Improving PhD skills for work in the fourth industrial revolution: A sociotechnical approach to AI ethics awareness in doctoral research education.
Doctoral researcher Bec Johnson, is exploring how sociotechnical approaches can improve fourth industrial era skills, such as AI Ethics awareness, in PhD candidates. The work aims to help PhD candidates to future-proof their research outputs and add to their researcher skills.
Research Map: STS – Sociotechnical Systems; PhDs – doctoral research education; 4IR – Fourth Industrial Revolution; Phil. Tech – Philosophy of Technology and postphenemonology; Agency – including Actor Network Theory; Power – through knowledge, agency, and experience. Click on the above image to go to a research overview slideshow.
The fourth industrial revolution (4IR) heralds a rapid increase of new technologies into our work and social systems bringing widespread disruption to how we interact with technologies and how that interaction shapes our environment. Increasing deployment of AI technologies into our fourth industrial era requires a wider understanding of the ethical applications of AI to doctoral research, either now or retrospectively. Doctoral researchers should be assisted to improve their understanding of how their work may intersect with AI technologies so they can ethically future-proof their research outcomes and add to their researcher skill sets for 4IR.
On this Page
Very brief introduction to key concepts and research components:
- What is the Fourth Industrial Revolution (4IR)?
- What are Sociotechnical Systems (STS)?
- What are AI Ethics?
- Why focus on doctoral education?
- Research Questions
- Primary Project Site
What is the Fourth Industrial Revolution?
In 2016 Klaus Schwab of the World Economic Forum declared that the world had entered the fourth industrial revolution (commonly abbreviated to 4IR). The first industrial revolution was characterised by the introduction of fossil fuels and the development of machinery to perform tasks that animals and humans used to do. The second was driven by the development of electricity and wired and unwired communications. The third saw the rise of the personal computer, digital communications, and the internet. Now as we enter the fourth we see highly networked technologies that are deeply integrated into our human social systems and even into our physical bodies. Just some of the technologies driving 4IR are artificial intelligence (AI), Internet of Things (IoT), blockchain, gene editing, 3D printing, and smart factories.
As with previous industrial revolutions, the skills most sought after by employers are changing dramatically. Systems thinking skills and learning how to learn capabilities are crucial for adept navigation in a future in which the only certainty is uncertainty. Creativity, collaborative competence, and emotional intelligence have become amongst the most desirable attributes by Industry 4.0 employers. In addition to changing skills for Industry 4.0 is a call for liberal-based capabilities, such as ethics, to have more emphasis in preparing future leaders for 4IR.
What are Sociotechnical Systems?
The ubiquity of new technologies in our social environments, signals substantial changes in our sociotechnical systems. Sociotechnical systems (STS) theory examines how human and technology agents interact with each other and with societal and organisational structures. The field was initially developed in response to social upheavals caused by the mechanisation of coal extraction and was initially developed by members of the Tavistock Institute. As we pass into 4IR with its signature fusion of cyber-physical systems, there has been a renewed interest in the field of STS as a way to navigate the enormous changes we are facing.
Sociotechnical (STS) systems thinking has evolved over the years from society and technology studies and organisational design. The original aim was to understand the impact of a new technology implemented into a social system; often STS was used in IT development (Davis et. al, 2014). More recently STS thinking has been applied to AI ethics, an excellent example of that being Selbst et. al’s work on Fairness and Abstraction in Sociotechnical Systems (2019). Recent work discusses how STS thinking must adapt to AI technologies; “What sets AI apart from traditional technologies is its capacity to autonomously interact with its environment and to adapt itself on the basis of such interactions” (Van de Poel, 2020). Using STS approaches to understanding the ethics of AI enables us to map the relationships between agents (human and non-human) in a system and delve into how the agentic enactment of each drives change in the social, technical systems we live in.
Other theoretical frameworks and lenses that work well with STS and AI ethics and that are being used in this work include:
- Philosophy of Technology and Postphenemonology
- Concepts of Agency and Actor Network Theory frameworks.
- Theories of power especially through knowledge, decision-making, and agency.
What are AI Ethics?
There are substantial ethical consequences of 4IR technologies implemented into our human system. High development of the human qualities of ethical acuity, empathy, self-agency, and global consciousness, are vital to provide leadership into the 4IR we hope for. Technologies are not inherently good or evil. They are tools created and used by humans, as such, deployment of technologies are imbued with human biases and can be used in both helpful and destructive ways. Tools such as AI have no capacity for a sense of morality and values, it is how we create and use these tools that can result in ethical problems in our societies and natural environments.
Whilst the ability to use technology for either good or bad is not at all new, our step change into 4IR heralds massively enhanced abilities for technologies to amplify our biases into our systems, increasing inequalities and undesirable impacts in positive feedback loops. We are at a point in history where it is critical to address these issues before they become further structurally embedded in society.
