Presented by John Reid at ITEANZ breakfast meeting with Victorian Minister for Roads, Ben Carroll as guest of honour: 23 February 2021
Good morning, Nick; Good morning Minister Carroll; good morning colleagues.
I'm sorry not to be with you this morning. I'm always looking forward to your making and catching up with my family and colleagues during the week. The impact of it has been huge, including impacting my return travel in and out of Melbourne. Melbourne remains a designated hot spot for Queensland.
The impact of COVID has been huge, but it is also being used as an excuse to cover over some long term, systemic problems.
The pandemic has highlighted how ill-prepared we are for assessing and tackling different situations.
There are examples everywhere. The world’s richest football club, Barcelona, is having trouble paying their bills. Their management blames COVID, others say the problems run deeper and have been developing over a longer period.
We are struggling to grasp the best solutions when, over an extended period, we have seen a sustained vilification of scientists, the scientific method and a measured approach. Our management structures are struggling as we have reduced the number of engineers, planners and statistical experts, and employed more PR and marketing professionals to show why it is someone else’s fault.
Journalism is being replaced with opinion.
We are racing ahead with high powered computer technology without the right foundation. We can make bigger mistakes at a faster rate: Garbage in – Garbage out.
A recent article in the Financial Review said in part:
“With so much data flowing freely across the internet, and with so many technologies evolving that can easily manipulate it, the time has come to think about the quality of the data, and how it is secured and managed, experts say”.
Mainak Mazumdar is a Data and Research expert. He gave a TED talk titled “Bad data keeps us from good AI”. Here are three quotes from his presentation:
“AI is only reinforcing and accelerating our biases at speed and scale, with societal implications”
“As a data scientist, I'm here to tell you it's not the algorithm, but the biased data.
“We're spending time and money to scale AI at the expense of designing and collecting high quality and contextual data.
From the beginning of the computer age there has been unjustified faith in machines to produce answers with little thought about the quality of the data. The originator of the digital programmable computer, Charles Babbage, was flabbergasted when, on two occasions, he was asked:
"Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?"...
Babbage commented later: “I am not able rightly to comprehend the kind of confusion of ideas that could provoke such a question”.
Statements about how we must “try harder” are not enough. We can no longer just shout from the sidelines.
This year is the 50th anniversary of compulsory seat belts and motorcycle helmets in NSW and even longer in some other states. These were good government leadership initiatives. And who can forget the orange haze and the damage to our children’s health that was exacerbated by lead in petrol. The lead issue has been greatly diminished by positive, forceful government initiatives.
Having spent nearly 40 years in the data collection business and seen many government and private industry personal stultified by bad data, we need to address:
- what is being collected;
- how is it being collected
- why price rather than relevance and accuracy is dominating the process;
- audits of the veracity of the data;
- resources on how we store the data; and
- managing how it is being used.
There are several longer articles on these issues on our specialist web site on data “Anything But Average” [such as Bad Data Makes a Joke of AI and What Can The Dangers Of Averages In Education Teach Us?]
Our society needs a data science led recovery to regain good management, respect for proper analysis and sustainable, properly resourced structures.