Hosted jointly by Manchester Metropolitan University and the Royal Statistical Society, and is part of the ESRC Festival of Social Science 2019

This event aims to discuss and disseminate research around how human based analytics and AI can be developed to support business and social decision-making.

The existing techniques of these methods are mostly based on scientific aspects. However, social science and business involve human beings, cultures, and societies. There is a challenge to integrate human feelings, social responsibilities, and ethics into such advanced technologies. Therefore, at this event we aim to explore these challenges and suggest possible solutions.

Speakers

Dr Xin Shi
Reader in Applied Statistics
Manchester Metropolian University

Dr Manuel Lopez-Ibanez
Programme Director, MSc Business Analytics
Alliance Manchester Business School (University of Manchester)

Professor Jon Bannister
Director, Crime and Well-Being Big Data Centre
Manchester Metropolitan University

Dr Sabrina Thornton
Senior Lecturer in Marketing
University of Huddersfield

Schedule

• 1.00pm – registration and refreshments
• 1.30pm – Introduction to the event and his research, Dr Xin Shi (Reader in Applied Statistics, Manchester Met)
• 2.00pm – Professor Jon Bannister (Director, Crime and Well-Being Big Data Centre, Manchester Met)
• 2.30pm – Dr Sabrina Thornton (Senior Lecturer in Marketing, University of Huddersfield)
• 3.00pm – Dr Manuel Lopez-Ibanez (Programme Director, MSc Business Analytics, Alliance Manchester Business School, University of Manchester)
• 3.30pm – Panel session with audience interaction chaired by Professor Scarf (Professor of Applied Statistics, Salford University)
• 4.00pm – Close

Abstracts

Dr Xin Shi
‘Using Big Data for Effective Health Management: Early diagnosis, intervention and prevention’

Ensuring healthy lives and promoting wellbeing for everyone is key to building prosperous and sustainable societies. Early diagnosis and intervention can be crucial in prolonging and saving lives, which is why multi-disciplinary researchers are working together to help develop the most effective health intelligence insights.

Health intelligence refers to the specific application of AI and data science methods and tools to provide accurate, efficient, and productive insights into healthcare and medicine. Our study aims to develop a systematic approach to integrating statistical and artificial intelligent health big data modelling into optimal health management decision-making with mobile application. By developing these statistical modelling methods around early diagnosis, prevention and intervention, we are developing a multi stage delay-time model to investigate risk factors and predict heath status at an earlier stage of disease/illness progression using linked clinical and non-clinical data.

In this talk, we will showcase our recent research findings and discuss the challenges for future research with the audience.

Dr Manuel Lopez-Ibanez
‘Automatic Machine Learning and Optimisation: The human-in-the-loop’

When applying machine learning and optimisation methods to real-world problems there is often the need to re-design and fine-tune these methods to particular real-world scenarios. New methods in AutoML and automatic algorithm configuration have proven very successful in research, and they are increasingly being adopted by businesses. Yet, there are significant challenges ahead, particularly regarding the role of humans guiding and interacting with these automatic systems. Although we posses some understanding of the potential cognitive biases involved in the process, there is little work on how such automatic methods may take into account those cognitive biases and fine-tune ML and optimisation algorithms to deal with them.

Professor Jon Bannister
‘Big Data and the Social Sciences: If the answer to life, the universe and everything is 42, what is the question?’

In 2010, the then Google Chairperson Eric Schmidt observed: ‘There were five exabytes of information created by the entire world between the dawn of civilisation and 2003. Now that same amount is created every two days’. Similarly, and for the last 50 years, the average speed of computation has been doubling every 18 months. How can the social sciences develop appropriate responses to keep pace with, to take advantage of these developments?

Disappointingly, much of the emergent discourse on Big Data and AI in the social sciences is shaped by the uncertainty that surrounds substantive technological change, polarised around a restrictive binary centred on utopian and dystopian visions of its future consequence. Thus, Big Data and AI have been heralded as possessing the potential to redress maladies, to drive forward the functioning of societies and improve the wellbeing of their citizenry. They have also been decried as ushering in an era of intense surveillance and of widening inequalities.

Big Data and AI pose challenges to the authority and value of the social sciences. They have ushered in new actors e.g. data scientists, and new forms of empiricism that threaten the death of theory with the speed of the Big Data revolution seemingly outstripping the capacity of the social sciences to offer critical reflection. On the other hand, Big Data opens new prospects for the social sciences, of illuminating the daily rhythms, activities and functioning of society.

Big Data and AI can facilitate exploration of areas of analytical interest already known, but previously unreachable, enabling theoretical as well as empirical advance. In this light, Big Data and AI, particularly when deployed in tandem with traditional techniques and data, hold the potential to initiate substantive progress in the social sciences. This paper sets out to illuminate and illustrate (some of) the challenges and advantages, posed and offered to the social sciences by Big Data and AI.

Dr Sabrina Thornton
‘B2B Analytics in the Airline Market: Harnessing the power of consumer big data’

This research utilizes market-level data to explore the relative performance of individual companies amongst defined competitors. We show the potential of using consumer clickstream data, which is an important type of big data, to create a new set of B2B analytical frameworks. In the markets where complex interactions between competitors, search intermediaries and consumers create a network, B2B relationships can be inferred from consumer search patterns, and can then be modelled to gauge the online performance of business organizations.

A commercial dataset from ComScore’s US panel of one million users is used to illustrate a new approach to measure and evaluate the online performance of competitors in the US airline market. This research makes theoretical and empirical contributions to the debate on the use of big data for B2B market analytics.