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Business and government

Australia needs crypto-asset reform

February 22, 2023

What is a crypto-asset, ASIC?

The Australian Securities and Investments Commission (ASIC) released a 2022–2026 corporate plan which included crypto-assets as a key focus. They acknowledged the need to support the development of an effective regulatory framework for crypto-assets, with attention given to consumer protection and market integrity.

Despite various papers and consultations by ASIC relating to how crypto related products are to be formally classified, their guidance is still very vague and this is causing confusion in the market. As a result, this is affecting Australia’s credibility as a serious crypto destination.

Regulatory confusion

Crypto-assets do not fall within the existing regulatory perimeter of financial products and services and are generally unregulated by ASIC. However, crypto-assets have been classified as a commodity by major global regulators and industry for years. ASIC has not accepted this categorisation, but instead created a new standalone crypto-asset class, as defined in INFO 230. It has also embedded this into licencing applications and company obligations under the Corporations Act 2001, as set out in INFO 225.

ASIC’s approach of treating crypto-assets as a new regulatory asset class has not stopped some crypto-assets still being regarded by the Australian market as commodities (much like gold). ASIC has also stated that some crypto-assets fall into the definition of a financial product. How a crypto-asset is structured or how the rights are provided to a person who acquires and holds the product, adds another level of complexity, as this defines a security.

On the 29th of October 2021 ASIC released guidance on crypto-asset related investment products (21-285MR). This included notes on how businesses can meet their regulatory obligations in relation to crypto-asset exchange traded products (ETPs) and other investment products. This guidance superseded ASIC’s Good Practices and Licencing Public Consultation Paper 343 (Crypto-assets as underlying assets for ETPs and other investment products (CP 343) in June 2021). Consultation Paper 343 defined a crypto-asset as

“can be understood to be a digital representation of value or contractual rights that can be transferred, stored, or traded electronically, and whose ownership is either determined or otherwise substantially affected by a cryptographic proof. A crypto-asset may or may not have identifiable economic features that reflect fundamental or intrinsic value.”

This definition does not describe crypto-assets as a financial product and furthermore ASIC went on to state that:

“working understanding of crypto-assets and may evolve over time. We may craft a different description of crypto-assets as needed in performing our legislative functions in line with government policy at that time. We use the term ‘crypto-assets’ but recognise that they may also be commonly referred to as digital assets, virtual assets, tokens, or coins. We are not aware of a universally accepted name for, or definition of, ‘crypto-assets’.”

Australia a global outlier

ASIC clearly admits that this is a moving space and may change its mind, which further supports the notion that their approach is causing instability in the market. This is especially evident where their changing direction and ambiguous approach raises the question as to whether offering crypto derived products requires an Australian Financial Service Licence (“AFSL”) to operate, when previously it did not. Furthermore, ASIC stated that it is not aware of a “universally accepted name for, or definition of, ‘crypto-assets.’” when they were consistently described as commodities by the vast majority of thirty-two firms consulted by them as part of the Consultation Paper 343. Broadly speaking, as ASIC regulates securities and other investments that are financial products but does not regulate direct commodity investments. This may explain their reluctance to accept commodities as the description of crypto-assets.

"The regulatory framework is tasked with protecting investors and preserving financial stability.  It should also allow innovation and not get in the way of progress ..."

If one accepts ASIC’s premise that a universal definition of a crypto-asset does not exist, and the suggestion that businesses with an association with crypto-assets require an Australian Finance Services Licence (AFSL), how can a business confirm an association exists when the asset itself cannot be universally defined?

The thirty-two firms consulted by ASIC, also raised concerns as to the confusion and regulatory burdens ASIC may create and suggested crypto-assets were out of ASIC’s asset class domain. Concerns over placing additional regulatory burdens to no benefit and being at odds with other countries could also delay consumer protections and add costs of additional regulator burden that would be paid by regular retail consumers and by crypto businesses.  This may discourage crypto businesses from engaging the Australian market, pushing their operations and jobs offshore.

The confusion does not end there, especially when considering how various crypto businesses might require regulatory authorisation to operate. For example, if contributions are pooled or used in a common enterprise to produce financial benefits or interests in an asset, these falls into the definition of a retail managed investment scheme and an AFSL is required. The investible asset class is now irrelevant, as it is the action of arrangement and dealing which is key. However, this approach is contradicted, if companies are considered which offer online trading in crypto-assets, via a non-dealing desk operation (not providing liquidity for transactions made by clients on their trading platform, as appose to a market making operation). These businesses are not required to hold an ASFL to operate, but should they then offer shares, spread betting, contracts for difference, commodities, forex, or indices, then they would, and therefore the investible asset class is now relevant.

