Academic year 2011-2022 Maastricht University School of Business and Economics

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School of Business and Economics

Bachelor in Business Analytics

COURSE Manual

Responsible Data Use

EBCXXXX

Academic Year: 2021-2022

Course Period: Semester 1, Block 2

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Table of Contents

Introduction ..................................................................................................................................3

Prerequisites .................................................................................................................................4

Couse coordinator and tutor...........................................................................................................5

Learning objectives ........................................................................................................................6

Course structure ............................................................................................................................8

Literature ......................................................................................................................................9

Assessment ................................................................................................................................. 10

Grading ....................................................................................................................................... 11

Fraud and Plagiarism.................................................................................................................... 12

Comments and Complaints........................................................................................................... 12

Course schedule .......................................................................................................................... 14

Lectures & Tutorials ..................................................................................................................... 15

Lecture 1: Introduction to Responsible Data Cycle...................................................................... 15

Tutorial 1: Overview (from design to feedback and disposal) ...................................................... 15

Lecture 2: AI, Philosophy and Ethics .......................................................................................... 16

Tutorial 2: Creating machines in our own image? ....................................................................... 16

Lecture 3: Privacy, Anonymity and GDPR ................................................................................... 17

Tutorial 3: Whose data? ........................................................................................................... 17

Lecture 4: Security ................................................................................................................... 18

Tutorial 4: Introduction to cryptography algorithms ................................................................... 18

Tutorial 5: Security continued ................................................................................................... 19

Tutorial 6: Integrity .................................................................................................................. 19

Tutorial 7: Integrity continued .................................................................................................. 20

Tutorial 8: CSI Data................................................................................................................... 20

Tutorial 9: Project Presentations and further discussions............................................................ 21

Tutorial 10: Course recap and responsible statistics. .................................................................. 21

Appendices.................................................................................................................................. 22

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Introduction

This course addresses one of the most important contemporary issues - responsible data

use. The concept of responsible data management is based on understanding the individual

and societal collective duty to prioritize and respond to the ethical, legal and social

challenges coming from the “use of data” by humans (or machines) under different

“digitalisation scenarios”. The key elements of the responsible data use - data privacy,

data protection and data ethics - are discussed in details. Different scenarios in data use

(by humans and algorithms alike) are analyzed through concepts such as fairness, equity,

and justice. Moreover, the main feature of the course is to bring all these three elements

together and to discuss them in context of contemporary legal and technological

environment as well as future development.

This course deals with philosophical, social, ethical, legal, security and privacy-

related challenges that come from using data. Almost any data science project spans

multiple phases, sometimes simultaneously, which require specific attention to the

aforementioned challenges. The course is structured around 5 phases concerning the data

cycle, i.e., design, collection, storage, use, feedback. Philosophical issues may raise

in the design or data collection phase, while security is mostly a concern specific to storing

and using data. While privacy and ethics sound like a concern for only data collection, it

can have serious legal consequences in terms of disposal of the data.

All in all, there’s an undeniable need for a responsible and an ethical handling of this

domain. The ultimate aim of this course is to create awareness while endowing the

participants with the toolkit to understand, analyse and attend to the aforementioned

challenges responsibly.

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Prerequisites

This course has two of the Year 1 courses as prerequisites:

1- Introduction to Business Analytics

2- Statistics

Although no particular programming language is a prerequisite, throughout the course the

students are expected to develop very basic skills to tackle some of the cases in the manual

including:

1- HTML

2- JavaScript

As explained, the course will focus on the ethical, legal, social, philosophical, security and

privacy-related challenges in responsible data use. However, besides its social and ethical

content, the course has a technical element to it. The students are also expected to develop

the basic skills to understand these social issues from a technical point of view. To that

end, there will be material and links provided throughout the block period.

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Couse coordinator and tutor

This course is developed by Burak Can, at the Department of Data Analytics and

Digitalisation (DAD), SBE, Maastricht University.

Details:

Coordinator:

Dr. Burak Can

Department of Data Analytics and Digitalisation

Maastricht University

Building: Tongersestraat 53 (TS53), Room F2.08

Email: b.can@maastrichtuniversity.nl

appointment by email only

Office Hours:

Every Friday 11:00-12:00

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Learning objectives

Below you will see a summary of the learning objectives for this course and their match

with the overall learning goals of the bachelor programme.

