Carnegie Mellon University’s two day workshop in Adelaide traverses a new minefield for regulators.
The delivery of public services is increasingly using descriptive analytics of big data. However, the application of predictive analytics in the public sector is proving to be more challenging.
Although predictive analytics is already making important contributions in areas such as suicide prevention and road safety, its testing ground is still in regulation.
In the US, Scotland and New Zealand, controversy over its use has raged, particularly in child protection policies. Similarly, the application of predictive analytics in the criminal justice system – namely parole and sentencing decisions – is proving contentious.
A host of emerging issues include machine bias, exaggerated predictive accuracy, ethics and the infiltration of programmers’ values into algorithm design.
Rather than slowing down the use of predictive analytics in regulation, however, these techniques are now being extended to areas as diverse as pollution control, access to alcohol and cycling rules. The bottom line is the need for effective strategies to build trust and legitimacy if these tools are to be used to generate public good.
The practice of regulation stands to be profoundly affected by the use of big data for such purposes.
Do data-driven techniques for making decisions threaten to supplant regulators’ traditional approaches to problem-solving using their expertise and experience?
What issues does the use of big data and predictive analytics present for increasing the consumers and citizens based affected by these regulatory processes?
Talking about transparency is fine but how do you explain the algorithm to a family having a child removed from their care?
Carnegie Mellon University is ranked #1 in the US for IT. Its Australian offshoot has designed this workshop to look closely into the future of the application of big data and predictive analytics in regulation, drawing upon the knowledge of leading practitioners and academics from around Australia.
The two day workshop uses the CMU IT faculty to give participants hands-on experience in techniques of data mining and predictive analytics using software and a provided data set.
No prior skills are required and all participants will be supported by CMU’s trained technical staff.
This is an ambitious agenda for two days. For that reason, participant numbers will be limited strictly to 35.