There are many aspects to ethically responsible AI, just one of them is the bias we imbue these technologies with. It is an inescapable fact that we are all biased. When using powerful 4IR technologies in social contexts we need to identify and understand our biases more clearly for better ethical outcomes. It is impossible to dissociate human produced technologies such as AI from the inherent bias of the creators, the deployers, and the users. In many cases datasets, have our bias baked in from data selection, data tagging, and contextual experiment design. Bias is just one example of ethical problems than can arise when we implement AI, others include transparency, fairness, human dignity, weaponisation, inequities, and automated decision-making (to name just a few). In a recent speech by Australia’s outgoing Chief Scientist, Alan Finkel noted that the most important question he sees in the issue of AI and society is “What kind of society do we want to be?”
Why focus on doctoral education?
How universities should plan for 4IR shifts, has been critically discussed across the literature (i.e. Gleason, 2018; Aoun, 2019; Molla & Cuthbert, 2019). Higher Education institutes are where many doctoral students are incubating new ideas for 4IR, developing research outputs that may be later investigated with AI technologies, and establishing lifelong research qualities. Facilitating the acquisition of 4IR skills is an obvious requirement of our universities and a direction strongly supported by Government. What may be more vital is the development of ethical understanding of 4IR and AI technologies in our future researchers.
As many doctoral graduates go on to leadership roles in research-based jobs, an enhanced understanding of how their work intersects with 4IR technologies, such as AI, in an ethically responsible way is valuable skill. This project addresses that need. It is expected that by facilitating PhD students’ to better future-proof their work to be ethically sustainable if future technologies intersect with their research, they will develop some of the most highly sought 4IR employability skills.
To create robust ethically responsible AI-human systems in research workplaces we should facilitate appropriate skill development in a wide range of doctoral candidates, not just computer science students. Leading to the question, how can we better prepare doctoral students with an understanding of how their research may be explored with ethically responsible AI technologies.
As a parallel initiative, in November 2020 I set up a group on LinkedIn called “PhD students in AI Ethics”. The group has over 80 members from 24 countries representing every continent (except Antarctica) and is growing steadily. Members are all doctoral or postdoctoral researchers studying AI Ethics and hail from a very wide variety of disciplines and fields. View the website here phdAIethics.com
What are critical 4IR and AI ethics skills for most doctoral students in the 2020s?
Anticipated Outcome: A defensible 4IR (AI Ethics) ready graduate qualities list.
- Sub question: Do universities adequately facilitate the skills and ethical AI knowledge for doctoral graduates wishing to be prepared for 4IR? Anticipated Outcome: Clearly identified gaps in research education preparedness.
How can we better prepare doctoral students to future-proof their research for ethically responsible AI technologies and prepare them for work in 4IR?
Anticipated outcome: A better understanding of what methods in research education can help doctoral students future-proof their work and develop a vital 4IR skill.
- Sub question: Are sociotechnical systems (STS) approaches to ethical AI models useful in improving doctoral students 4IR skills? Anticipated Outcome: A developed and tested STS ethical AI model specifically designed for research education.
- Sub question: What educational interventions to improve awareness of ethical AI are most effective in research education? Anticipated Outcome: A tested understanding of paths universities may take to better equip doctoral graduates with ethically aware 4IR skills.
This project is focused on sociotechnical approaches to building AI Ethics awareness in the fourth industrial revolution. The work is situated in the doctoral research education environment.
The anticipated impact of this project is that it will contribute to an understanding of how universities can better equip graduates of research degrees to be successful and ethical leaders in the fourth industrial revolution. The end goal is to develop a tested, STS approach for doctoral candidates to use to assess their work in the context of ethical AI. Additionally, to compare other current educational approaches to identify which ones may be most suitable for disciplinary uptake.
Primary project site
Bec Johnson is a doctoral student at the University of Sydney. Bec is supported by a strong team including her research supervisor, Prof. Ross Coleman, Director of Graduate Research; auxiliary supervisor Prof. Dean Rickles from the School of History of Philosophy of Science, and industry supervisor, Dr Lucy Cameron from the Commonwealth Scientific and Industrial Research Organisation (CSIRO). More details on the Team page.
The work is being initially conducted at the University of Sydney until travel opens up again. Other universities are to be selected for additional data collection in New Zealand, the UK, Canada, and possibly the USA.
Preparing for uncertainty
The problems of how to prepare for the unprecedented changes of 4IR affect every everyone as well as the planet itself. Universities have an important role to play in addressing this challenge. In a world in which the only certainty is uncertainty, and where our poor ethical choices can be magnified at vast scales of social impact, our future PhD graduates should provide a valuable contribution to navigating the current revolution.