A presentation given at the BCS AI2020 conference 8 Decembner 2020

1. Professor of Business Analytics, University of New South Wales
Professor of Business Analytics, Birkbeck University
Emeritus Professor of Systems Thinking, University of Hull
AI2020: 8 December 2020

2. Chollet, F., (2018). Deep Learning with R. Manning Publications.
Artificial
intelligence
Deep
learning
Machine
learning
Classical
programming
Rules
Data
Answers
Machine
learning
Data
Answers
Rules
Deep learning
Chui, M., and McCarthy, B., (n.d.). An Executive’s Guide to AI.

3. Operational Research
(OR)
“The discipline of
applying advanced
analytical
methods to help make
better decisions”
(INFORMS and ORS)
Data Science
“Data science is a
multidisciplinary approach to
extracting actionable
insights from the large and
ever-increasing volumes of
data collected and created
by today’s organizations ...
presenting the results to
reveal patterns and enable
stakeholders to draw
informed conclusions.” (IBM)
Business Analytics
is concerned with
“the extensive use of data,
statistical and quantitative
analysis, explanatory and
predictive models, and fact-
based management to drive
decisions and actions”
(Davenport and Harris,
2007)
Hindle, G., Kunc, M., Mortensen, M., Oztekin, A., and Vidgen, R., (2020). Business Analytics: Defining the
field and identifying a research agenda. European Journal of Operational Research, 281(3): 483-490.

5. https://www.altexsoft.com/blog/datascience/how-to-
structure-data-science-team-key-models-and-roles/
http://www.operational-
research.gov.uk/recruitment/competencies/

6. HARD approaches SOFT approaches
Problem
definition
Seen as
straightforward,
unitary
Seen as
problematic,
pluralistic
Model
A representation of
the real world
A way of generating
debate and insight
about the real
world
The organisation Taken for granted
Has to be
negotiated
Outcomes
Product or
recommendation
Progress through
learning
https://www.lancaster.ac.uk/users/incism/back2.html
“Over the last 40 years,
methodologies have been developed to
deal with “wicked problems” or
“messes” that are beyond the reach of
the traditional, mathematical
modeling methods of operations
research (O.R.). These methodologies
are structured and rigorous but non-
mathematical. Examples include Soft
Systems Methodology (SSM), cognitive
mapping/SODA and the Strategic Choice
Approach (SCA). Collectively they are
known as Soft O.R., Soft Systems or
Problem Structuring Methods (PSMs),
and they have been accepted as an
important part of O.R. almost
everywhere with one notable exception
the United States.”
John Mingers (2009)

7. 1. Domain knowledge: the hard OR discipline
has a wealth of knowledge and experience in
traditional OR applications in which AI can be
embedded
2. Methdology for business: OR can bring a
business focus to building AI applications in
organisations – in particular, soft OR can help
in in structuring and acting in messy problem
situations
3. Ethical: the OR Society is exploring an
ethical perspective on the role of AI in
organisations and its impact on citizens and
society

8. Scheduling: of aircrews and the fleet for airlines, of vehicles in supply chains, of orders in a factory and of
operating theatres in a hospital.
Facility planning: computer simulations of airports for the rapid and safe processing of travellers, improving
appointments systems for medical practice.
Forecasting: identifying possible future developments in telecommunications, deciding how much capacity is
needed in a holiday business.
Yield management: setting the prices of airline seats and hotel rooms to reflect changing demand and the risk of
no shows.
Credit scoring: deciding which customers offer the best prospects for credit.
Marketing: evaluating the value of sale promotions, developing customer profiles and computing the life-time
value of a customer.
Defence and peace keeping: finding ways to deploy troops rapidly.

9. Vidgen, R., Kirshner, S., and Tan, F., (2019).
Business Analytics: a management approach. Red
Globe Press.
https://businessanalyticsmanagement.com
Vidgen, R., Shaw, S. and Grant, D. (2017).
Management challenges in creating value from
business analytics. European Journal of Operational
Research. 261(2): 626-639.

10. Vidgen,R.,Kirshner,S.,andTan,F.,(2019).Business
Analytics:amanagementapproach.RedGlobePress.
https://businessanalyticsmanagement.com

11. 11

12. https://ethicalai.ai

15.  The Information Commissioners Office (ICO) defines automated decision-making:

16. Algorithmic
pollution
“Algorithmic pollution is a form of social
pollution which denotes the unjustified,
unfair, discriminatory, and other harmful
effects of algorithmic decision-making for
individuals, their families, groups of people,
communities, organizations, sections of the
population, and society at large.”
Cecez-Kecmanovic, D., Marjanovic, O., and Vidgen, R., (2018).
Algorithmic Pollution: Understanding and Responding to
Negative Consequences of Algorithmic Decision-making.
Proceedings of the IFIP 8.2 Working Conference, San
Francisco, USA, December 11-12.

17. Vidgen, R., Hindle, G., and Randolph, I., (2020). Exploring the ethical implications of business
analytics with a business ethics canvas. European Journal of Operational Research, 281(3): 491-501.

18. Citizens
Informed
Understandable
Contestable
Organisations
Fair
Transparent
Accountable
Safe
Society and the environment
Rights (human and non-human)
Social justice
Vidgen (2020). Ethical AI
and algorithmic decision-
making Framework. Work in
progress with ethicalAI and
the OR Society.

19. Summary:
what OR
can do for
AI
 Bring a wealth of knowledge and
experience of traditional OR applications
in which AI development is embedded
 Provide methods to guide the
development of AI applications that create
business value
 Constitute an ethical framework and
ethical practices for those working in the
development, management, and
governance of AI applications
Opportunities for the Operational Research Society (ORS) and the British Computer
Society (BCS) to work together?

20. Richard Vidgen (r.vidgen@unsw.edu.au)