AI offers the impression that the creation of new jobs and opportunities will account for
the temporary loss of certain replaceable jobs. However there is also a strong argument
that new jobs will be concentrated in certain parts of the developed world, whilst posing
a threat to developing regions such as Africa through the substitution of low-wage jobs
with robots that could ultimately displace millions of jobs in the future.
An example of this trend is automated systems, substituting many semi-skilled jobs
present throughout administrative, labour or customer relations roles in automotive,
banking, health, insurance, accounting and call centre industries which are typically
outsourced to developing countries such as India, Vietnam, south Africa and Morocco.
Poorer nations have used their relatively low-cost labour force as a driving force to
catch up with developed economies, examples being China, Thailand and Vietnam.
This method of outsourcing may soon come to a close as the growing influence of AI
and supplementary technologies like 3D printing will encourage consumers to
manufacture locally with the convenience of customisation.
The IT industry also encourages skilled workers to migrate to safe and developed cities
that offers career opportunities like Silicon Valley, but exacerbates skilled labour in
developing cities, further driving inequality between regions.
It is critical that AI is used to serve the broader society such as boosting productivity in
developing economies and enabling greater access to education, health, employment
and other opportunities. M-Pesa and M-Tiba serves as a great example by facilitating
mobile phone-based money transfer and health services in Kenya. It is crucial for policy
makers to confront the rapidly and far reaching impacts of AI and make decisions that
will benefit society.
Photography was a chemistry issue
As Arithmetic became cheap, it was replaced. This is now in Banking, music,
communication etc.
CES (robotics, drones, VA,
Economists say AI is in a different category. The rise of AI is the drop in cost of
prediction.
Prediction: taking information you have to generate information you don’t have. e.g:
Demand forecasting, classification. The cost of prediction is plummeting, and the
implication is that we will be using significantly more of it (better faster cheaper), we will
start converting non prediction problems into prediction problems (autonomous driving
was once seen to be impossible because it would require programming of an infinite
number of variables in an uncontrolled environment. This has since changed with AI
which uses predictions of a safe driver’s actions. Language translation was once rule
based issue requiring expensive resources such as linguists, but is now a prediction
machine. Google will soon launch a commercial translation device called google pixel
buds, which uses AI).
Input, Prediction, judgement( how costly would it be for the prediction error), action,
Outcome, Feedback.
Implications for jobs in the economy. The substitute machine intelligence (Value of
human prediction falls as the capability of machine prediction increases and its cost
decreases. Human prediction is very flawed, it has systematic biases and is very slow
and leads to failure often and thus future businesses will be likely to incorporate
machine prediction over human labour, a bleak view of the future. However the value of
other factors like Input, Judgement, Action, Outcome and feedback will increase as a
complement of Prediction. This means that data will become more valuable as an
aspect of input, since it can be used with variety as a result of cheaper prediction. Our
judgement increases dramatically as the value of machine prediction increases, a
doctor can
They’re the things that increase in value; data is the new oil
AI as a prediction tool is actively increasing its accuracy and quality of prediction at a dynamic
pace, and could have far reaching impacts that could surmount to a
Amazon is making efforts to improve their prediction accuracy of consumer choices and with
enough progress, this could fundamentally disrupt their business strategy of customer shopping
and shipping. Amazon’s patented and tested anticipatory shipping strategy through clothes in
certain markets, serves as an example of this future disruption as
Mobile to AI strategy
Science of how we see Visual Analysis visual perception
1. We don’t go in order
Visuals aren’t read in a predictable, linear way, as text is. Instead, we look first at the
visual and then scan the chart for contextual clues about what is important. We should
write charts spatially.
2. We see first what stands out
Our eyes go directly to change and difference, such as unique colours, steep curves,
clusters or outliers.
3. We see only a few things at once
The more data that’s plotted in a chart, the more singular the idea it conveys. If we
need to focus on individual data points, we should plot as few as possible so that the
visuals don’t disappear into an aggregate view.
4. We seek meaning and make connections
Our minds incessantly try to assign meaning to a visual and make causal connections
between the elements presented, regardless of whether any real connections exist.
