton Keynes,
which run on special single trajectory lanes,
or in pedestrianised areas. The WEpods in
Gelderland will drive on regular roads
amongst public traffic. During its test phase
it will not travel in challenging conditions,
such as in rush hour traffic, at night or in bad
weather. A control room will monitor the
vehicle and safety of its passengers. The six-
person vehicle has a maximum speed of 25
kilometers per hour. (Madhumita Murgia,
2016). Researchers at the University of
Minnesota have developed a computer-
vision system that analyzes truck parking
lots along the highway to automatically
detect when a spot is available and notify
approaching truckers. The system analyzes
videos of truck stops and can distinguish
between open and occupied spaces with 95
percent accuracy, which is more reliable than
systems that rely on sensors embedded in
pavement (Kevin Coss, 2016)
National Security
A 2017 report by the World Wide Web
Foundation suggested that Ethiopian
“intelligence services are using machine
intelligence techniques to break encryption
and find patterns in social media posts that
can be used to identify dissidents.‖ And
while mobile phone and internet penetration
in Ethiopia is comparatively poor — a
situation made worst amid widespread anti-
government protests, which prompted an
internet crackdown in February. The report
added that government surveillance and
oppression could increase as the use of smart
phones expand (Thomas Lewton and Alice
McCool, 2018)
Challenges

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Despite the unlimited opportunities AI is
providing and will provide in various areas,
there is and will be a great challenge to adopt
and implement it. The challenge may differ
from developed nations to the developing
ones. Eliminating job opportunities and
privacy issues as a result of AI disruption
might be the primary concerns of the
developed nations. According to researchers
from the University Oxford, AI and robotics
will destroy 47 percent of U.S. jobs in 20
years; one Silicon Valley pundit has claimed
that it will destroy 80 to 90 percent of U.S.
jobs in the next 10 to 15 years. And not to be
outdone, Kevin Drum writes in Mother
Jones that all jobs will be gone in 40 years.
As a result of this sort of unquestioning
hysteria, the public is bombarded with
overblown fears and a false sense of
urgency. Because of the crowded field of
opinion and analysis, the media tends to
recognize those with the most outrageous
claims, setting a pattern whereby it
continuously escalates the perceived
implications, challenges, and threats brought
by the new technology. This has been the
pattern with AI skeptics and antagonists
have engaged in hyperbolic and emotional
rhetoric that the media then repeats and
amplifies. This phase of panic has been
marked by apocalyptic and dystopian
imagery for AI, including Elon Musk‘s
warning that it could be ―summoning the
demon‖ that destroys the human race
(Robert D. Atkinson 2018).In case of the
developing nations the cost to develop or
adopt AI is more challenging while sharing
the consequences of AI disruption with the
developed nations .What makes developing
AI challenging for the developing nations is
high digital illiteracy, lack of STEM skills,
data and low infrastructures and Ethiopia
falls in this category. The cost of not
developing artificial intelligence or
developing it more slowly will be enormous:
lower growth in per-capital incomes, slower
progress in areas such as health and
environment, and reduced improvement of
quality of a wide array of public and private
goods and services (Robert D. Atkinson,
2016)
Skills
The extent to which the AI may disrupt
the labor markets is difficult to measure. It`s
clear that as AI becomes more advanced, it
will result in the increased automation of
work. This trend may or may not result in
mass job dislocation. However, some low
skilled jobs are clearly vulnerable to
automation and improvements in AI
technologies will certainly result in the loss
`of some jobs. It`s important to recognize
that AI is like any, many other technological
developments that has led to the replacement
of labor by machines. What`s new is the kind
of jobs AI will allow to be automated. The
negative impact for certain workers in
certain fields shouldn`t blind us to likely
benefits of increased productivity in terms of
economic performance and job-creation
elsewhere in the economy. Government
policies that promote economic growth and
help dislocated workers in order to maximize
the benefits of AI it is vital that populations
in low and middle income countries have the
skills to develop and deliver programs. This

