Postgraduate Diploma in Algorithmic Transparency and Health Equity
Online 800 h Postgraduate Diploma in Algorithmic Transparency and Health Equity. 8 modules covering Regulation (EU) 2024/1689 (AI Act), Regulation (EU) 2017/745 (medical devices) and Python (Fairlearn). Assigned tutor and open enrolment.
Postgraduate Diploma in Algorithmic Transparency and Health Equity — studying from Australia
Health technology and biomechanics are growing areas in Australia, supported by a strong medical research sector and an established medical device industry. Wearable devices and occupational safety systems are increasingly used in mining, construction and healthcare. Medical devices are regulated nationally, requiring engineers to understand conformity, risk management and clinical evaluation. Employers include medical device manufacturers, hospitals, research institutes such as those affiliated with universities, and workplace safety consultancies.
Fees for Australia: A$2,050 (AUD), with instalments and employer sponsorship available.
Relevant bodies in Australia
Typical roles
Questions from Australia
Will a SEIUM qualification be recognised by Engineers Australia or other professional bodies for this area?
SEIUM qualifications are private qualifications and are not accredited by Engineers Australia or any other national body. Recognition is decided by individual employers and, where relevant, by the professional body assessing your application. Engineers Australia assesses skills and experience independently, so check directly with the body you hope to join before enrolling.
Which Australian employers might value this training in health, biomechanics or wearable safety work?
Employers in Australian medical device firms, rehabilitation services, workplace health and safety teams and wearable technology companies may consider advanced engineering training when hiring. Whether they value a private qualification depends on each employer's own policy and on your existing experience. Speaking with prospective employers about how they view private qualifications is a sensible step.
Can I study this alongside full-time work in Australia, and is employer funding possible?
The programmes are 100% online and taught in English, so you can study from any time zone, including Australian time zones, around your work hours. Fees are payable in instalments, and employer sponsorship is possible; ask your employer whether they fund training. Plan for the workload of 300 to 1,900 hours depending on the programme, and confirm the schedule with SEIUM.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Describe the uses of algorithms in clinical practice and their risks for equity.
Apply equity metrics such as demographic parity, equal opportunity and group calibration.
Detect biases originating in data, labels and model design.
Apply bias mitigation techniques before, during and after training.
Generate explanations understandable to professionals and patients.
Interpret the obligations of the AI Act and the MDR for high-risk healthcare systems.
Carry out a complete algorithmic audit of a clinical model.
Who it's for
- Healthcare algorithm auditor who wants to specialise in Algorithmic Transparency and Health Equity.
- Clinical data scientist who wants to specialise in Algorithmic Transparency and Health Equity.
- Head of AI governance in healthcare who wants to specialise in Algorithmic Transparency and Health Equity.
- Regulatory affairs specialist in healthcare software who wants to specialise in Algorithmic Transparency and Health Equity.
- Health equity researcher who wants to specialise in Algorithmic Transparency and Health Equity.
- AI ethics consultant who wants to specialise in Algorithmic Transparency and Health Equity.
- Graduates in engineering or science seeking to enter public and private healthcare.
What you'll work with
- Regulation (EU) 2024/1689 (AI Act)
- Regulation (EU) 2017/745 (medical devices)
- Regulation (EU) 2016/679 (GDPR)
- ISO/IEC TR 24027 (bias in AI systems)
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
- WHO guidance on ethics and governance of AI for health (2021)
8 modules · 800 h
1.1Rules, scores and clinical machine learning1.2Lifecycle of a healthcare model1.3From the Framingham score to deep learning1.4Sources of bias in clinical data1.5Social determinants of health1.6Cost versus need bias (Obermeyer, 2019)1.7Race-based equations: eGFR and spirometry1.8Stakeholders: developers, hospitals and patients1.9WHO principles on AI in health1.10Case study: initial audit of a triage score
2.1Demographic parity and its limits2.2Equal opportunity and equalised odds2.3Calibration by subgroup2.4Kleinberg and Chouldechova impossibility theorems2.5Individual versus group fairness2.6Intersectionality and small subgroups2.7Confidence intervals by group2.8Choosing metrics according to the clinical context2.9Fairlearn and AIF360 tools2.10Practice: calculating metrics with Fairlearn
3.1Under-representation in cohorts and trials3.2Pulse oximetry and skin tone3.3Proxy labels in care data3.4Missing data linked to healthcare access3.5Data drift between hospitals3.6Dermatological imaging and the Fitzpatrick scale3.7Datasheets for Datasets3.8Collection and protection of sensitive variables3.9Stratified exploratory analysis3.10Laboratory: detecting label bias
4.1Reweighting and resampling4.2Training with fairness constraints4.3Post-processing and group-specific thresholds4.4Debate on the explicit use of race4.5Multicentre federated learning4.6Synthetic data and its risks4.7Cost in overall performance4.8External validation in diverse populations4.9Bias in clinical language models4.10Practice: comparing mitigation techniques
5.1Transparency, explainability and interpretability5.2Interpretable models: GAMs and scorecards5.3SHAP, LIME and their limitations5.4Saliency maps in medical imaging5.5Counterfactual explanations for clinicians5.6Model Cards and model sheets5.7TRIPOD+AI and CONSORT-AI guidelines5.8Communicating uncertainty5.9Explanations for patients5.10Workshop: drafting a clinical Model Card
6.1Regulation (EU) 2024/1689 and high-risk systems6.2Interaction with MDR 2017/7456.3Data governance under Article 106.4Article 22 GDPR and automated decisions6.5Sensitive data for bias correction6.6FDA GMLP principles and PCCP6.7European Health Data Space6.8Law 15/2022 on equal treatment6.9Liability and claims6.10Case study: analysis of a prioritisation algorithm
7.1AI committees and clinical ethics7.2Algorithmic impact assessment7.3Patient and community participation7.4Fairness monitoring in production7.5Subgroup drift and alerts7.6Internal and external audits7.7Public registers of algorithms7.8Withdrawal and correction procedures7.9Fairness indicators in dashboards7.10Workshop: post-deployment monitoring plan
8.1Algorithm selection and clinical context8.2Map of stakeholders and affected groups8.3Analysis of data and labels8.4Evaluation of fairness metrics8.5Mitigation proposal8.6Transparency documentation8.7Governance and monitoring plan8.8Conclusions and recommendations8.9Report and presentation8.10Final review with the tutor
How you'll learn
Training is 100 % online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the programme with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (Fairlearn), AI Fairness 360, SHAP, What-If Tool, R, in free, academic or evaluation versions. Exercises are based on real equity audit procedures applied to clinical models, bias analysis by patient subgroup, preparation of model sheets and transparency documentation. Assessment is continuous, module by module, plus a final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Describe the uses of algorithms in clinical practice and their risks for equity.
Apply equity metrics such as demographic parity, equal opportunity and group calibration.
Detect biases originating in data, labels and model design.
Apply bias mitigation techniques before, during and after training.
Generate explanations understandable to professionals and patients.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow technical regulatory texts.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme comprises 800 hours in total, spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with full-time work.
Students work with Python (Fairlearn), AI Fairness 360, SHAP, What-If Tool, R. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An own SEIUM diploma awarded upon passing continuous assessment and the integrative project. It is in-house training, not an official Ministry qualification.
Yes. Modules passed in a course or diploma in the same area are recognised in the higher programme. Check the recognition policy when you enrol.
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