Growing the AI talent pool: We need deep learning

Image result for deep plant roots

The AI Forum NZ recently kicked-off six working groups to investigate a range of emerging issues around artificial intelligence and society.

Working group #5 has its focus on Growing the AI Talent Pool.

New Zealand is facing a shortage of technical workers capable of developing AI applications. In what follows I argue that ‘growing’ is the right metaphor to apply to responsibly solving this problem in the long term.

We will clearly need to increase the size of the available talent pool. That will be a multifactorial task that includes increasing the numbers of people choosing AI, data science and machine learning as a career; increasing the throughput of formal learning institutions; increasing the availability and uptake of on-the-job and mid career training; and increasing the supply of talent from outside of New Zealand.

However, having an ideal talent pool is not merely about the numbers, it is also about ensuring that the talent we grow is the right kind of talent, with the right traits and characteristics to best to enable a prosperous, inclusive and thriving future New Zealand. This means developing skills that go beyond technical capability. It also means ensuring that non-technical specialists understand machine learning and the capabilities of AI in order to make optimal and ethical use of it.

Impacting society at scale

With any technology that affects society at scale (as AI can clearly do) we have an obligation to develop it responsibly. In the past the industrial revolution was poorly managed resulting in exploitation of factory labour. Technological innovation in the Twentieth Century began the catastrophe of atmospheric pollution. More recently, we can note that:

“In the past, our society has allowed new technologies to diffuse widely without adequate ethical guidance or public regulation. As revelations about the misuse of social media proliferate, it has become apparent that the consequences of this neglect are anything but benign. If the private and public sectors can work together, each making its own contribution to an ethically aware system of regulation for AI, we have an opportunity to avoid past mistakes and build a better future.” – William A. Galston (Ezra K. Zilkha Chair in Governance Studies)

When the things we do and create impact society at scale there is a responsibility to get it right. That’s why we have professional certifications and a structured programme of non-technical knowledge and skill learning embedded in courses such as engineering, medicine, and law. Take for example the University of Auckland’s Medical Humanities programme and compare that to the course list for University of Otago’s computer science department, where only one paper mentions ethics as a designated component.

AI talent origin stories

Furthermore, machine learning practitioners and AI developers do not come from any one particular development pipeline. You do not need to specifically have a PhD in AI to fill these roles. AI practitioners can come from any mathematically rigorous training programme. Graduates in computer science, math, physics, finance, logic, engineering, and so on, often transition to AI and machine learning.

One glaring issue is that some of these generalist disciplines do not have a programme of social responsibility and professional ethics embedded in them (engineering may be an exception). Nor are there professional certification requirements for a lot of these skilled workers. This is in stark contrast to other professional disciplines such as accounting, law, nursing, teaching, medicine, and many others.

Social responsibility and professional ethics

To ensure responsible development of the developers we either need to embed responsibility development in all these programmes that can lead to AI practice, or take the whole thing a step further back to high school, or, stepping vertically, we need to ensure institutional codes and professional regulation. Probably all these are required.

Society expects the developers of intelligence to respect public institutions, privacy, and people as autonomous agents among many other things. We do not want to be phished for phools for profit or to further an agenda. Just because something affecting society is possible does not mean it is automatically acceptable.

Just like medical writers sign up to a code of medical writing ethics to push back and rein in the whims of Big Pharma (who employ most of them), we need to have faith in the talent pool who will be developing AI if it affects us all.

The problem may not be so great when workers are employed by businesses that are ethical, socially responsible, and whose aims are aligned with those of societal flourishing. It can be argued that several of the big tech firms are moving in this direction. IBM, Google and Microsoft, for example, have published ethical and/or social codes for development of AI in 2018. But not all developers will migrate from their technical training into socially responsible firms.

IBM’s Everyday Ethics for AI report notes the following:  “Nearly 50% of the surveyed developers believe that the humans creating AI should be responsible for considering the ramifications of the technology. Not the bosses. Not the middle managers. The coders.” Mark Wilson, Fast Company on Stack Overflow’s Developer Survey Results 2018

Growing true AI talent through deep learning

Growing the talent pool is an appropriate metaphor. We do not just want a wider harvest of inadequate talent, nor do we merely want the planting of many more average seeds. We also need to choose the right educational soil and to add the right fertilizer of ideas, concepts and socially responsible skills.

