Artificial Intelligence - Data Analysis, Creative & Critical Thinking and AI Values

Structured data is often held in tables such as Excel files or SQL databases. In
these cases, the rows and columns of the data hold different variables or
features, and it is often possible to discern the relationship between data
points by checking to see where data rows and columns intersect. Structured
data can easily be fit into a relational database, and examples of different
features in a structured dataset can include items like names, addresses,
dates, weather statistics, credit card numbers, etc. While structured data is
most often text data, it is possible to store things like images and audio as
structured data as well.
What is Structured Data?

Types
1. Date and Time Data type : ‘Date and Time’ datatype is used to store values
that contain both – date and time. There could be many formats, in which
date-time data can be stored. Date data type helps us to specify the date in a
particular format. Let's say if we want to store the date, 2 January 2019, then
first we will give the year which would be 2 0 1 9, then the month which
would be 01, and finally, the day which would be 02. Time data type helps us
specify the time represented in a format. Let's say, we want to store the time
8:30:23 a.m. So, first, we'll specify the hour which would be 08, then the
minutes which would be 30, and finally the seconds which would be 23. Year
data type holds year values such as 1995 or 2011.

2. String Data Type : A string is a structured data type and is often
implemented as an array of bytes (or words) that stores a sequence of
elements. A string can store alphanumeric data, which means a string
can contain [ A -Z], [ as z], [ 0 -9] and [ all special characters] but they
are all considered as if they were text. It also contains spaces. String
data must be placed within a quote (““or ' ‘)

3. Categorical data : It is a type of data that can be stored into groups or
categories with the aid of names or labels. This grouping is usually made
according to the data characteristics and similarities of these characteristics
through a method known as matching.

Data Representation
A method of analysing numerical data is data representation. In a
diagram, data representation depicts the relationship between facts,
ideas, information, and concepts. It is simple and easy to understand
and one of the most fundamental learning techniques.
Data representation techniques are broadly classified in two ways:
1. Non-Graphical technique : Tabular form and case form --This is the old
format of data representation not suitable for large datasets. Non-
graphical techniques are not so suitable when our objective is to make
some decisions after analysing a set of data.
2. Graphical representation : refers to the use of intuitive charts to
clearly visualize and simplify data sets. Data is ingested into graphical
representation of data software and then represented by a variety of
symbols, such as lines on a line chart, bars on a bar chart, or slices on a
pie chart, from which users can gain greater insight than by numerical
analysis alone.

Graphical representation includes :
(i) Line graphs : A line graph also called the line chart is a graphical
display of information that changes constantly over time Within a line
graph, the data is connected by points which show a continuous
change.
(ii) Bar diagrams : contains a vertical axis and horizontal axis and
displays data as rectangular bars with lengths proportional to the
values that they represent.

(iii) Pie diagram : A pie chart is a circular graph in which the circle is divided
into many segments or sections. Each division of the pie shows the relative
size i.e. each category’s contribution or a certain proportion or percentage of
the total.
(iv) Scatter Plots : a diagram that shows the relationship between two sets of
data, where each dot represents individual pieces of data and each axis
represents a quantitative measure.

Correlation
Correlation or dependence is any statistical relationship, whether causal
or not, between two random variables or bivariate data. In the broadest
sense correlation is any statistical association, though it actually refers to
the degree to which a pair of variables are linearly related.
Types
1. Positive : A positive correlation is a relationship between two variables
that move in tandem—that is, in the same direction. A positive
correlation exists when one variable decreases as the other variable
decreases, or one variable increases while the other increases.
2. Negative : Negative correlation is a relationship between two variables
in which one variable increases as the other decreases, and vice versa.

3. Zero or no correlation : A correlation of zero means there is no
relationship between the two variables. In other words, as one variable
moves one way, the other moved in another unrelated direction.

Data exploring
Data exploration refers to the initial step in data analysis in which data
analysts use data visualization and statistical techniques to describe dataset
characterizations, such as size, quantity, and accuracy, in order to better
understand the nature of the data.

Cases, Variable and Levels of
Measurement
Cases and Variables
A case is an experimental unit. These are the individuals from which data
are collected. When data are collected from humans, we sometimes call
them participants. When data are collected from animals, the
term subjects is often used. Another synonym is experimental unit.
A variable is a characteristic that is measured and can take on different
values. In other words, something that varies between cases. This is in
contrast to a constant which is the same for all cases in a research study.

