Check out Dr. Aaron Chen’s Q&A on the new course below.
Could you tell us about the course and its content?
The title of the course is “Managing Artificial Intelligence”. As you can see, this is a human-centric approach to AI. I suggested this course because I have seen many courses at our school and others around the world that focus on the technical capabilities of Big Data and AI. They help students understand the potential of this technology, rather than giving a practical administrative perspective or guidelines on how to manage AI.
The course offers 10 lectures covering both the technicalities and management of AI, as well as social and ethical considerations. Balanced to give students different perspectives on AI.
Nine seminars have been added to this course so that students can get exposed to and engage with real-world management practices of AI. Among them, there are three case study sessions covering product development, human participation, business models, and global strategies for AI applications in various contexts such as social media, healthcare, and telecommunications. So this is an interesting line-up of educational cases that show that AI is real and that managing AI is a priority for many organizations today, not something we imagine and predict the future. is.
There is also an interesting discussion about generative AI, a modern AI that can automatically generate content for people to use. We’ve seen a number of related applications (such as ChatGPT) these days. In one of the seminars, students were assigned to five roles: employer, university, teacher, student, and AI vendor to discuss the role of this technology in higher education. We wanted to see what problems would arise in this ecosystem, and we had some interesting conversations with students putting themselves in different roles. The debate has also had regulatory implications for how AI should be governed in the context of higher education.
The most exciting task for students is a team project on AI management. Teams of students incorporate what they learned in the lectures into their AI projects, develop their present and advance their projects in four seminars. Most teams develop startups around AI solutions, starting with a pressing business or social challenge.
Some students considered whether journalism and public relations could be fully automated, but ultimately decided not to. One team is looking at how to use predictive analytics to help college students and teachers book spaces and schedule appointments. As you can see, all of these projects are innovative and can actually be brought to market, so the students are very excited about it.
Overall, we can see that the students love this course. Their coursework has gone hand in hand with the rapid changes in his AI field, especially in the past few months since his ChatGPT began, with many tech companies vying to drive innovation day by day. While the field is fascinating, it also presents course design challenges.
Is this course designed for students who work for companies coming up with AI projects?
It is aimed at students who want to work in any field that currently employs this technology. IT developers and data scientists are needed to create AI and data-driven solutions, but the diffusion of such innovations requires more skilled professionals, both technical and management savvy. It is important to note that
These professionals are often referred to as business analysts and managers at various levels within the organization who can lead the digital transformation, often acting as intermediaries connecting supply and demand for AI and analytics solutions. . Statistics from the McKinsey Global Institute show that managers and analysts who can leverage big data and AI know-how for effective decision-making outperform data scientists and machine learning (ML) engineers who are primarily specialized in their field. has been shown to be 10 times less than programming.
To meet the demand for management talent in AI, my courses focus not on teaching students how to design technology, but on how to manage technology and use AI to lead digital transformation. .
It is also important to mention the program that hosts this course, Management Information Systems and Digital Innovation (MISDI). This is the flagship master’s program of the Information Systems and Innovation Group (ISIG) in the School of Management (DoM). The ISIG faculty’s expertise and the courses offered at his MISDI are all about connecting technology know-how with business and management know-how, giving students an edge and knowledge through this connection.
This is also a course that meets the needs of the students. Over the past few years, students at MISDI and other DoM programs have taken a keen interest in AI issues, and many have used AI management topics in their courses and papers. However, there was no course specifically for it.
Other faculties at LSE like Statistics have some very good AI and ML courses, mostly from a statistician or computer scientist perspective. From 2021, the LSE100 course ‘How to control AI’, very well designed from a social science point of view, is available, but only for undergraduates.
Integrate multiple perspectives of AI, focus on management considerations, and comprehensively critique the role of automation and augmentation of AI for individuals to further meet the needs of master’s degree students studying AI management. We have launched this new course at MISDI for dealing with , organizations, and society as a whole.
Is this course designed for people interested in the business side of AI?
I think so too, but I would like to emphasize that it is a more balanced course and also attracts students with interests beyond business. Another important point to mention is that the course sits within a polarized public debate with diverse views on AI.