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is the case for all levels of society. For poor
communities, STEM skills could be a path to
economic empowerment. For programs
intended to work under government
supervision, there is the need to be mindful
of the limited government capacity in many
low and middle income countries. On the
other side there are jobs that will be created
as a result of involvement of AI in the work
environment. Below are list of jobs that will
be created, jobs to be eliminated as a result
of automation and jobs on demand according
to (Gerlind & Blandine etl. 2017).
New Job Types
Despite eliminating some sort of jobs due to automation, there are some kinds of popular
jobs to be emerged in the AI time.
Data Scientist
Data science is believed to be the sexiest
job of 21 century. A data scientist is an
expert responsible for structuring huge
volumes of data collected by data analyses.
This includes the research of both the data
and their structure or origin, to supplement
incomplete data sets and to create links
between abstract data sets. Big data
developers, by contrast, deal with the set-up,
processing and storage of huge unstructured
data volumes in companies, whereas data
artists are primarily responsible for the
graphic presentation and editing of data
volumes.
The data scientist role has been in
existence only for a few years and will gain
importance in the future. On the basis of
statistical calculations using probability
theories, this new occupational group will
draw up forecasts. Comprehensive IT
knowledge is vital for this complex task.
This includes the relevant programming
languages and writing complex
programming codes. In addition, a data
scientist must be familiar with the business
processes of the company in order to be able
to create reasonable links. Therefore, basic
knowledge of business administration,
economics, stochastic and marketing is vital.
In addition to this comprehensive
knowledge, interpersonal qualities are also
required. A data scientist must be able to
adapt their services to the expectations of
customers or the employer and to
communicate.

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Crowd Worker
Crowd workers are freelancers who offer
their skills via their computers on online
platforms. Crowd working is a symbol of a
changing world of work for white-collar
workers in the gig economy. This covers
smaller tasks, such as writing product
reviews, searching for phone numbers, and
more comprehensive work, such as testing
software, providing legal advice,
ghostwriting or designing and programming
a website. These bigger and more
meaningful tasks are regularly summarized
under the term ‗crowd sourcing‘.
Simple physical work
New high-end jobs will be created, but
there will be an increase in the low-wage,
non-routine sector as well. There are positive
spillover effects from high-tech employment
to low-tech employment, especially in the
form of in-person services. Creating one
high-tech job will create between 2.5 to 4.4
other jobs in the local area, mostly in low-
skilled and medium-skilled in-person
services. These non-routine manual
occupations are service jobs, such as
janitors, gardeners, manicurists or home
health aides.
Jobs at High Risk of Elimination

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High routine occupation
Accountant, court clerk or desk officer at
fiscal authorities owing to the high level of
routine in their performance, these jobs will
mainly be done independently by software.
Almost every job where an employee sits in
front of a computer screen and processes and
interprets data is at high risk. According to a
study by ING-Diba conducted in 2015, the
probability of the relevant job being
eliminated is 89 per cent.
As a result of the simplification of the
tax returns, there is a 72 per cent probability
that the job of assistant tax consultant will be
eliminated.
Simple physical work/manual work
Simple work mostly carried out by mere
physical strength will be increasingly, but
never completely, performed by machines in
the future. The decisive criterion remains the
level of routine. The efficient use of a
machine rather than a human employee is
possible only if the process can be made
independent and is repeated with certain
regularity. Many supplier operations have
thus been planning already for some time to
eliminate many jobs in production and have
the work performed by robots. It is
acknowledged that even in low-labor-cost
countries, such as China, traditional factory
workers are gradually being replaced by
robots. Whether it will be possible to retrain
traditional assembly line workers to be IT
experts or account managers is a different
matter.
Dismissal of employees as a result of
digitalization
If retraining the employees is out of the
question (eg, the employees working in those
jobs will not be able to be trained for a
highly qualified job because of their own
lack of ability concerning digitalization) or if
the technical reorganization will reduce the
number of employees to such extent that not
all employees can be retrained, collective
redundancies in the fields of work listed
above, by way of example, will be the
inevitable consequence. The ability to affect
such collective redundancies may be
constrained by labor law but the rules vary
from country to country.