Levels of Measurement
Level of measurement or scale of measure is a classification that
describes the nature of information within the values assigned to
variables.
Broadly, there are four levels of measurement for the variables:
(a) Nominal
(b) Ordinal
(c) Interval
(d) Ratio

(a) Nominal : In nominal measurement the numerical values just “name”
the attribute uniquely. No ordering of the cases is implied. For example,
jersey numbers in basketball are measures at the nominal level. A player
with number 30 is not more of anything than a player with number 15, and
is certainly not twice whatever number 15 is.
.

(b) Ordinal : In ordinal measurement the attributes can be rank-ordered.
Here, distances between attributes do not have any meaning. For
example, on a survey you might code Educational Attainment as 0=less
than high school; 1=some high school.; 2=high school degree; 3=some
college; 4=college degree; 5=post college. In this measure, higher
numbers mean more education. But is distance from 0 to 1 same as 3 to
4? Of course not. The interval between values is not interpretable in an
ordinal measure

(c) Interval : In interval measurement the distance between
attributes does have meaning. For example, when we measure
temperature (in Fahrenheit), the distance from 30-40 is same as distance
from 70-80. The interval between values is interpretable. Because of
this, it makes sense to compute an average of an interval variable, where
it doesn’t make sense to do so for ordinal scales. But note that in
interval measurement ratios don’t make any sense - 80 degrees is not
twice as hot as 40 degrees.

(d) Ratio : In ratio measurement there is always an absolute zero that is
meaningful. This means that you can construct a meaningful fraction (or
ratio) with a ratio variable. Weight is a ratio variable. In applied social
research most “count” variables are ratio, for example, the number of
clients in past six months.

Data Matrix and Frequency Tables
Data matrix
Data Matrix is the tabular format of representation of cases and variables
being used in your statistical study. Each row of a data matrix represents a
case and each column represent a variable. When data is collected about
any case, it is finally stored in a data matrix.

Frequency tables
A frequency table is constructed by arranging the collected data values
in either ascending order of magnitude or descending order with their
corresponding frequencies. The frequency of a particular data value is
the number of times the data value occurs (occurrences) in a given set
of data. The frequency of a data value is often represented by ‘f’.

The shape of a distribution is described by its number of peaks and by its
possession of symmetry, its tendency to skew, or its uniformity.
(Distributions that are skewed have more points plotted on one side of the
graph than on the other).
The shape of the data distribution represents:
• Spread of data i.e. scatter, variability, variance etc
• Where the central tendency (i.e. mean) lies in the data spread
• What the range of data set is
Shapes of distribution are defined by different factors such as:
1. Number of Peaks : Distribution can have single peak (unimodal), also
called modes, two peaks (bimodal) or (multimodal).
One of the most common types of unimodal distribution is normal
distribution of ‘bell curve’ because its shape looks like bell.
Graphs and Shapes of Distribution

2. Symmetry : A symmetric graph when graphed and a vertical line drawn
at the centre forms mirror images, with the left half of the graph being
the mirror image of the right half of the graph. The normal distribution or
Udistribution is an example of symmetric graphs.

3. Skewness : Unsymmetrical distributions are usually skewed. They
have pointed plot on one side of mean. This causes a long tail either in
the negative direction on the number line (left skew) or long tail on the
positive direction on the number line (positive skew or right skew).

Mode : It is the first measure of central tendency and a value that occurs most
frequently. In other words, mode is the most common outcome. Mode is the
name of the category that occurs more often.
Median : The second measure of central tendency is the median. The median
is nothing more than the middle value of your observations when they are
order from the smallest to the largest.
For a grouped data, calculation of a median in continuous series involves the
following steps:
(I) The data arranged in ascending order of their class interval
(ii) Frequencies are converted into cumulative frequencies
(iii) Median class of the series is identified
(iv) Formula used to find actual median value
l1= Lower limit of median class
cuff= Cumulative frequency of the class preceding the median class
f= Frequency of the median class
I= Class size
Mean, median and mode

3. Mean : The third measure of central tendency is the most often used
one, and also the one you most probably already know quite well: the
mean. The mean is the sum of all the values divided by the number of
observations. It is nothing but the average value.
M = ∑ fox / n
Where M = Mean
∑ = Sum total of the scores
f = Frequency of the distribution
x = Scores
n = Total number of cases

