Module description
What is the module about?
Artificial intelligence (AI) designates a set of technologies increasingly used in professional activities including financial investments, credit ratings, medical diagnostics, traffic control, undersea exploration, and many more. It is argued that AI is about to lead a fourth industrial revolution. It changes work processes and the professions, together with how organisations are managed. But what exactly is AI, how does it work, and what are the impacts on the professions? How is it received by users in professional contexts and with what consequences? What is the impact with regard to expertise—are experts to be replaced by AI in the foreseeable future? Does AI have ethical implications in organisational contexts—if yes, what are these? This module examines the impact of AI on work processes in organisational contexts, on the professions and on expertise. It starts with defining what AI is, examining how it exactly works, how it is used in professional contexts, and how its uses should be regulated. The module uses a number of case studies from across finance, health organisations, or urban planning to investigate how AI changes the professions, expertise, and organisations.
Who should do this module?
Anyone interested in the impact of technology and software engineering on professions, expertise, and organisations, and in understanding the consequences of AI on organised work.
Provisional Lecture Outline
This will be adapted according to relevant further evolutions in the field
Lecture 1. Introduction. What are AI and ML and how do they work?
In this introductory lecture, we clarify the meanings of the terms artificial intelligence (AI) and machine learning (ML), their history, and how they work. We review concepts such as algorithms, supervised and unsupervised learning, deep learning, structured and unstructured data, centralised databases, cloud computing, and edge computing, ledgers and blockchain. This introductory discussion is intended to define key terms and provide a deeper understanding of the matter at hand, in ways that are accessible to business and management students. #
Lecture 2. The Organisation of AI and Big Data
In our second lecture, we investigate the organisation and applications of AI and ML in a variety of businesses and domains. We start by analysing capital flows and investments into AI and ML by sector and the dynamics of these investments over the past years. We also discuss the costs and benefits of operating AI and ML across a variety of applications and their adoption rates by business and industrial sectors. We also examine data requirements for AI/ ML, the costs associated with this data, and ESG aspects of data and AI/ML applications. We identify the most successful domains of AI and ML by sector and discuss future trends.
Lecture 3. AI, Professions, and Expertise. Does AI change Human Expertise?
We focus in this lecture on the impact of AI and ML on professions and expertise. How do professions change under the impact of AI? Will expertise be automated and replaced by expert systems? Or does AI enhance existing domains of expertise, and if yes, how? Does AI make it easier to, or more difficult acquire skills? Does AI impact wellbeing in the workplace? We discuss how AI applications impact expertise in a variety of domains, such as financial services, healthcare, marketing, or law. We discuss, based on data, whether AI improves productivity of various professions and whether organisations become more efficient or not. We also analyse whether AI/ML changes the professional composition of organisations and businesses, and whether we can identify any future trends in this respect.
Lecture 4. Digital Organisations and the Metaverse for Work
How does a digital, distributed organisation look like? We have seen over the past five years or so a new organisational model emerging, away from centralisation and away from face-to-face interactions as the dominant mode or working. Now we have decentralised autonomous organisations, or DAOs (admittedly still a minority, but hailed as a model for the future) and we have the Metaverse as being hailed as a model of performing expert work in the future. Big Tech (Microsoft, Meta) have invested significant sums in the Metaverse, and the claims of its revolutionary character extend well into the organisational realm (especially with Microsoft). We examine in this lecture new organisational forms emerging, the extent to which they rely on AI/ ML, and how they are bound to impact work.
Lecture 5. AI in Healthcare
We examine how AI is changing the healthcare professions and where the most progress has been made, from image analysis to drug design, clinical analysis support, and healthcare administration. We investigate the forays made by BigTech in personal health and their consequences. We discuss how AI/ML impacts skills and the medical professions, as well as healthcare organisations. We also investigate in this tutorial challenges and risks related to AI in healthcare, including here data protection and fairness in data use.
Lecture 6. AI and Financial Services
The financial services industry has been one of the most impacted by AI/ ML, in a variety of ways, ranging from financial chatbots to trading algorithms. The scale and breadth of impact requires that regulatory agencies have to up their game in order to ensure consumer protection and a fair functioning of financial services. In this week, we have a guest lecturer from the Financial Conduct Authority, Ravi Bhalla, who will talk about the impact of AI/ ML on the financial services in the UK and the steps undertaken by the FCA to ensure consumer protection.
Lecture 7. AI, Consumer Services, and the Entertainment Industries
In this lecture, we focus on how AI/ ML changes consumer services and the entertainment industries. This is broad and deep field where AI has left its imprint. A few aspects to be mentioned here are recommendation engines, price optimisation engines, or gaming—digital avatars, anyone? We have invited Jody Ford, the CEO of Trainline, as a guest speaker to talk to us about how AI changes consumer services and transport.
Lecture 8. AI, Smart Cities, and Public Services
In week 8, we turn to examining how AI changes public services and the urban landscape, including here public sector organisations and services. A lot has been written about smart cities, a concept that requires considerable investments in AI. The latter is already at work in a series of public settings, such as airports. We examine here among others issues such as unmanned vehicles and smart motorways, but also a spectrum of ways in which public services and public life are impacted by AI and compare the UK urban landscape with other cities.
Lecture 9. How To Achieve Sustainable, Human-centred AI?
Lecture 9. How To Achieve Sustainable, Human-centred AI? As we have discussed in the introductory lectures, AI/ ML requires significant investments of capital and is energy intensive. Its impact on professions and expertise is non-negligible across several economic sectors? Can a sustainable AI be achieved? Can it be human-centred? In this lecture, we focus on a series of issues related to AI/ ML such as environmental impact, explainability, data bias, and data manipulation, among others. We discuss how future growth can address these issues. We debate ways of achieving a sustainable AI/ML and ways to make it human-centred by including a plurality of stakeholder perspective in AI/ ML adoption.
Lecture 10. Ethics and the Governance of AI and ML
The final lecture examines the ethics and governance of AI/ ML, connecting directly into the sustainability issues discussed during the previous week. We investigate and debate issues related to the ways on ensuring an ethical AI in organisations, as well as to how AI/ ML should be governed. A special focus is provided by the governance of the Metaverse, something that has come recently to the attention of legal scholars, computer scientists, and social science scholars.
Assessment details
80% Individual Coursework
20% Group Coursework
Teaching pattern
Weekly lectures
Weekly tutorials
Suggested reading list
Key text or background reading
A good start is Martin Burgess’s Artificial Intelligence. How Machine Learning Will Shape the Next Decade. New York 2021: Random House.
Taulli, T., & Oni, M. (2019). Artificial intelligence basics (pp. 62-63). Berkeley: Apress.