5. We rely on conventions and metaphors
We use learned shortcuts to assign meaning to visual cues based on common
expectations. For example, green is good and red is bad
The Two Questions
1. Is the information conceptual or data-driven?
2. Am I declaring something or exploring something?
1. Conceptual or Data Driven?
Conceptual: Focus in Ideas, and the aim is to simplify, and teach
- E.G (Here’s how our organisation is structured)
Data Driven: Focus in Statistics, and the aim in informing, enlightening
- E.G (Here are our revenues for the past 2 years)
- Data driven charts could take on a conceptual form and vice versa
2. Declarative or Exploratory?
Declarative / Confirmatory : Focus in documenting, designing and the aim is in affirming the
message
- Declarative E.G(Here are our revenues over the past five years)
- Confirmatory; is what I suspect actually true? And what are some other ways of looking
at this idea? E.G ( Lets see if seasonal cycles are the causes to a dip in sales)
Exploratory: Focus in prototyping, iterating, interacting, automating
- Corroborate, E.G(Let’s see if marketing investments contributed to rising profits)
- Discover, E.G (What would we see if we visualised customer purchases by gender
location?)
Declarative
Idea Illustration
Everyday dataviz
Data/info type: Process, framework
Viz type: Simple, metaphorical
Viz features: Convention, metaphor
Typical setting: Presentations, teaching
Talent skew: Design, editing
Goals: Learning, simplifying
Examples: process diagram, cycles, trees, bridges,
hierarchies, pyramids.
Data/info type: Simple, low volume
Viz type: Conventional chart, static
Viz features: Clear point, simple narrative
Typical setting: Formal, presentations
Talent skew: Design, storytelling
Goals: Affirm, set context
Examples: Line/bar chart that gets the message across
in the right context
Data Driven
Conceptual
Visual Discovery
Idea Generation
Data/info type: Complex, undefined
Viz type: Creative, metaphorical
Viz features: Convention, metaphor
Typical setting: Working session, Brainstorming
Talent skew: Team Building, facilitation
Goals: Discovery, simplification
Examples: Informal settings, notes/scribbles
Data/info type: Big Data, complex, dynamic
Viz type: Advanced, unconventional
Viz features: Interactive, autodynamics
Typical setting: Working session, testing, analysis
Talent skew: BI, programming
Goals: Trend Spotting, sense making, deep analysis
Examples: Complex network diagrams, rough
scatterplots, Function over form
Exploratory
Data Visualisation Process:
Sketch
1. Prep: basics for reminders, material prep, intended audience and context
2. Talk and listen: Discuss with associates, phrases, words, statements that captures idea
3. Sketch: Match keywords to chart, try different visual approaches
4. Prototype: more accurate detailed sketch,
Refine
1. Focus on design structure and hierarchy
Title, subtitle, visual field, source line, align elements, consistent weights
2. Charts that are straightforward should focus on design and clarity
Remove unnecessary elements, make sure all elements support the idea
3. Design simplicity leads to elegance
Show only what’s needed, avoid belt and suspenders, limit eye travel.
Refine to persuade
1. Hone the main idea
This will expose where and how you can focus your energy on persuading an audience
What am I trying to say or show?
I need to convince them that … etc
2. Make it stand out
Use simple design to support the main idea
3.
-
Emphasize: the main idea by adding
Isolate the main idea by reducing unique attributes
Adjust what’s around it
Remove reference points
Add reference points
Shift reference points
Persuasion vs Manipulation
1. The truncated Y Axis
Chart that removes value ranges from y-axis, thereby removing data from the visual
field.
Advantages: emphasises changes, makes curves curvier and distance from points bigger
Disadvantages: It can exaggerate or misrepresent change, making increases look steep.
2. The double Y Axis
Chart that includes 2 vertical scales for different data
Advantages: compels viewer to make comparison between datasets
Disadvantages: relationships between different values are artificial.
3. The Map
Geographical boundaries to encode values related to that location, such as voting
results by region.
Advantages: Geography is a convention that allows to find data quickly on basis of
location.