A Standardized Score (Z-Score) is useful to know how many standard
deviations an element falls from the mean. A z-score can be
calculated from the following formula.
Z-Score or Standardized Score

In the AI age, where data is the new electricity, students need to know how to
use, analyze and communicate data effectively. Data Analysis should not be
limited to mathematics, statistics or economics, but should be a cross-
curriculum concept. Institutions like the World Bank to entities like the local
government, organizations are becoming increasingly open about the
information that they gather and are ready to share the same with the public.
Those who know how to analyze and interpret data, can crunch those
numbers to make predictions, identify patterns, explain historical trends, or
find fault in arguments. Students who become data literate are better
equipped to make sense of the information that's all around them so that
they can support their arguments with reliable evidence.
Statistics is the science of data and its interpretation. In other words, statistics
is a way to understand the data that is collected about us and the world;
therefore, the basic understanding of statistics is important. There are
statistics all around us – news, in scientific observations, sports, medicine,
populations, and demographics. Understanding statistics is essential to
understand research in the social sciences, science, medicine and behavioral
sciences.
Summary

Critical and Creative
Thinking

What is Design Thinking?
Design Thinking is an iterative process in which we seek to understand the
user, challenge assumptions, and redefine problems in an attempt to
identify alternative strategies and solutions that might not be instantly
apparent with our initial level of understanding. At the same time, Design
Thinking provides a solution-based approach to solving problems. It is a
way of thinking and working as well as a collection of hands-on methods.
The five phases of Design Thinking are as follows:
Empathize – with your users
Define – your users’ needs, their problem, and your insights
Ideate – by challenging assumptions and creating ideas for innovative
solutions
Prototype – to start creating solutions
Test – solutions

Empathize :
Empathize” is the first stage of the Design Thinking process. This requires
doing away with any preconceived notions and immersing oneself in the
context of the problem for better understanding. In simple words, through
empathy, one is able to put oneself in other people's shoes and connect with
how they might be feeling about their problem, circumstance, or situation.
Define :
In the Define stage, information collected during Empathize is used to draw
insights and is instrumental in stating the problem that needs to be solved.
It's an opportunity for the design thinker to define the challenge or to write
the problem statement in a human-centred manner with a focus on the
unmet needs of the users.

Ideate :
Ideation is a creative process where designers generate ideas in sessions.
It is the third stage in the Design Thinking process. Participants gather with
open minds to produce as many ideas as they can to address a problem
statement in a facilitated, judgment-free environment.
Prototype :
A prototype is a simple experimental model of a proposed solution used to
test or validate ideas, design assumptions and other aspects of its
conceptualization quickly and cheaply, so that the designer/s involved can
make appropriate refinements or possible changes in direction.
Test :
It is One of the most important parts of the design thinking process
.Testing, in Design Thinking, involves generating user feedback as related
to the prototypes you have developed, as well as gaining a deeper
understanding of your users

Right Questioning
In the process of developing solutions using design thinking
framework, designers are expected to interact with customers / users
very frequently to gather detailed facts about the problems and user’s
expectations. A detailed analysis of these facts leads to approaching
the problem in best possible way. In order to extract / gather relevant
facts and information from users/customers, it is recommended to
use this simple and reliable method of questioning: the 5W1H
method.
To collect facts and key information about the problem, ask and
answer the 5 W's and One H question— Who? What? When? Where?
Why? and How?

Problem solving is the act of defining a problem; determining the cause of
the problem; brainstorming to generate probable solutions and selecting
alternatives for the most suitable solution.
It has often been found that finding or identifying a problem is more
important than the solution. For example, Galileo recognized the problem
of needing to know the speed of light, but did not come up with a
solution. It took advances in mathematics and science to solve this
measurement problem. Yet to date Galileo still receives credit for finding
the problem.
Identifying the Problem to Solve

Ideation is the process of generating ideas and solutions through
sessions such as sketching, prototyping, brainstorming etc. In the
ideation stage, design thinkers generate ideas — in the form of
questions and solutions — through creative and curious activities.
Ideation Will Help You:
• Ask the right questions and innovate with a strong focus on your users,
their needs, and your insights about them.
• Bring together perspectives and strengths of your team members.
• Get obvious solutions out of your heads, and drive your team beyond
them.
Ideate