Intervention is needed at three levels and across three time horizons. We need broad social, ethical, civics, and society education prior to the choice of a career specialization.

We need to cross-fertilize tertiary training in all disciplines that lead into AI practice with courses and dialogue on social responsibility, human flourishing, ethics, law, democracy and rights. And we need to ensure that professional engineering, AI and machine learning institutions mandate adherence to appropriate codes of conduct.

We need deep learning around all these issues from early on.

We need to begin now with current practitioners, we need to foster these ideals in those who have selected AI as a career, and we need to prepare the future generation of AI talent.

If the tech specialists don’t see the force and necessity of these points, then that in itself proves the truth of the argument.

Who is responsible?

Here I am with no background in AI or machine learning telling those who would make a career in these fields that they must study soft skills too. So why do all our voices count in this space?

We are talking about the applications of intelligence, and as intelligent beings we are all qualified to talk about how intelligence is distributed in society, how it is deployed and what functions it has.

When you go to a conference on nuclear physics everyone at the conference may be a nuclear physicist. But those that develop a technology are not automatically those who get to decide how we use it.

We see this when policy makers, ethicists and the public determine whether those nuclear physicists are permitted to detonate atomic weapons. We see this when the committees at the FDA determine whether medical technologies and pharmaceuticals will be licensed for use in society. AI and machine learning applications bear much in common with these other domains.

With great intelligence (and the development of great intelligence) comes great responsibility.

Importance and urgency

The reason all this is important is because digital technology now infuses every domain in society, and AI is rapidly becoming an integral part of Law, Medicine, Engineering, and every other professional discipline. We are going to need professionals who understand AI, but we are also going to need technical developers who understand the professional aspects.

There are tasks in society that are urgent and those that are important. There are interventions that will have a narrow impact and those that will have a wide ranging impact. In addressing those issues that are urgent and narrow (and therefore manageable) we cannot forget the issues that are ongoing and less well-defined, but highly impactful.

The most important things moving forward are to ensure a just and cohesive society that supports democratic institutions and upholds social norms and rights; a society that does not use exploitation or manipulation as key processes for generating profit. A society in which technological innovation respects the evolution of institutions.

We must ensure that as a society we develop a pool of talented, socially aware, and responsible AI practitioners.

Efficient Cancer Care in New Zealand: Lessons from five years of Australian research literature


The cost of cancer care is rising and a review of the research literature on cancer care in Australia can teach many lessons to us in New Zealand.

In Australia real costs for cancer per person (adjusting for inflation) have more than doubled in the last 25 years. The drivers are multifactorial but due in part to upward trends in diagnosis (often the result of new diagnostic methods and screening programmes), the rising cost of cancer pharmaceuticals, and increasing expectations.

The largest costs are treatment costs. Taking Australia as an example, hospital services, including day admitted patients (usually for chemotherapy), account for 79% of cancer costs. The number of approved cancer medicines has doubled since 2013.

Rising costs in health care are not sustainable. We need better efficiency.

Efficiency in health is about making choices that maximise the health outcomes gained from the resources allocated. And it seems like there are a number of different ways that we could target the cancer care pathway to improve efficiency. However, this can only work if the entire care path is looked at as a whole, and the notions of funding silos are dispensed with.


For example, healthy lifestyle and regular screening could prevent an estimated one third to one half of all cancers, but presently, only single-figure percentages of cancer funding target prevention.

This is despite the modelled return on investment for cancer prevention programmes, which is often $3–$4 per $1 spent. As an added bonus, cancer prevention can also reduce the burden of other diseases (e.g. reducing inactivity can also benefit diabetes and heart disease).


Participation rates in screening programmes are generally poor. For many programmes 40–60% is considered a good uptake. This is inadequate. Increasing screening rates is likely to increase the effectiveness of screening programs. And modelling suggest in some cases that sufficient uptake can lead to future cost savings.