Disadvantages: Size of region doesn’t necessarily reflect the data encoded.
Present to persuade
1. Show the chart and stop talking
2. Don’t read the picture
3. For unusual visual forms, guide the audience
4. Use reference charts
5. Ged rid of chart if you something important to say
6. Show simplicity but include more information
Presentation engagement tips
1. Create tension: show part before the full visual, ask audiences to speculate
2. Use time: Reveal large values gradually for easier understanding
3. Zoom in or out: Increase or decrease scale to show value to understand
4. Bait and Switch: Lure viewers in with a visual they may expect to see and then show
actual version
5. Deconstruct and reconstruct: Break down visual into smaller parts
6. Tell stories: Use dramatic structure of setup, conflict and resolution to make charts tell a
story
Visual Criticism
1. Make a note of the first few things you see
2.
3.
4.
5.
Make a note of the first idea that forms in your mind and then search for more
Make notes on likes, dislikes and wish I saws
Find three things you’d change briefly
Sketch and prototype your own version, and critique yourself
Comparison
Composition
Variable width chart
Comparison among items
2 variables/item
Stacked 100% Bar chart
Changing over time
Only relative differences matter
Few periods
Table with embedded charts
Comparison among items
Many categories
One variable/item
Stacked area chart
Many periods
Relative and absolute
differences matter
Many Periods
Pie chart
Static
Simple share of total
Bar Chart
Comparison among items &
Time
One variable and few
categories
Circular Area chart
Comparison over time
Cyclical data
Line chart
Comparison over time
Non-cyclical data
Many categories
Relationship
Waterfall chart
Static
Accumulation or subtraction of
total
Tree Map
Static
Accumulation to total and
absolute difference matters
Distribution
Scatter Plot
2 variables
Histogram
Single variable
Few datapoints
Scatter Plot bubble size
3 or more variables
Line histogram
Single variable
Many data points
Scatter plot
2 variables
Alluvial chart
Maps Network Logic
Flow Chart
Geography
Hierarchies
2X2
Networks
In the case of salon reservations, customers were booking time with a stylist whereas restaurants were
booking seats
Simplicity, development board meetings
Disruption to the business strategy can lie in AI. Since AI are prediction machines, as the level of
prediction accuracy increases, Recruit could potentially override all verticals of its business where it
would begin to book customer’s sessions before their intention.
1.
2.
3.
4.
5.
Adecco Group
Randstad
Manpower Group
Allegis Group
Recruit Holdings Co., Ltd is the world’s 4th largest staffing company
Recruit Holding’s
Current 46 Billion dollar valuation
-
Permanent and temporary staffing
HR services
Executive search and online recruitment
Provision of advertisements an value added services in travel real estate automobiles,
dining, beauty, and weddings
Goals
1. Largest staffing agency in the world in terms of number of positions filled by 2020
2. Largest marketing media company by 2030
3. Recently announced that they want to have the most users by 2030
Strategy
1. Strengthening operations in Japan
2. Expanding globally through mergers and acquisitions
3. Creating new businesses by leveraging data analytics, machine learning and AI
Leaders
-
Ken Asano CEO of Recruit Lifestyle
Yoshihiro Kitamura managing Corporate executive officer of Media and Solutions
Alon Halevy CEO of Recruit Institute of Technology (RIT)
History
Recruit was rocked by a major political bribery and insider trading scandal which forced the retirement of
the Prime Minister Noboru Takeshita as well as Recruit’s founder Hiroshima Ezoe. Coupled with this
scandal and the sharp downturn in the financial markets and the real estate industry, Recruit’s growth was
stunted and was subsequently ¥1.4 trillion in debt.
Since this drastic event, Recruit rapidly tightened its ethical rules recovering the trust of consumers and
clients and took advantage of the rapid emergence of the internet in the 1990s, identifying new key
markets in house rentals, bridal, travel and hospitality to provide information services. Today the
sustained level of Recruit’s ethical responsibility is evident where their primary goal, avoids promising
certain sales or financial figures as it may pressure the organisation to partake in unethical actions. It
however maintains strong ambition with its goals of reaching the most users by 2030.