The regulatory framework is tasked with protecting investors and preserving financial stability.  It should also allow innovation and not get in the way of progress, but this is becoming difficult with crypto businesses confused as to how to they can remain regulatory compliant, in such an ambiguous and rapidly shifting regulatory landscape, supervised by regulators with mercurial temperaments.

Author

Will BanksWill Banks is an Adjunct Industry Fellow in Griffith Business School. Will has had a successful career in senior financial and board level executive positions, which have spanned across Australia, Europe, and the United Kingdom. With an expertise in leading businesses through financial regulatory authorisations, mobilisations, or crisis management, he has dedicated over two decades to building, advising, and managing global financial service institutions and start-ups.

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Categories
Society and culture

Chatting about ChatGPT

February 22, 2023

Conversation starters and points of orientation

Pulling back the curtain

The release of the currently free to use chatbot ChatGPT in late November 2022 put the capabilities of large language models (LLMs) into the hands of mere mortals. GPT-3 from which ChatGPT was built has been available since June 2020 for a small charge. The parent Large Language Model or LLM, GTP-3 has a better capacity to process text than the chatbot but lacks the chatbot’s conversational interface.

Chattiness makes the difference. For formal education, the release of ChatGPT put AI firmly on the education agenda; it’s the noisy canary in the AI coal mine. When its capabilities to write credible undergraduate essays or pass medical exams was noticed the predictable reaction—let’s ban it’s use—played out in many formal education institutions worldwide. In this moment, a Janus-like reaction to ChatGPT became evident. Bans looked to the past: “our graduates don’t/won’t/can’t cheat”. Uses looked to the future: “our graduates are well prepared for an AI-infused future.”

This is a familiar tension. Over the history of the use of digital developments in formal education the playbook has remained the same: ban it (good luck) or domesticate it: make it fit within your existing world. Neither response plays out as imagined but typically they tend to play out over longish time periods. This time it is different. The rate of take up of ChatGPT is the fastest in take up of any digital technology. And it this that has left formal education systems scrambling to work out what to do.

In preparing this piece we often felt we were also scrambling to keep abreast of things as reports of usage, reactions, commentary and more and more AI-based apps appeared rapidly and, at the time of writing, show no signs of slowing down. To put it more bluntly, we felt like wall-paper hangers, nursing babies while decorating their nursery in the midst of a tropical Queensland cyclone.

Chat GPT
Image by Rolf van Root
Stepping back to take a breath

We think it is useful to point out that while the release of GPT-3 and then ChatGPT appeared to be sudden, it masked the many years of slow developments in AI of which we have had only odd glimpses. Reports of a machine beating a world champion chess or Go player and, more interestingly, developments in medical diagnosis, protein folding, the law, music and so on.

As some commentary likes to describe it, all that has happened with the release of LLMs is that the curtain has been pulled back on them. It is only one curtain though and it takes attention away from still largely veiled to the public AI developments. As Yann LeCunn, Chief AI scientist at meta put it ”On the highway towards Human-Level AI, Large Language Model is an off-ramp.”

It’s impossible in this short piece to do justice to any kind of mapping of other developments in AI. All that might be observed is that investment in AI research, application and development is huge and likely to keep us busy for a long time.

Given all of that, a key question remains for educators and ChatGPT.

What to do on Monday?

The cover of Douglas Adam’s A Hitchhiker’s Guide to the Galaxy is good advice: “Don’t Panic”. But don’t be smug either.

Whenever a new technology appears it is commonly understood in terms of analogies with what is familiar and well known. ChatGPT has been commonly described as a conversational Google search or a conversational database. This assumption leads us to a confusing space. After a Google search we have to select or curate our ‘answers’ assuming at least some responsibility for accuracy. At times ChatGPT provides useful, even accurate information but it also can and does provide output that is politely described as hallucinatory. It will give answers that are not correct. It will provide references to papers that do not exist. It will (if pressed) change its ‘mind’. It is not a shortcut to ‘the truth’.

The confusion arises because of a poor or limited understanding of how the model was built and how it operates. We believe that a fundamental first step, before Monday, is to develop a rough working understanding of the model which can inform how we work with.

GPT-3 was trained on a large amount of text that was available online. It processes the text in small chunks (called tokens) and establishes numerical patterns of association between them. When the model is prompted, it acts like the autocomplete on your phone. It predicts the next word in a sequence based on the context provided by the previous word – a bit like that co-worker who always wants to try and finish your sentences.

