Social scientific doctoral student focused on transparency and explainability in a medical AI project - Hiring in process/Finished, not possible to apply

The department of technology and society employs around 80 senior lecturers, PhD students and administrative staff. The department is currently developing a multidisciplinary research environment around the adoption of artificial intelligence (AI) in society at large, including ethical, social and legal issues. Current PhD position is one of five planned within the five-year research project Artificially Intelligent use of Registers (AIR Lund), primarily funded by the Swedish Research Council. This means that while the PhD candidate is located at the department for technology and society and appointed to the research subject with the same name, the project will be highly collaborative within the social, medical, technical and legal sciences.

Research subject: Technology and Society

Technology and Society is a third-cycle subject that encompasses multidisciplinary and interdisciplinary studies of technology’s role, interplay and importance in different sectors of society.

Project description
The highly interdisciplinary project will in its entirety critically assess the added value of machine learning compared to standard statistical approaches for predictions and decision aids for prevention, diagnosis and prognosis, focussing on cardiometabolic diseases. AIR Lund represents a unique research environment at five different faculties at Lund University that collaborate closely with applied intelligent systems research at Halmstad University. We expect to bring substantial interdisciplinary novelty into register-based research, and at the same time to open up the Swedish register infrastructure to AI research.

Specifically, the PhD candidate in Technology and Society will be part in a work package (WP) addressing ethical, legal, societal and methodological challenges directly related to the proposed use of machine learning (ML) on rich register data.

One particularly challenging task for the interdisciplinary team behind this WP will be to evaluate and refine two major approaches to explainability used within ML today. While the used methods seem promising from a mathematical perspective, it is far from that certain that they can deliver explanations that make sense in the applied settings, and thus contribute to transparency. Nor is it certain that these approaches are safe-guarded from bias and stigmatization with respect to, e.g., gender, country of origin or socioeconomic conditions that may arise from registers. A critical assessment of these issues will thus be a central task for this WP and this PhD candidate.

In sum, we are looking for a doctoral student to study issues of explainable AI and transparency in a medical registry-based health project, primarily from a social scientific perspective.

Work duties
The main duties of doctoral students are to devote themselves to their research studies which includes participating in mentioned research project and third cycle courses. The work duties could also include teaching and other departmental duties (no more than 20%).

Admission requirements
A person meets the general admission requirements for third-cycle courses and study programmes if the applicant:

• has been awarded a second-cycle qualification, or
• has satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second cycle, or
• has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

A person meets the specific admission requirements for third cycle studies in Technology and Society if the applicant has:

• at least 30 credits awarded in the second cycle relevant to the subject area.

Additional requirements:

• Very good oral and written proficiency in English.

Assessment criteria
Selection for third-cycle studies is based on the student’s potential to profit from such studies, including the abilities necessary for successfully completing the third cycle programme. The assessment of potential is made primarily on the basis of academic results from the first and second cycle. Special attention is paid to the following:

1. Knowledge and skills relevant to the thesis project and the subject of study.
2. An assessment of ability to work independently and to formulate and tackle research problems. The assessment could be made on the basis of the student’s degree project and a discussion of this at a possible interview.
3. Written and oral communication skills, not least in making research-based knowledge available to a wider audience, public debate, legislators or in industrial collaborations.
4. Other experience relevant to the third-cycle studies, e.g. professional experience.

Other assessment criteria:

• While we mainly look for someone with a background in the social sciences, in a broad sense, we would particularly favour candidates with an understanding and knowledge of the technologies and methods within the AI-domain in general, and in health in particular.
• An understanding of basic regulatory provisions for data collection and handling, such as the GDPR and the Swedish Patientdatalagen, is a merit.

Consideration will also be given to good collaborative skills, drive and independence, and how the applicant, through his or her experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme.

Terms of employment

Only those admitted to third cycle studies may be appointed to a doctoral studentship. Third cycle studies at LTH consist of full-time studies for 4 years. A doctoral studentship is a fixed-term employment of a maximum of 5 years (including 20% departmental duties). Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 §§.

Instructions on how to apply
Applications shall be written in English or Swedish and include a cover letter stating the reasons why you are interested in the position and in what way the research project corresponds to your interests and educational background. The application must also contain a CV, degree certificate or equivalent, and other documents you wish to be considered (graduation thesis, grade transcripts, contact information for your references, letters of recommendation, etc.).