As recruit’s operations begins to be more involved with AI and it’s capacity to replace rudimentary roles,
it is crucial to be aware of its potential in driving inequality across other regions, and displacement of
jobs. Recruit’s transition from a paper magazine medium to the internet has in part reduced the demand
for printing services but also increased the need for jobs in cybersecurity, webdesign etc. AI can allow
recruit to access developing country markets with a superior unmatched service and uplift many semi
skilled labour in the process.
The IT industry also encourages skilled workers to migrate to safe and developed cities that offers career
opportunities like Silicon Valley, but exacerbates skilled labour in developing cities, further driving
inequality between regions.
AI offers the impression that the creation of new jobs and opportunities will account for the temporary
loss of certain replaceable jobs. However there is also a strong argument that new jobs will be
concentrated in certain parts of the developed world, whilst posing a threat to developing regions such as
Africa through the substitution of low-wage jobs with robots and AI that could ultimately displace millions
of jobs in the future
An example of this trend is automated systems, substituting many semi-skilled jobs present throughout
administrative, labour or customer relations roles in automotive, banking, health, insurance, accounting
and call centre industries which are typically outsourced to developing countries such as India, Vietnam,
south Africa and Morocco. Poorer nations have used their relatively low-cost labour force as a driving
force to catch up with developed economies, examples being China, Thailand and Vietnam. This method
of outsourcing may soon come to a close as the growing influence of AI and supplementary technologies
like 3D printing will encourage consumers to manufacture locally with the convenience of customisation.
The IT industry also encourages skilled workers to migrate to safe and developed cities that offers career
opportunities like Silicon Valley, but exacerbates skilled labour in developing cities, further driving
inequality between regions.
It is critical that AI is used to serve the broader society such as boosting productivity in developing
economies and enabling greater access to education, health, employment and other opportunities. MPesa and M-Tiba serves as a great example by facilitating mobile phone-based money transfer and health
services in Kenya. It is crucial for policy makers to confront the rapidly and far reaching impacts of AI and
make decisions that will benefit society.
Economists say AI is in a different category. The rise of AI is the drop in cost of
prediction.
Prediction: taking information you have to generate information you don’t have. e.g:
Demand forecasting, classification. The cost of prediction is plummeting, and the
implication is that we will be using significantly more of it (better faster cheaper), we will
start converting non prediction problems into prediction problems (autonomous driving
was once seen to be impossible because it would require programming of an infinite
number of variables in an uncontrolled environment. This has since changed with AI
which uses predictions of a safe driver’s actions. Language translation was once rule
based issue requiring expensive resources such as linguists, but is now a prediction
machine. Google will soon launch a commercial translation device called google pixel
buds, which uses AI).
Input, Prediction, judgement( how costly would it be for the prediction error), action,
Outcome, Feedback.
Implications for jobs in the economy. The substitute machine intelligence (Value of
human prediction falls as the capability of machine prediction increases and its cost
decreases. Human prediction is very flawed, it has systematic biases and is very slow
and leads to failure often and thus future businesses will be likely to incorporate
machine prediction over human labour, a bleak view of the future. However the value of
other factors like Input, Judgement, Action, Outcome and feedback will increase as a
complement of Prediction. This means that data will become more valuable as an
aspect of input, since it can be used with variety as a result of cheaper prediction. Our
judgement increases dramatically as the value of machine prediction increases, a
doctor can
feedback data greatly improves the prediction quality and informs better judgement
overall. Combining offline and online data
and enables the organisation to stand apart from major players like google, facebook,
amazon etc.
AI as a prediction tool is actively increasing its accuracy and quality of prediction at a dynamic
pace, and could have far reaching impacts that may surmount to a disruption in business
strategy. This notion is best outlined in Amazon’s endeavours to transform its online shopping
to shipping business model through the use of AI. By gradually improving the prediction
accuracy of product recommendations to customers, Amazon could eventually find success in
its patented and tested anticipatory shipping strategy which is to ship the product to the
customer before they even shop for them, providing convenience to customers, upselling
additional Amazon products, simplifying logistical processes and gaining the first mover
advantage. This profound disruption of the conventional online shopping model through AI,
serves as one potential example in many other major transformations that could occur in
Recruit’s various business strategies that may significantly enhance its ribbon model.