So ChatGPT is a chatbot that outputs fluent, plausible text that reflects the body of text that was online up to the middle of 2021. This leads to experiences which reflect the wide range of views, biases and prejudices of English-speaking people (who are probably, also, able-bodied, middle class, cis-gendered and white). (Ask ChatGPT for an opinion on Shakespeare and you’ll get the idea). To address the problem of producing toxic text, OpenAI, the company that produced the GPT series of LLMs—outsourced the work of identifying either incorrect responses or a text that a subscribed found unhelpful/inadequate/offensive to a company that employed Kenyan workers on very low wages. These people manually tag or label large volumes of text snippets drawn from the “darkest recesses of the Internet”. The manually tagged text snippets were then used to fine tune the model.

The fine tuning of the model continues via the interactions of users since the release of ChatGPT. The text shown prior to use of the bot indicates, “Conversations may be reviewed by our AI trainers to improve our systems”.  You can experiment with the bot by finding a fact that is wrong and correcting it. The correction may result in a minor tweaking that will give a more accurate response in a subsequent conversation with it but not necessarily.

Cheating
Back to Monday

With a rough idea of how it works, to understand and get a good relationship with ChatGPT you need to use it. A lot. You specifically need to spend a lot of time playing with prompts. What counts in any interaction with ChatGPT is the  quality of your questions or prompts. The more detail and context you can provide in a prompt the more useful (to you) the output from the bot is likely to be. Asking for an example of a haiku will produce one kind of response. Asking for a gender-inclusive haiku that reflects the style of Buffy the Vampire Slayer will give you something else. There are many examples online of “creative prompting” or prompt wrangling. As with any new way of doing things you have to use it, play with it, explore what it can and can’t do as well as what it will and won’t do.

If prompt wrangling feels like work, we can take comfort from the fact every educator on the planet with access to ChatGPT is working through this challenge right now. This is a good thing. These are our people. When responding to fast moving innovations like AI we can try and fly solo but a far more productive approach is to work with colleagues, peers: those nearby and those far away; those we already know, and those we have yet to meet.

"For students from culturally and linguistically diverse backgrounds who routinely report challenges with communication as barriers to their education, this might be a game changer."
How to negotiate the use of ChatGPT with students?

What ChatGPT does well is produce what some have described as beige text, which is unsurprising given the corpus it was trained on. So, things like university mission statements, lesson plans, outlines of research reports, job descriptions, press releases, lists of key performance indicators, policy documents, assessment questions, rubrics, assignment descriptions and so on are easily generated. So too are summaries of text and counter arguments to a text.

When it comes to educational practices, it would be naive to assume students won’t make use of such a resource. Equally naive (or, in fact, just offensive) is the assumption that most students will use it to cheat. How to negotiate the use of ChatGPT with students is fundamentally an educational problem, not a technical one. There are all manner of opportunities here, too many to list in a short piece. An important equity opportunity is that for students whose written English is seen as poor, the bot can provide a useful first draft of an essay or report, or identify and explain an error in a sentence, or decipher and explain as many times as needed a tutor’s feedback on a draft. The student can engage the bot in conversation, a one-to-one tutor if you like, to explain an idea, an argument, the use of a phrase. Unlike a tutor, or colleague or study friend, the bot doesn’t get tired. It doesn’t mind how many times you ask a question; it has an enormous repertoire of analogies or metaphors to draw upon, and can increase or decrease the difficulty of an explanation on request. This is not support that University’s seem able to offer from tutors.

For students from culturally and linguistically diverse backgrounds who routinely report challenges with communication as barriers to their education, this might be a game changer. On the other hand, of course, we risk losing the individuality inherent in people’s more natural expression. And that would definitely be a loss.

We did ask ChatGPT to improve this text. We stuck with what we wrote. This points to the third of what we think of as complementary skills for using GPT-3, an ability to judge the quality and accuracy of the output. ChatGPT could say a lot more to us, but for now, the big challenge is for us to be curious, rather than frightened, and to look carefully at the claims (both positive and negative) that are made about our new AI. There was so much we wanted to say. We hope this is a useful conversation starter.

Authors

 

Professor Chris Bigum is an Adjunct Professor in the Griffith Institute for Educational Research at Griffith University.

 

Professor Leonie RowanProfessor Leonie Rowan is the Director of the Griffith Institute for Educational Research at Griffith University.

Professor Rowan’s research and teaching interests focus on three key inter-related areas: gender and education, University teaching and learning Educational and social justice. Her research and teaching are fundamentally interconnected and build on more than two decades of experience in education settings. She has received numerous, prestigious awards for her teaching and multiple competitive research grants (including 6 ARC funded grants) which signal the quality and impact of her work.

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