We should also do more to ensure that patients who are up-to-date with screening are not re-screened (e.g. those who have had recent colonoscopy) and ensure that follow-up after screening is based on guidelines. It often isn’t.


Over-diagnosis is becoming a problem in the cancer care path. Breast screening often reveals anomalies that are not cancer. Artificial intelligence systems used to augment physician diagnosis could curb this.

Not only is there evidence from a 2015 systematic review that prostate cancer screening is not cost effective, but prostate screening with PSA can lead to cancer diagnoses (and treatment) in men whose tumors will never cause them problems.

There has also been a rapid spike in thyroid cancer diagnoses, leading to an increase in thyroid surgeries, for example in Australia, but no corresponding change in deaths from thyroid cancer.

Reducing unnecessary detection and a conservative approach could lead to millions of dollars in savings and reduced harms to patients from over-diagnosis.


The cost of treatment is also a problem. In Australia, cancer accounts for 6.6% of hospital costs, but the cost of cancer medication is one sixth of the total pharmaceutical budget. The 10-fold increase in cost of these medicines over 10 years is a serious threat to patients and health systems.

We could decrease the costs of cancer medications by modifying prescription habits, considering treatment costs in professional guidelines, disinvesting in medicines that have not proven cost-effective in the real world, improving patient selection, and increasing use of generics.

There is evidence of over-treatment. A watch and wait approach is appropriate for many prostate cancers in the early phase, or active surveillance of low risk patients could reduce costs and is often clinically reasonable.

We could consider pharmacist review of prescriptions to avoid the risk of adverse drug reactions (and the associated treatment costs). We could do more to ensure there are no unjustified variations in clinical practice.


We should ensure that patients have a written care plan and are not receiving follow-up from multiple overlapping providers. Also, follow-up should be guideline based. Some studies indicate that less than half of bowel cancer patients received guideline-based follow-up colonoscopy.

We could make more use of primary care where studies have not shown hospital follow-up to be any more effective in detecting recurrent disease.

Traditional follow-up focuses on detecting cancer recurrence, but this can fail to adequately address many survivors’ concerns. Getting back to work (and being supported to do so is important to reduce the societal costs of cancer. Occupational therapy may be important in facilitating this.


Palliative care costs less than hospital care and is under-utilised. But to optimise the use of out-of-hospital palliative care, patients need to have accurate prognostic awareness, allowing them to make informed choices. This requires important conversations with treatment providers. Lack of a palliative care plan leads to unnecessary emergency room visits and hospital admissions that are primarily palliative.


Research costs could also be streamlined. We should ensure that the cancer research being undertaken reflects the burden of cancer. Lung cancer has the greatest burden of all cancer (especially in terms of years of life lost) and yet there is far less lung cancer research than this burden demands.

Cancers including leukaemia, breast, ovary, liver and skin, often receive proportionately more funding than their disease burden. Prevention, cancer control, and survivorship research could be funded more. This is because effectiveness in these components of the care path lead to downstream cost savings and potentially increased social productivity.


Overall, it looks like prevention and early detection are generally underfunded. There is also scope to increase participation in screening programs.

The rapidly rising costs of treatment, including medications, need to be curtailed through wise practice, and new models of care, that prioritise prevention, screening, surveillance, guideline and evidence-based follow-up, return to work, and palliative care where appropriate.

Reducing the cost of medications is a high priority, with large potential cost savings. The focus should be on treatments that are proven to work well in the real world rather than on increasing use of expensive drugs with marginal benefit.

We need a long-term focus including a culture of change and workforce planning. Further efficiencies might be gained through initiatives such as: Choosing Wisely, addressing variations in process and treatment, minimising non-adherence to treatment, avoiding communication failures, ceasing ineffective interventions, coordinating care, reducing admissions, using generics, negotiating price, reducing adverse events, taking a societal perspective of costs, and considering upfront cost versus long-term impact.