174
Jobs in Demand
IT management and science
IT and science professions, in particular,
as well as media science and humanistic
professions, will initially benefit from the
increase in investments and the associated
growth in the area of Industry 4.0. The
probability of jobs being eliminated in the
area of the sciences (physics, biology,
information technology and chemistry) is
10%. In addition to executive employees
(11%), the prospects are similarly good for
physicians (only 1%), which is due to the
ageing European population. In the next ten
years, the IT service sector will experience
the greatest boom. Typically, high-paying
occupations are corporate managers,
physical, mathematical, and engineering
professionals, life science and health
professionals, in other words, typical jobs
related to science, technology, engineering
and mathematics (STEM) that can be found
in parts of both manufacturing and services.
These high-paying occupations will not be
eliminated; on the contrary, the number of
employees in those occupations will rise.
Afterwards, the teaching, legal and advisory
professions will participate in the gains
caused by the numerous reorganizations and
technical upgrades.
Teaching professions
Teaching professions is not limited to the
areas of school, university and vocational
training, but in particular, possibilities of
further and advanced training for adults. This
professional group benefits from the need of
companies to spend more money on the
education of employees and the achievement
of further key qualifications for new and
existing employees. The number of young
professionals with a university degree is
already increasing year by year. The range of
additional qualifications up to internationally
acknowledged degrees is also increasing, so
even the individual employee is under
pressure to obtain additional degrees in order
to stand out against well-educated
colleagues. There is a general trend towards
more education. More education leads to
more jobs for teachers even if there are
fewer attendance seminars, more webinars
and more online workshops in the future.

175
Humanistic, social science, media science and artistic professions
Creative professions have benefited in all
respects in recent decades, and humans in
these professions will not be replaced by
machines in the future either. Whether they
are superstars with their music, artists with
their works or authors and actors with their
literary or cinematic works, or simply
humanities and media scholars, increasing
demand is forecast for their professions. In
the future, the end user will still demand
creative entertainment options and
graphically appealing presentations.
Special cases: Lawyers
Even if the risk of being replaced by
intelligent software or a machine is low for
members of the legal profession (3-5%), the
following examples show that technical
development is taking place also in this
sector. Algorithms not only make the work
easier for lawyers, but also allow them to
offer fixed prices. It is therefore logical that
according to a study conducted by Deloitte,
100,000 jobs in the English legal sector will
be automated in the next 20 years.
Moreover, it can be observed that more and
more companies are working with on fees.
Private clients also want to have a better
overview of the lawyers‘ fees, so more and
more law firms are forced to offer fixed
prices and to invest in digital innovations
because of the pressure from their clients and
increased competition.

176
Doctors and nursing Staff
Like lawyers, doctors and nursing staff
are far from being replaced. However, in this
sector too, technical possibilities can lead to
staff reduction. In some cases, machines are
able to work faster, more accurately, and
more efficiently than the best humans. It will
no longer be possible to imagine hospitals
without robots in the future. Their tasks will
be, for example, to move people out of their
wheelchairs or their beds or even help the
doctors to perform surgery on people.
Additionally, software technology based on
artificial intelligence, called ‗Watson
Health‘, will help doctors to diagnose
various illnesses by reconciling patient data
with medical knowledge collected in a cloud.
Finally, the demographic change, at least in
Western Europe, will lead to an increase of
jobs in this sector because the population is
constantly ageing and modern medicine is
one reason for this.
Infrastructure
Although AI technologies can have
global impacts, development has often been
limited both geographically and by sector,
with a small number of companies driving
forward these technologies with little input
from different industries, disciplines, social
classes, cultures, and countries. For that
reason, there is a risk that increased reliance
on AI may have unintended consequences
that aggravate current disparities,
particularly in countries that rely on
industries at risk of being automated.
However, with the help of strategic policies,
AI technologies might be harnessed to
overcome the persistent challenges posed by
unequal access (Gasser, Ryan & Amar,
2018) some of the optimism about the
application of AI in developing countries
like Ethiopia rests on the ubiquity of mobile
phones. Yet, across low and middle income
countries, internet and mobile penetration
varies significantly between urban and rural
areas, age groups and genders (World Wide
Web Foundation, 2017)