SBE BSc Learning Goals Learning Objectives of this course Assessment & Feedback

1. Knowledge and insight

1.1 Multi-disciplinary knowledge

-Students have knowledge of basic

concepts within different academic

disciplines, i.e. economics,

mathematics, statistics and computer

science.

1.2 Knowledge application

-Students apply, combine and integrate

models, theories, methods, techniques

and concepts, possibly originating from

different disciplines, to analyse a

business (analytics) problem.

-Students understand the philosophy,

ethics, and socio-economics of digital

technologies,

-Students understand and learn

social/ethical dilemmas, research

integrity and legal boundaries in data

use,

-Students gain an overall awareness of

responsibility in data sciences, learn to

implement responsible data analytics

projects.

-Group discussions, assignments, games

and feedback during course on the final

project

-Group discussions, assignments, games

and feedback during course on the final

project

-Final project; feedback during course on

exercises

-Final Exam

2. Academic Attitude

2.1 Argumentation

-Students are able to build valid

argumentation using empirical evidence

and theories learnt in the field of

business analytics.

2.3 Critical Reflection

-Students are able to come to

conclusions and substantiate this in a

logical and structured way using

business (analytics) evidence, and do so

in a contemplative and intellectually

responsible manner.

-Students critically assess academic

papers/projects through ethical

research conduct and investigate

research misconduct on data.

-Students have a comprehensive

understanding of research integrity,

academic ethics and data security.

-Assignments on research integrity, group

discussions

-Assignments on research integrity, group

discussions

-Assignment on security, and research

integrity, games, and feedback during the

course on the final project.

-Final Exam

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3. Global Citizenship

3.2 Social Responsibility and Ethics

-Students are able to understand and

interpret the professional, cultural and

social context in which they are

operating as business analysts, and can

oversee the responsibility required by

their function, and ethical consequences

of their data-driven decisions.

-Students understand and can evaluate

the societal implications of changes

induced by new technologies,

identification of biases and moral issues

in the data, or in the algorithms, e.g., in

machine learning.

-Assignments, group discussions, games,

and feedback during the course on the

final project.

-Final exam

4. Interpersonal Competences

4.1 Oral and written communication

-Students effectively communicate

information, and solutions to problems

to both specialist and non-specialist

audience; both through written (e.g.

application design, data visualisation,

reports) and oral format (e.g.

discussion, presentations, providing and

receiving feedback).

4.3 Team work

-Students are able to share knowledge

and can effectively work together in

(multi-disciplinary and intercultural)

teams, with the aim of solving business

analytics problems and performing

related tasks.

-Students design a data analytics

project from scratch and implement it

through the perspective of responsible

data use, writing policy reports (on

security, integrity etc).

-Students work within agile teams that

communicate effectively and achieve

deliverables fast and efficiently.

-Final project, group assignments, and

group discussions.

-Working on assignments as teams, group

work, final project.

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Course structure

There will be a total of 4 Lectures, 10 Tutorials in 7 weeks. The lectures are structured in

the first four weeks of the course.

There will be a total of 3 in-class games, 5 take home assignments, 1 final project and 1

final exam. Assignments, final project, and the final exam are parts of the assessment

while the games are just for fun (that ’s the way it’s got to be anyhow)!

The groups for the final project will be formed immediately during the first week, and the

reports of this final project will be submitted last week. All deadlines for the assignments

and the final project are provided in detail in section Lectures and Tutorials.

The final exam will be conducted in the exam week.

The introduction week initiates the participants to individual aspects of the data cycle and

the responsible data use concepts. The tutorials are color-coded with respect to the

concepts they are addressing:

philosophical social ethical legal security and privacy

The color codes do not mean that the relevant weeks are exclusively about these concepts.

They are merely indicators of the focus of the week. For instance, despite the focus of a

week being a legal one, there will (undeniably) be ethical and social questions around the

discussions.

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Literature

The literature for the course is a compilation of articles, research papers, and online

resources. These are provided separately at each tutorial. During the tutorials, students

will also be provided additional material in case necessary. In addition, the first-year

course book Data Science in Business is a strongly recommended source for the whole

course.

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Assessment

The attendance to lectures is strongly recommended. Tutorials are obligatory. Participation

in tutorials is graded via the tutor and the peers (in terms of “active” participation in group

work and assignments).

