Comment: There’s a new obstacle to getting a job after college: Getting approved by AI

Companies may not be ready to outsource vetting candidates for C-Suite and executive positions to algorithms, but the stakes are lower for entry-level roles and internships. That means some of today’s college students are effectively the guinea pigs for a largely unproven mechanism for evaluating applicants.

Metz, R. (2019). There’s a new obstacle to getting a job after college: Getting approved by AI. CNN Business.

I agree with the concern that we don’t have a good idea of how well these algorithms will work when it comes to narrowing the field of potential interviewees for a post. However, I think that it can’t be any worse than what currently happens.

We already know that unstructured interviews by human beings are completely unreliable predictors of future performance (structured interviews seem to work better but the improvements in validity are marginal…better than chance but not by much). What if we find out that AI is at least reliable? At first glance, the idea that an AI-based system will screen candidates to narrow the pool of applicants seems unfair but we already know that being screened and interviewed by a human being is also unfair. So a human interview panel is likely to be both invalid and unreliable, whereas a computer might at least be reliable. Although I suspect the AI will also be a better predictor of performance than human beings, because it’ll probably be less likely to be influenced by irrelevant factors.

For me, this seems to be another example of having different expectations for outcomes, where an AI has to be perfect but a human being gets a pass. Self-driving cars are the same; they have to demonstrate near perfect reliability, whereas human drivers are responsible for the preventable deaths of tens of thousands of poeple every year.

AI clinical

Research project exploring clinicians’ perspectives of the introduction of ML into clinical practice

I recently received ethics clearance to begin an explorative study looking at how physiotherapists think about the introduction of machine learning into clinical practice. The study will use an international survey and a series of interviews to gather data on clinicians’ perspectives on questions like the following:

  • What aspects of clinical practice are vulnerable to automation?
  • How do we think about trust when it comes to AI-based clinical decision support?
  • What is the role of the clinician in guiding the development of AI in clinical practice?

I’m busy finalising the questionnaire and hope to have the survey up and running in a couple of weeks, with more focused interviews following. If these kinds of questions interest you and you’d like to have a say in answering them, keep an eye out for a call to respond.

Here is the study abstract (contact me if you’d like more detailed information):

Background: Artificial intelligence (AI) is a branch of computer science that aims to embed intelligent behaviour into software in order to achieve certain objectives. Increasingly, AI is being integrated into a variety of healthcare and clinical applications and there is significant research and funding being directed at improving the performance of these systems in clinical practice. Clinicians in the near future will find themselves working with information networks on a scale well beyond the capacity of human beings to grasp, thereby necessitating the use of intelligent machines to analyse and interpret the complex interactions of data, patients and clinical decision-making.

Aim: In order to ensure that we successfully integrate machine intelligence with the essential human characteristics of empathic, caring and creative clinical practice, we need to first understand how clinicians perceive the introduction of AI into professional practice.

Methods: This study will make use of an explorative design to gather qualitative data via an online survey and a series of interviews with physiotherapy clinicians from around the world. The survey questionnaire will be self-administered and piloted for validity and ambiguity, and the interview guide informed by the study aim. The population for both survey and interviews will consist of physiotherapy clinicians from around the world. This is an explorative study with a convenient sample, therefore no a priori sample size will be calculated.


Graduates are taking £9k courses to help beat AI interviews for City jobs

Via a webcam, the software remotely asks preliminary-round candidates 20 minutes of questions and brain-teasers, and records eye movements, breathing patterns and any nervous tics. Popular software such as HireVue also scans for emotion and expressions, such as blinks, smiles and frowns, by monitoring the face through the applicant’s front-facing smartphone camera or computer webcam.

Source: Blunden, M. (2018). Graduates are taking £9k courses to help beat AI interviews for City jobs.

Well, that’s just terrifying.

curriculum learning physiotherapy teaching

Interview: The use of technology-mediated teaching and learning in physiotherapy education

Selection_001I was recently asked to do a short interview by Physiospot, on the use of technology-mediated teaching and learning in physiotherapy education. As it turns out, the bulk of the interview relates more specifically to a Scholarship of Teaching and Learning, rather than the use of technology. However, I think that this makes it potentially more relevant for physiotherapy educators, especially those who may not be interested in the “technology” aspect. Thanks to Rachael Lowe at Physiospot for the invitation to chat.

Here is the link to the interview.