Further Reading

Ananda, S., Kosmider, S., Tran, B., Field, K., Jones, I., Skinner, I., . . . Gibbs, P. (2016). The rapidly escalating cost of treating colorectal cancer in Australia. Asia-Pacific Journal of Clinical Oncology, 12(1), 33-40.

Chen, C. H., Kuo, S. C., & Tang, S. T. (2017). Current status of accurate prognostic awareness in advanced/terminally ill cancer patients: Systematic review and meta-regression analysis. Palliative Medicine, 31(5), 406-418.

Colombo, L. R. P., Aguiar, P. M., Lima, T. M., & Storpirtis, S. (2017). The effects of pharmacist interventions on adult outpatients with cancer: A systematic review. Journal of Clinical Pharmacy and Therapeutics, 42(4), 414-424.

Cronin, P., Kirkbride, B., Bang, A., Parkinson, B., Smith, D., & Haywood, P. (2017). Long-term health care costs for patients with prostate cancer: a population-wide longitudinal study in New South Wales, Australia. Asia-Pacific Journal of Clinical Oncology, 13(3), 160-171.

Doran, C. M., Ling, R., Byrnes, J., Crane, M., Shakeshaft, A. P., Searles, A., & Perez, D. (2016). Benefit cost analysis of three skin cancer public education mass-media campaigns implemented in New South Wales, Australia. PLoS ONE, 11 (1).

Furuya-Kanamori, L., Sedrakyan, A., Onitilo, A. A., Bagheri, N., Glasziou, P., & Doi, S. A. R. (2018). Differentiated thyroid cancer: Millions spent with no tangible gain? Endocrine-Related Cancer, 25(1), 51-57.

Gordon, L. G., Tuffaha, H. W., James, R., Keller, A. T., Lowe, A., Scuffham, P. A., & Gardiner, R. A. (2018). Estimating the healthcare costs of treating prostate cancer in Australia: A Markov modelling analysis. Urologic Oncology: Seminars and Original Investigations, 36(3), 91.e97-91.e15.

Jefford, M., Rowland, J., Grunfeld, E., Richards, M., Maher, J., & Glaser, A. (2013). Implementing improved post-treatment care for cancer survivors in England, with reflections from Australia, Canada and the USA. British Journal of Cancer, 108(1), 14-20.

Langton, J. M., Blanch, B., Drew, A. K., Haas, M., Ingham, J. M., & Pearson, S.-A. (2014). Retrospective studies of end-of-life resource utilization and costs in cancer care using health administrative data: A systematic review. Palliative Medicine, 28(10), 1167-1196. doi:10.1177/0269216314533813

Lao, C., Brown, C., Rouse, P., Edlin, R., & Lawrenson, R. (2015). Economic evaluation of prostate cancer screening: A systematic review. Future Oncology, 11(3), 467-477.

Leggett, B. A., & Hewett, D. G. (2015). Colorectal cancer screening. Internal Medicine Journal, 45(1), 6-15.

Ma, C. K. K., Danta, M., Day, R., & Ma, D. D. F. (2018). Dealing with the spiralling price of medicines: issues and solutions. Internal Medicine Journal, 48(1), 16-24.

MacLeod, T. E., Harris, A. H., & Mahal, A. (2016). Stated and Revealed Preferences for Funding New High-Cost Cancer Drugs: A Critical Review of the Evidence from Patients, the Public and Payers. The Patient, 9(3), 201-222. doi:10.1007/s40271-015-0139-7.

Olver, I. (2018). Bowel cancer screening for women at midlife. Climacteric, 21(3), 243-248.

Shih, S. T., Carter, R., Heward, S., & Sinclair, C. (2017). Economic evaluation of future skin cancer prevention in Australia. Preventive Medicine, 99, 7-12.

Wait, S., Han, D., Muthu, V., Oliver, K., Chrostowski, S., Florindi, F., & de Lorenzo, F. (2017). Towards sustainable cancer care: Reducing inefficiencies, improving outcomes—A policy report from the All.Can initiative. Journal of Cancer Policy, 13, 47-64.

Youl, P., Baade, P., & Meng, X. (2012). Impact of prevention on future cancer incidence in Australia. Cancer Forum, 36(1).