Students can get a maximum of 100 score for this course as follows:

1. (Active) Participation in tutorials:

In total: 10 points,

2. Assignments (5 times): each assignment has a maximum score of 5.

In total: 20 points,

3. Final Project: The grading is on presentation (10) and the report (10).

In total: 20 points,

4. Final Exam: A list of multiple choice and open-ended questions on covered topics.

In total: 50 points.

To pass this course:

1- Students are required to attend at least 70% percent of the tutorials. Otherwise

they will be required to take a resit (see below).

2- Students are required to submit (and present) a final project

3- Students have to score at least 25 (out of a maximum 50) on the final exam.

4- Students have to score at least 55 (out of a maximum of 100) on the whole

assessed compononets, i.e., participation, assignments, project and exam.

Please read the rules of procedure for examinations (to be found on MySBE Intranet)

carefully. Take into account that it is not allowed to take any of the examination papers

or your scrap paper home.

The examination and the answer key will be published on “Course details” in the Student

Portal in due time after the examination.

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Grading

Validity of partial results

The following course components:

1- Participation

2- Assignments

3- Final Project

which were passed will remain valid in the academic year in which the partial results are

obtained and two (2) more academic years, without prejudice to the competency of the

Board of Examiners to extend this period of validity.

Re-sit

1- Students who fail the participation criteria (70%) can be eligible to take an

additional assignment as resit.

2- Students who fail the final project and final exam can be eligible for a resit

project and resit exam

3- Note that all resit assignments/projects/exams will be doubled in length and in

difficulty for fairness to others.

4- Note that resits should not be taken for granted. Students will have to provide

evidence and reason to the coordinator for eligibility to any of the aforementioned

resit possibilities.

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Fraud and Plagiarism

In order to protect the reputation of the degrees that you – as students – receive, instances

of cheating or plagiarism are treated extremely seriously.

Fraud, including plagiarism, is understood as a student’s act or failure to act that makes it

partially or fully impossible to correctly assess his/her knowledge, insight and skills.

Plagiarism is understood as the presentation of one’s own or other people’s ideas or words

without adequate reference to the source.

Any assignment is an individual piece of work, which means that plagiarism is strictly

forbidden. Equally, the use of mobile phones, communication devices or any other

information carrier (whether the phone or other device is turned on or off, used or not

used, etc. is irrelevant) during an examination is also forbidden.

If the Board of Examiners concludes that anything has occurred in an examination that

makes it partially or fully impossible to correctly assess his/her knowledge, insight and

skills, they may impose a sanction in accordance with SBE’s policy on fraud, including

plagiarism.

More information can be found on EleUM.

Comments and Complaints

If you would like to make a comment on the examination itself or file a complaint about

your examination results, there are procedures in place to do this.

Please refer to MySBE Intranet via the Student Portal for more information.

Comment

Within five days after the examination date you can submit comments on the content and

design of the examination (questions) to the course coordinator. The coordinator will

inform you how you can submit your comments via a remark on the front page of the exam

and/or via the Student Portal > My Courses > Course Details.

Inspection

Within ten working days of the publication of your examination results, you will be able to

have a look at your assessed work.

The date and time of the inspection will be published on the ‘Student Portal > My Courses

> Course Details’ or in the ‘Announcements’.

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Complaint

Students can lodge a complaint during the inspection by using the complaint form.

Appeal

For information regarding an appeal procedure, please read the information on MySBE

Intranet.

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Course schedule

WEEK LECTURE/TUTORIAL TOPIC (summary) DATE

WEEK 1

LECTURE 1 Introduction to Responsible

Data Cycle

TUTORIAL 1 Overview

WEEK 2

LECTURE 2 AI, Philosophy and Ethics

TUTORIAL 2 Moral Machine and Biases

WEEK 3

LECTURE 3 Privacy, Anonymity, GDPR

TUTORIAL 3 Data ownership

WEEK 4

LECTURE 4 Introduction to cybersecurity

TUTORIAL 4 Cryptography and security

WEEK 5

TUTORIAL 5 Security

TUTORIAL 6 Research Integrity

WEEK 6

TUTORIAL 7 Research Integrity

TUTORIAL 8 CSI Data

WEEK 7

TUTORIAL 9 Project Presentations

TUTORIAL 10 Course recap

WEEK 8

EXAM

WEEK

Final Exam

Inspection TBA

Possible

Resits TBA

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Lectures & Tutorials

Week 1: ______________________________________________

Lecture 1: Introduction to Responsible Data Cycle

Tutorial 1: Overview (from design to feedback and disposal) Meeting:

1- General Introduction.