177
Data
The use of AI to develop solutions to
address inequalities is promising but also
raises novel challenges for policy makers
and regulators. For example, many
companies are working to use AI and
machine learning to provide alternative
credit scoring to the unbanked in developing
countries, using data gathered from cell
phone usage to assess credit-worthiness.
While this has the potential to facilitate
access to credit for many individuals, the
quality of the input data depends on existing
infrastructure, digital literacy, and cultural
practices underlying cell phone usage.
Innovative applications of AI technologies,
such as alternative credit scoring, will inherit
both the old challenges of the digital divide,
such as disparities in digital infrastructure
and technological literacy, and face AI-
specific challenges, such as a lack of
representative data sets(Gasser, Ryan &
Amar, 2018). Besides Data and AI are
interconnected, and there is no AI without
data. AI will be used to either assist or
replace humans at making certain types of
decisions, and if AI start to make ethical and
political decisions for us, then this means
that the study of ethics and ethics training
are now more important than ever. In light of
these concerns, the government is putting
ethics at the core of how the United
Kingdom develops and harnesses the power
of AI and automation (AI in UK, 2018)
Eliminating Job Opportunities
Ethiopia will not be different to the
global AI web which is beginning to uproot
workers from their jobs as a result of
automation. Technological development, and
in particular digitalization, has major
implications for labor markets. Assessing its
impact will be crucial for developing
policies that promote efficient labor markets
for the benefit of workers, employers and
societies as a whole. Rapid technological
progress and innovation can threaten
employment. Such a concern is not new but
dates back at least to the 1930s, when John
Maynard Keynes postulated his
‗technological unemployment theory‘
technological change causes loss of jobs
(Keynes, 1937). Technological innovations
can affect employment in two main ways: by
directly displacing workers from tasks they
were previously performing (displacement
effect) by increasing the demand for labor in
industries or jobs that arise or develop due to
technological progress (productivity effect).

178
The (Economist, 2016) reports further case
studies that show similar patterns.
In general, past industrial revolutions
suggest that in the short run the displacement
effect may dominate. But in the longer run,
when markets and society are fully adapted
to major automation shocks, the productivity
effect can dominate and have a positive
impact on employment. Moreover, the main
engine of technological progress in the AI
era is the continuous development of deep
machine-learning techniques that use the
function and complexity of the human brain
as a model for design (Petropoulos,
2017).Machines are trained to be intelligent,
which can have additional implications for
the workforce.
Privacy Issues
While not yet the focus of as much
techno-panic as the effects of AI on human
existence and jobs, some have argued that AI
will destroy privacy, through its ability to
more automatically collect and analyze
information (ROBERT D. ATKINSON,
2016). More recently, Globe and Mail
reporter Carly Weeks writes that ―new and
ever-expanding ways computer technology
can search, store and archive information
about all of us means that, in many ways, the
notion of privacy is becoming obsolete.‖
Matthew Aylett, a computer scientist at
Edinburgh University argues that ―Given
enough data sources you can find out things
that people didn‘t realize. Take the classic
idea that if you know people‘s positions on
their phone, you can tell where they move
about and guess where they work and where
they live. It is very exciting for companies,
who can sell things to you based on that.‖ To
be sure, AI, like any technology, can be used
unethically or irresponsibly. But resistance
to AI because of this concern fails to
recognize a key point: AI systems are not
independent from their developers and, more
importantly, from the organizations using
them. (Robert D. Atkinson, 2016). AI
algorithms supercharge surveillance and
threaten privacy. For example, AI-powered
facial recognition software gives closed-
circuit TV systems the capacity to track
individuals as they move through the urban
landscape. This is concerning both socially
and politically, as privacy is key to other
fundamental rights such as freedom of
expression and association (Matthew Smith,
2018)
Conclusion
AI has got an application area in every sector
and becoming a necessity other than a luxury
especially in today`s world. On the other
hand adopting and implementing AI comes
with its own challenges which are more
visible in the developing countries. Ethiopia
will be either directly or indirectly affected
by the high disruption of artificial
intelligence because of its socio-economic
and political relations with other countries
who are adopting and implementing artificial
intelligence. Hence not adopting AI or
slowly adopting it may lead to AI divide
which can affect the country`s economy.
Recommendation
Ethiopia is a developing country and it might
lack the potential to build AI companies but
the government can find partners to work
with. Besides the government should work

179
on eliminating digital illiteracy and
developing the STEM skills of the work
force either by formal education or training.
More over transferring paper based
information to a computer based system is
necessary as data and AI are interconnected.
Last but not least AI like other technologies
require Policy and strategy of adoption and
implementation and the government need to
work towards building a legal framework for
it. The combined effort of these ideas can
push AI ahead and make Ethiopia
competitive globally.
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