Ideation Techniques
Here is an overview of the most essential ideation techniques employed
to generate numerous ideas:
Brainstorm :
During a Brainstorming session, students leverage the synergy of the
group to generate new innovative ideas by building on others’ ideas.
Participants should be able to discuss their ideas freely without fear of
criticism. A large number of ideas are collected so that different options
are available for solving the challenge.
Brain dump :
Brain dump is very similar to Brainstorm; however, it’s done individually.
It allows the concerned person to open the mind and let the thoughts be
released and captured onto a piece of paper. The participants write down
their ideas onto paper or post-it notes and share their ideas later with
the larger group.

Brain writing :
Brain writing is also very similar to a Brainstorm session and is known
as ‘individual brainstorming’. At times only the most confident of team
members share their ideas while the introverts keep the ideas to
themselves. Brain writing gives introverted people time to write them
down instead of sharing their thoughts out loud with the group. The
participants write down their ideas on paper and, after a few minutes,
pass on their own piece of paper to another participant who then
elaborates on the first person’s ideas and so forth. In this way all
participants pass their papers on to someone else and the process
continues. After about 15 minutes, the papers are collected and posted
for instant discussion.

A Focus on Empathy
Empathy is the first step in design thinking because it allows
designers to understand, empathize and share the feelings of the
users. Through empathy, we can put ourselves in other people’s
shoes and connect with how they might be feeling about their
problem, circumstance, or situation.

An extremely useful tool for understanding the users’ needs and gaining a
deeper insight into the problem at hand is the empathy map. It also helps in
deepening that understanding, gaining insight into the user’s behavior.
To create a “persona” or profile for the user, we can use the empathy map to
create a realistic general representation of the user or users. Personas can
include details about a user’s education, lifestyle, interests, values, goals, needs,
thoughts, desires, attitudes, and actions.
What’s an Empathy Map?

We are living in a rapidly changing complex world characterized by
learning, unlearning and relearning which happens to be the new
normal. 85% of the future jobs have either not been visualized or
invented yet. So, how can we prepare our children for a future full of
uncertainty and dramatic changes? Of the 10 skills expected to be in
high demand in the future, World Economic Forum lists complex
problem solving, critical thinking and creativity as the top three skills for
future employment. Hence, it becomes imperative for the school or an
educator to prepare and connect the students with the demands of the
real world. And design thinking can be instrumental in establishing such
connections.
Summary

AI Working for Good
Across many sectors, AI offers advantages of new and innovative services,
and the potential to improve scale, speed and accuracy. AI extends and
combines many of these advantages with insights from statistics and big
data.. At the World Economic Forum 2019 in Davos, Paul Daugherty,
Accenture’s Chief Technology and Innovation Officer floated the idea of
Human + Machine = Superpowers.
Here are some actual AI projects on which companies like Google, IBM
etc. are working to serve humanity:
1. IBM : (a) Applying AI to accelerate COVID-19 Research. As the COVID-
19 pandemic unfolds, we continue to ask how these technologies and
our scientific knowledge can help in the global battle against the
corona virus.
(b) The potential benefits of AI for breast cancer detection
(c) AI Enables Foreign Language Study Abroad, No Travel Required

2. Google : (a) Keeping people safe with AI-enabled flood forecasting.
3. Assessing Cardiovascular Risk Factors with Computer Vision Agricultural
productivity can be increased through digitization and analysis of images
from automated drones and satellites.
4. AI can be instrumental in providing personalized learning experience to
students.
5. AI can help the people in special needs in numerous ways. AI is getting
better at doing text-to-voice translation as well as voice-to-text translation,
and could thus help visually impaired people, or people with hearing
impairments, to use information and communication technologies (ICTs).
6. Pattern recognition can track marine life migration, concentrations of life
undersea and fishing activities to enhance sustainable marine ecosystems
and combat illegal fishing.
7. With global warming, climate change, and water pollution on the rise,
we could be dealing with a harsh future. Food shortages are not something
we want to add to the list.

8. Imago AI an India-based agri-tech startup that aims to use AI to increase
crop yields and reduce food waste. The company’s vision is to use technology
to feed the world’s growing population by optimizing agricultural methods.