2- Responsible Conduct in Data Management (Office of Research Integrity at US Health

and Human Services and RCR at Northern Illinois University).

3- Students will respond to a hypothetical, high-risk focus group situations through a

Case Study. (Oxfam Case Study)

Literature:

Book:

Chapter 1: Provost, Foster, and Tom Fawcett. Data Science for Business: What you need

to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.", 2013.

Responsible Conduct in Data Management:

https://ori.hhs.gov/education/products/n_illinois_u/datamanagement/dmmain.html

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Week 2: ______________________________________________

Lecture 2: AI, Philosophy and Ethics

Tutorial 2: Creating machines in our own image? Moral Machine, Dilemmas, Ethics and Discrimination in the age of Digitalisation.

Meeting:

1- Game Time 1: (Moral Machine): Discussion of ethical dilemmas which machines will

face instead of us, and how to deal with these.

2- Assignment 1: Students will develop their own Moral Machine scenarios, publish it,

run a survey on it, then they write a report on the findings and a policy

recommendation for a new system.

3- Machines and Gender: Students are going to discuss the evolution of algorithms

and discuss gender-bias in machine learning through the evolution of Google

Translate and introduction of neural networks.

4- Create teams for projects. A select list of projects will be provided; however,

students are also encouraged to suggest their own projects.

Literature:

http://moralmachine.mit.edu

https://ai.google/responsibilities/responsible-ai-practices/

https://arxiv.org/pdf/1903.00780.pdf

http://research.google.com/bigpicture/attacking-discrimination-in-ml/

https://blog.google/products/translate/reducing-gender-bias-google-translate/

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Week 3: ______________________________________________

Lecture 3: Privacy, Anonymity and GDPR

Tutorial 3: Whose data? Preparation: Read the GDPR document,

Meeting:

1- This tutorial will be devoted to create awareness about privacy polic ies, terms and

conditions, cookie polic ies etc, and how GDPR makes a difference.

2- Discussion of different policies.

3- Work on projects: students will create the surveys (e.g., on Qualtrics). Discussion

of privacy and anonymity during the survey design will be addressed.

4- Assignment 2: Anonymizing Data

Literature:

Privacy in the Design of Projects.

https://maastrichtuniversity.bbvms.com/p/default_videoteam/c/2914514.html

UM Employee Privacy regulations

https://www.maastrichtuniversity.nl/support/um-employees/you-and-your-

work/legislation/privacy-regulations

Maastricht University Policy on the Processing of Personal Data:

https://www.maastrichtuniversity.nl/file/um-beleid-verwerking-

persoonsgegevensen290518pdf

GDPR (2016):

https://www.maastrichtuniversity.nl/file/generaldataprotectionregulationgdprpdf

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Week 4: ______________________________________________

Lecture 4: Security Elements of cybersecurity, types of attacks, and prevention mechanisms (anti-malware,

firewall, VPN etc.)

Tutorial 4: Introduction to cryptography algorithms Topics: Ethical Hacking, salt, hash functions and secure algorithms.

Meeting:

1- A brief discussion of Star Wars, Jedi’s and Sith’s, and how to be ethical with hacking

tools.

2- Students will be introduced to ethical hacking and security algorithms.

3- Assignment 3: The students will be testing the IoT servers (distributed to teams

during lecture). They will try to extract preloaded data from the devices. The data

they extract will be used in the next tutorial. Further instructions will follow.

4- Students will discuss how not to secure their server/data through a real life case

(Adobe Hack Scandal).

Literature:

Ethical Hacking:

https://www.hackthissite.org

Introduction to hash functions and cryptography.

https://nakedsecurity.sophos.com/2013/11/20/serious-security-how-to-store-your-

users-passwords-safely/

https://medium.com/@isuruj/introduction-to-hashing-5b4daf343889

https://passwordsgenerator.net/sha256-hash-generator/

Adobe Database Hack Scandal

https://www.theguardian.com/technology/2013/oct/03/adobe-hacking-data-breach-

cyber-attack

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Week 5: ______________________________________________

Tutorial 5: Security continued Meeting:

1- Assignment 4: Students will try to retrieve passwords from the hash database

acquired from the devices in the previous tutorial (in assignment 3), provide a list

of what they found.