Through data, businesses could forecast certain days or specifically certain times of peak
demand from customers to Recruit’s restaurant and salon clients. This information could be
provided to assist store owners to allocate the appropriate resources and staff during busy
periods and also highlights certain opportunities for clients to take advantage of.
Cultivating an online and offline data generating environment through Recruit is the
foundation to AI applicability across all current and future business activities This is because
feedback data, informs greater accuracy in predictions and is often difficult and costly to
collect. The success of Recruit’s sales team in enriching relationships with their clients has
allowed access to deep offline data, such that the outcome of online advertising campaigns
could be measured reliably through the boost in sales. This efficient input and feedback
system that Recruit has developed with their clients, serves as an ideal foundation for
accurate predictions which holds an advantage from major players like Google, Facebook and
Amazon. This will likely accelerate Recruit to be one of the first beneficiaries in an AI induced
business disruption.
Like Amazon’s anticipatory shopping strategy Recruit’s client to customer matching business
strategy, such as the AI applications in SalonBoard’s service can experience a major
disruption as AI plays a larger role in the future. As the prediction quality increases with a
growing wealth of consolidated input and feedback data, Recruit can begin to accurately
predict when a user will require a haircut and even the specific hair style, weeks or even
months ahead of the user’s inception to get a haircut. Subsequently all appointments could
be automated on behalf of the user, which completely expedites the booking and scheduling
process away from the client, ultimately improving scheduling efficiency, resource and
workload management and allowing the clients to “do what they do best – look after
customers”. From the user’s side, the inconvenient cost of searching for the right salon as
well as booking an optimal time is mitigated through prediction, further improving the
relationship with the Salon as well as gaining the reduced-price advantage derived from the
Salon’s cost savings as a result of operational efficiency. This shared reduction in cost
between the user and client driven by prediction, will entice more client and customer
participants as well as establish Recruit’s position as a “reliable friend for the end customer”
and simultaneously increase dependency from their clients.
This transformation is effectively applicable to all business segments across Recruit,
especially in the profound enhancements in HR services, as the process of marketing to a
large range and narrowing down to the best candidate, is ineffective with biases, and costly
overall. This convention can soon transform into an approach where candidates will have
been selected through prediction algorithms.
Since Recruit’s acquisition of Indeed, the organisation has aims to improve job matching
through the adoption of AI, using input data from candidates and employer’s proximity,
salary estimates, ratings and reviews, and so on. In its strategy, Indeed employs natural
language processing to the listings on its site, optimizing unstructured data into text that
appeals to the right candidates and has since increased the rate of employers responding
back to applicants by 10 percent across all industries. Furthermore, the organisation aims to
enhance the ribbon model of matching employers and candidates through AI by evaluating
candidates’ résumés and predict their suitability to the role requirements and provide
candidate evaluation models for employers to issue to candidates, to further improve
optimisation and reduce cost of examination. Essentially the emphasis in AI intervention for
the HR platform is reduced cost, as employers will gain access to a larger and more desirable
pool of candidates that can reach towards specific talent across country borders, as well as
anticipating promising individuals undertaking education and training which before AI,
restrained this possibility. Grasping this new technology into their HR segment can push
Recruit into the forefront of the staffing industry and fulfilling its ambitious goals of reaching
the largest number of positions filled.
Other creative ways to incorporate AI to improve Recruit’s verticals would be open source
code like image recognition which could be significantly helpful for fulfilling a customer’s
specific requests by uploading a photo of a specific hair style or food type to then be
recommend with an appropriate client that can fulfil the specific request.
Upon discovery of AI’s application in HR and combining with Salon board, we can really see
the profound far reaching impacts of AI to the overall ribbon model.