Artificial Intelligence (AI) is a technology to design a machine which can
perform tasks normally requiring human intelligence. In light of its
powerful transformative force and profound impact across various
societal domains, AI has sparked ample debate about the principles and
values that should guide its development and use.
UNI Global Union has identified 10 key principles for Ethical AI:
1. AI systems must be transparent : Consumers should have the right to
demand transparency in the decisions and outcomes of AI systems as
well as their underlying algorithms. They must also be consulted on
AI systems’ implementation, development and deployment.
2. AI systems must be equipped with an “ethical black box” : The ethical
“black box” should not only contain relevant data to ensure system
transparency and accountability, but also include clear data and
information on the ethical considerations built into the system.
Principles for Ethical AI

3. AI must serve people and planet : Codes of ethics for the development,
application and use of AI are needed so that throughout their entire
operational process, AI systems remain compatible and increase the
principles of human dignity, integrity, freedom, privacy and cultural and
gender diversity, as well as fundamental human rights.
4. Adopt a human-in-command approach : The development of AI must
be responsible, safe and useful where machines maintain the legal
status of tools, and legal persons retain control over, and responsibility
for these machines at all times.
5. Ensure a gender less, unbiased AI : In the design and maintenance of AI
and artificial systems, it is vital that the system is controlled for negative
or harmful human-bias, and that any bias–be it gender, race, sexual
orientation, age–is identified and is not propagated by the system.
6. Share the benefits of AI systems : The economic prosperity created by AI
should be distributed broadly and equally, to benefit all of humanity.
Global as well as national policies aimed at bridging the economic,
technological and social digital divide are therefore necessary.

7. Secure a just transition and ensure support for fundamental freedoms
and rights : As AI systems develop and augmented realities are
formed, workers and work tasks will be displaced. It is vital that
policies are put in place that ensure a just transition to the digital
reality, including specific governmental measures to help displaced
workers find new employment.
8. Establish global governance mechanism : Establish multi-stakeholder
Decent Work and Ethical AI governance bodies on global and regional
levels. The bodies should include AI designers, manufacturers, owners,
developers, researchers, employers, lawyers, CSOs and trade unions.
9. Ban the attribution of responsibility to robots : Robots should be
designed and operated as far as is practicable to comply with existing
laws, and fundamental rights and freedoms, including privacy.
10. Ban AI arms race : Lethal autonomous weapons, including cyber
warfare, should be banned.

What is Bias?
Bias is a tendency to lean and act in a certain direction, either in
favour of or against a particular thing. Bias lacks the neutral viewpoint.
Similarly, AI bias is an anomaly in the output of machine learning
algorithms, due to the prejudiced assumptions made during the
algorithm development process or prejudices in the training data.

1. Educate and check yourself : The first step to removing bias is to proactively
look out for it and keep checking your own behaviour, as a lot of bias is
unconscious.
2. Build a diverse team : Another way to reduce the risk of bias and to create
more inclusive experiences is to ensure the team building the AI system is
diverse. This should include the engineer teams, as well as project and middle
management, and design teams.
3. Be able to explain automated decisions : Explainable AI is new normal. With AI
in system, ability to explain the algorithm under the hood is critical. This involves
ensuring transparency at both the macro level as well as at the individual level.
4. It’s all about the data : make sure you choose a representative dataset --
Choosing data that is diverse and includes different groups to prevent your model
from having trouble identifying unlabeled examples that are outside the norm.
Make sure you have properly grouped and managed the data so you aren't forced
to face similar situations as Google and its facial recognition system.
How to fix biases in AI and machine
learning algorithms?

AI is progressing towards a stage where, eventually it will replicate
human’s general intelligence. The possibility of making a thinking
machine raises a host of ethical issues. These ethical questions ensure
that such machines do not harm humans and the society at large. To
harness the potential of AI in the right way, guidelines and ethical
standards are therefore required. As a result, ethical guidelines have
been developed in recent years and developers are expected to be
adhere to these principles.
Inspite of the standards, collectively as a society we have to face the
challenges arising from current AI techniques and implementations, in
the form of systematic decrease in privacy; increasing reliance on AI
for our safety, and the ongoing job losses due to mechanization and
automatic control of work processes.
Summary

Artificial Intelligence - Data Analysis, Creative & Critical Thinking and AI Values

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