We have seen two camps. One camp is held by those concerned about AI and the social and ethical implications of replacing humans in the workplace. The other is the utopian view of AI by humans who only claim its technical ability to augment human capabilities. The latter clearly have a more positive view of AI, but also of humans themselves, especially when AI reinforces inequalities among those who lack the knowledge and skills to manage it. May downplay existential threats.
These two camps are now very large, but strictly segregated. They often use different linguistic systems to argue, so it feels like they are not talking to each other in a very productive way. I believe that there is a great need for effective communication between these camps in modern society, and that people need to understand the roots of these two camps before developing beliefs and behaviors about AI. I think it is necessary to know the logic and assumptions in Especially for current and future leaders in the private and public sectors. They need a deep understanding of AI’s possibilities, possibilities, and dangers. We also need to take a sober view of the AI expectations and hype claimed by both camps.
I hope that this course will plant seeds deep in the hearts of the students. So when they go into their professional careers as business leaders and social planners, they understand what AI is and, more importantly, accept the responsibility of managing it for a better future for humanity. I owe. After all, we should be able to not only create powerful AI, but also create our own humanity, and achieve co-prosperity with AI. This is the basic idea of this course.
What makes this course unique and different?
Let’s talk about similar courses and the difference my course brings to AI management education.
For the past 5 years I have attended the largest IT education workshops in my field (Information Systems) almost every year. At workshops, teachers from most universities in the US and Europe present courses on big data and data analytics, but I haven’t seen many specialized courses on AI.
Of course, in the computer science community, there are many popular courses on machine learning and data science that are rarely mentioned as AI courses. It’s important to note that the concept of AI is socio-technical, not just technical. We need to study and teach the nature of AI and its implications by examining its technical characteristics and its social context. As far as I know, few courses are as balanced as this one.
One reason is that most courses focus on the technicalities of AI. This is obvious. STEM jobs pay much better than many other jobs. Preparing students for such jobs can help increase the popularity of universities, further promoting the offering of technical AI or data science courses.
A leader in the social sciences approach to higher education, LSE has strengths in developing leaders who can think about and navigate societal change, especially the current transformational changes driven by AI. Therefore, we offer this new LSE course to position the debate about AI in academic and public debates and to approach AI education in a more comprehensive and critical way. It begins with the history of AI, discusses the role of data in making AI, and unlocks the black boxes of algorithms and related issues such as opacity, bias, and interpretability.
Students are then presented with a socio-technical analysis of AI management at various levels. At an individual level, assess the human role in the loop and how and when human decisions need to be made in AI design and use. At the organizational level, we analyze AI-powered business models, operations, innovation and governance. At the societal level, we discuss ethical concerns and regulatory efforts to manage AI forever. As you can see, this approach to AI starts students thinking and asking their own important questions about managing AI in the digital economy.
What made you personally think this course was really necessary?
To answer this question, I would like to start with my educational background and then my readings and thoughts on AI over the past decade.
Since my college education 15 years ago, I have been in the same field of Management Information Systems, but my initial training was more technical, specifically computer science. I then moved to a master’s program, which gave me a better understanding of technology after exposing me to a more behavioral perspective on how people interact with it. After that, my doctoral training in economic analysis of information technology enabled me to undertake research into the larger role of technology in business and society.
Now I am a researcher and teacher in information systems and innovation, and LSE really broadened my horizons for a social scientific approach to technology. In the course of my education, AI has been with me for many years, although more often in the form of algorithms and machine learning techniques.
Until the AI field boomed, especially when deep learning and generative models were developed and used to create powerful applications such as Deep Fakes and ChatGPT, AI didn’t get much attention for me and others. I think it’s the same for people in These days, in contrast to the limited artificial intelligence of the past few decades (AI can only serve a small number of pre-specified objectives and can automate tasks just like regular software), It is said that the era of artificial general intelligence has arrived.
Over time, we realized that AI has great potential to change human life in a positive way. At the same time, people are concerned that the apocalyptic claim that “machines are the end of mankind” has reached an all-time high. I think it’s time to seriously think and research how to manage AI.
Teaching AI management is an opportunity for me, as a researcher, to explore with my students the socio-technical nature of AI and its implications, and how we can be more responsible in the design and implementation of AI. I am glad that my students were excited about this course, took this journey seriously and benefited from it.