2- Students will discuss possible strengthening of the system they hacked.

3- Game Time 2: https://game.cybersaveyourself.nl/

Tutorial 6: Integrity

Meeting:

1- Discussion of Diederik Stapel’s case

2- Students will discuss scientific misconduct prevention.

3- Team Projects will also be discussed, progress will be reported. (5 min each team)

Literature:

New York Times Article about Diederik Stapel’s Fraud

https://www.nytimes.com/2013/04/28/magazine/diederik-stapels-audacious-academic-

fraud.html

Preventing Scientific Misconduct:

https://ajph.aphapublications.org/doi/pdf/10.2105/AJPH.88.1.125

Dutch Code of Ethics in Social and Behavioural Sciences:

https://www.utwente.nl/en/bms/research/forms-and-downloads/code-of-ethics-for-

research-in-the-social-and-behavioural-sciences-dsw.pdf

UK Medical Research Council (Good Research Pracrice and Principles):

https://mrc.ukri.org/publications/browse/good-research-practice-principles-and-

guidelines/

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Week 6: ______________________________________________

Tutorial 7: Integrity continued

Meeting: Case Study

1- Discuss a given real-life case through the perspective of European Code of

Conduct for Research Integrity (from the real research cases).

2- Students will be provided with data for investigation in the next tutorial.

Literature:

All European Academies Code of Conduct for Research Integrity:

https://allea.org/wp-content/uploads/2017/05/ALLEA-European-Code-of-Conduct-for-

Research-Integrity-2017.pdf

Tutorial 8: CSI Data. Meeting:

1- Assignment 5: The students will discuss if the data they are given was

manipulated by use of hash functions. If yes to what extent? (Hint: Hashing files

and images are done quite differently, and for a good reason! Investigate the

differences to understand the assignment better).

2- Feedback on Final Project will be provided.

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Week 7: ______________________________________________

Tutorial 9: Project Presentations and further discussions Meeting:

1- Every team will present their research project and the data cycle involves. Teams

will also discuss and showcase their responsible data use.

2- Each team will go through their project through the Royal Statistical Society

Guidelines, and check for compliance and discuss among teams.

Literature:

Royal Statistical Society Guidelines:

https://www.rss.org.uk/Images/PDF/influencing-change/2019/A-Guide-for-Ethical-Data-

Science-Final-Oct-2019.pdf

Tutorial 10: Course recap and responsible statistics. Meeting:

1- Hand in the report on the project.

2- A preparation for the final exam will be done.

3- Game Time 3: Students will respond to hypothetical questions involving a Case

through the perspective of responsible data use. (Oxfam)

4- Course evaluations.

5- Read D. McCloskey’s Secret Sins of Economics and discuss around the following:

“The confusion and meaninglessness arises from a particular technique in statistical studies, called “statis -

tical significance.” It has become since the cheapening of computation in the 1970s a plague in economics, in psychology, and, most alarmingly, in medical science. Consider the decades-long dispute over the prescribing of routine mammograms to screen for early forms of breast cancer. One school says, Start at age 40. The other says, No, age 50. (And still another, Never routinely. But set that aside.) Why do they differ? The American

nurses’ epidemiological study or the Swedish studies on which the empirical arguments are based are quite large. But there’s a lot of what engineers call “noise” in the data, lots of things going on. So: although starting as early as age 40 does seem to have some effect, the samples are not large enough to be conclusive. By what standard? By the standard called “statistical significance [at the 5%, 1%, 0.1%, or whatever level].” The

medical statisticians will be glad to explain to you (for example, the over-50 school will) that “significance” in this narrow and technical sense of the word tells you how likely it is the result comes just from the noise. A “highly” significant result is one in which the sample is large enough to overwhelm the noise. That is, it’s

unlikely—those 5%, 1%, etc. figures, successively more stringent—you’ll be fooled into thinking there’s an effect when in fact the effect in the real world is zero.”

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Appendices

HERE COMES THE FORMS AND TEMPLATES FOR THE GAMES, ASSIGNMENTS, AND THE

FINAL PROJECT.

A SAMPLE EXAM WILL ALSO BE PROVIDED IN THIS APPENDIX.