Indeed’s candidate evaluation prediction service will always be constrained to an assistive
role and must be subject to greater scrutiny. This is because the algorithm may not perform
accurately in the real world and could commit unintended unethical acts to perform its duty.
Partaking in AI into business strategy must align with corporate ethics and values and broader
societal morals, which suggests that AI must have extensive training with humans and is likely
to play an augmented role over a primary one. Many examples of AI’s misuse have emerged
recently such as its involvement in recommending vaccine misinformation, in the form of
articles and youtube videos to vaccine sceptical viewers, which progressively compelled many
to become adamant antivaxers today. The moral externalities that is not factored in to Ai which
lacks inquisitiveness, is evident here as it’s aim to keep viewers on the platform and increase ad
viewership is also noticeable in a growing trend where political opinion pieces are attached
with more extreme, inaccurate and misguided material such as crackpot conspiracy theories
which has been proven to attract viewership. 22% of net sales are sourced from Recruit’s
targeted advertising and marketing revenue from their platforms. With a significant portion of
the revenue share coming from targeted ads, Recruit must be capable to factor in ethical
controls where necessary to avoid any forms of discrimination or spread of misinformation. This
was exhibited where higher salary job postings on google, were disparagingly less visible for
females and google search only reflected 11% of the actual 27% of female CEOS in the industry.
It is strongly recommended for Recruit to view this case as a central concern in using prediction
models to recommend clients with customers and mitigate any scenario where discrimination
may occur.
As recruit’s operations begins to be more involved with AI and it’s capacity to replace rudimentary roles,
it is crucial to be aware of its potential in driving inequality across other regions, and displacement of
jobs. Recruit’s transition from a paper magazine medium to the internet has in part reduced the demand
for printing services but also increased the need for jobs in cybersecurity, webdesign etc. AI can allow
recruit to access developing country markets with a superior unmatched service and uplift many semi
skilled labour in the process.
The IT industry also encourages skilled workers to migrate to safe and developed cities that offers career
opportunities like Silicon Valley, but exacerbates skilled labour in developing cities, further driving
inequality between regions.
Boston consulting and MIT published a report that Ai is creating both fear and hope for the
general workforce. The industrial revolution and the computer revolution has both produced
significant transformation in jobs which has also increased in variety. d
Interpretability
Data citizenship
Fairness
Governance
Future of work
Most users in Recruit’s matching platform implements
This will unfortunately drive inequality if HR boosts the connection of workers to developed
economies etc etc



Prediction machines utilize three types of data:(1)training data for training the AI, (2) input
data for predicting, and (3) feedback data for improving the prediction accuracy.
Data collection is costly; it’s an investment. The cost of data collection depends on how
much data you need and how intrusive the collection process is. It is critical to balance the
cost of data acquisition with the benefit of enhanced prediction accuracy. Determining the
best approach requires estimating the ROI of each type of data: how much will it cost to
acquire, and how valuable will the associated increase in prediction accuracy be?
Statistical and economic reasons shape whether having more data generates more value.
From a statistical perspective, data has diminishing returns. Each additional unit of data
improves your prediction less than the prior data; the tenth observation improves
prediction by more than the one thousandth. In terms of economics, the relationship is
ambiguous. Adding more data to a large existing stock of data may be greater than adding it
to a small stock—for example, if the additional data allows the performance of the
prediction machine to cross a threshold from unusable to usable, or from below a
regulatory performance threshold to above, or from worse than a competitor to better.
Thus, organizations need to understand the relationship between adding more data,
enhancing prediction accuracy, and increasing value creation.
Unknown Knowns are a common underlying issue affecting prediction accuracy
Data can inform decisions as well as come from decisions. If machines do not understand the
decision process that generated the data, its predictions can fail. This can arise from reverse
causality
The process is an excellent predictor that can enhance
1. Good predictions (data that is used to make the decision)
2. Reverse causality (data that was derived to trace back from the decision that was
already made)
3. Omitted variables (data that is not yet known that contributed to the prediction)
Reverse Causality: The feedback data on an action not taken, can often be unattainable as
many circumstances only allows for data to be observed ensuing the action taken.
Disadvantages of AI