Computer Science

COM2012 - Data Science in Society (2019)

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MODULE TITLEData Science in Society CREDIT VALUE15
MODULE CODECOM2012 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content

This module will focus on the societal context for data science, machine learning and artificial intelligence. An increasing number of social, governmental and commercial processes now take place in online or digital environments, making it important to consider the ways in which data is used to make decisions and how the application of computational methods to engineer social processes can be managed in ways that are ethical, transparent and socially acceptable. This module will teach you the core knowledge around data ethics, privacy, fairness and data governance. You will be encouraged to form your own opinions on how digital tools can best be developed to deliver benefits and avoid harm. Seminar discussions and ethical case studies will be used to highlight and explore different social issues around data science.

Pre-requisites: None

Co-requisites: None

Suitable for non-specialists and interdisciplinary pathways

AIMS - intentions of the module

This module aims to:

• Explore how data science and analytics can utilised in ways that are responsive to broader social concerns in the UK and internationally;

• Discuss data ethics and societal issues around data collection, processing, dissemination and use in decision-making;

• Enhance knowledge of data protection policies at local, national and international level;

• Provide skills in engaging data providers, users and customers to ensure compliance with regulations and legal systems;

• Ensure awareness of social context of data science with attention to potential ethical concerns including privacy, fairness and bias;

• Develop skills in assessing data science services (and particularly Big Data) for the general public as well as specific stakeholders (government, local authorities, competitors in industry, lobby groups).

The module will draw on recent scholarship in data studies and case studies based on a variety of contexts. The module provides training in ethical and societal implications of data collection, processing and management strategies.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module, you should be able to:

Module Specific Skills and Knowledge:

1 Understand key terms and concepts in data ethics, data governance and information management and be able to relate these to a typical business situation;

2 Understand how to manage data collection, processing, storage and dissemination within current legal frameworks;

3 Understand some ethical issues relevant to data science and how ethical concerns can be managed;

Discipline Specific Skills and Knowledge:

4 Apply theoretical arguments, frameworks and concepts from data studies to the use of data science in an organisational context;

5 Demonstrate an integrated and holistic perspective when generating data science solutions;

6 Develop knowledge of professional responsibility and ethics around data science;

Personal and Key Transferable/ Employment Skills and Knowledge:

7 Demonstrate cognitive skills of critical and reflective thinking;

8 Demonstrate effective critical reading and discussion skills.

SYLLABUS PLAN - summary of the structure and academic content of the module

The module will cover:

• The role of data science in society;

• Basic concepts of data ethics and data governance;

• Applying current concepts in data ethics and data governance to real examples;

• Understanding the legal frameworks around use and analysis of data in social and business contexts;

• Intellectual Property, legal regimes and regulatory structures around the ownership and maintenance of databases;

• Data dissemination, curation and Open Data, the limits of automation, and the challenges of making data accessible and re-usable;

• Data protection and legal frameworks for data collection, storage and analysis.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 33.00 Guided Independent Study 117.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching Activities 33 Lectures and seminar discussions
Guided Independent Study 117 Reading; seminar preparation; coursework

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Seminar Discussion e.g. 2 hours group seminar per week All Verbal

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 40 Written Exams 60 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Essay 40 2,000 words All Written
Exam 60 2 hours All Written

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Essay Essay All August reassessment period
Exam Exam All August reassessment period

 

RE-ASSESSMENT NOTES

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you will be required to re-take some, or all, parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 40%.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic Reading:

ELE: http://vle.exeter.ac.uk/

Schutt, R. and O’Neill, C., Doing Data Science: Straight Talk from the Frontline.
Boyd, D. and Crawford, K., Six provocations for Big Data. A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society.
Kitchin, R., The Data Revolution.
Leonelli, S., Data-Centric Biology: A Philosophical Study.

Other Resources:

Science International (2015). Big Data in an Open Data World.

Hey, Tony, et al. 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Publishing.

Hine, Christine. 2006. ‘‘Databases as Scientific Instruments and Their Role in the Ordering of Scientific Work.’’ Social Studies of Science 36 (2): 269-98.

Dove, Edward S., Yann Joly, Anne-Marie Tassé, Paul Burton, Rex Chisholm, Isabel Fortier, Pat Goodwin, et al. 2015. “Genomic Cloud Computing: Legal and Ethical Points to Consider.” European Journal of Human Genetics 23 (10): 1271–78. doi:10.1038/ejhg.2014.196.

Dove, Edward S., David Townend, Eric M. Meslin, Martin Bobrow, Katherine Littler, Dianne Nicol, Jantina de Vries, et al. 2016. “Ethics Review for nternational Data-Intensive Research.” Science 351 (6280): 1399–1400. doi:10.1126/science.aad5269.

Burton, Paul R., Madeleine J. Murtagh, Andy Boyd, James B. Williams, Edward S. Dove, Susan E. Wallace, Anne-Marie Tassé, et al. 2015. “Data Safe Havens in Health Research and Healthcare.” Bioinformatics 31 (20): 3241–48. doi:10.1093/bioinformatics/btv279.

Boulton, Geoffrey, Brian Campbell, Brian Collins, Peter Elias, Wendy Hall, Graeme Laurie, Onora O’Neill, et al. 2012. “Science as an Open Enterprise.” 02/12. London: The Royal Society Science Policy Centre.

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Schutt, R. and O’Neill, C. Doing Data Science: Straight Talk from the Frontline O'Reilly 2014 [Library]
Set Boyd, D. and Crawford, K. Six Provocations for Big Data. A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society Electronic Elsevier 2011 [Library]
Set Kitchin, R. The Data Revolution Sage 2013 [Library]
Set Leonelli, S. Data-Centric Biology: A Philosophical Study Chapter 2 Chicago University Press 2016 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 6 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Friday 12 April 2019 LAST REVISION DATE Monday 12 August 2019
KEY WORDS SEARCH Data Science; Data Ethics; Data Governance; Data Protection