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It's Time to Sit Down and Think about AI

Version of 24 January 2026

Roger Clarke **

© Xamax Consultancy Pty Ltd, 2025-26

This document is at http://rogerclarke.com/EC/BAI.html

A great deal of enthusiasm for AI continues to be evident. But there are also plenty of warning signs. ChatGPT and its competitors (Generative AI / GenAI) stimulated claims that staff costs could be slashed. On the other hand, early studies suggest the claims may have been exaggerated, because higher-level skills are now in demand, to discover and fix errors in GenAI output. The massive amounts investors claim to be injecting have already given rise to talk of a stock market bubble, and predictions of another 'AI winter' are imminent. GenAI has been found to be prone to hallucinations, and humans may have been having some as well. Added to that, misunderstandings abound as to what AI is. It's a good time to take stock of the situation, and ensure that we, both as individuals, and in our organisational roles, are awake to realities, and understand and manage risks.

A first step is to understand the general idea of AI. Three different interpretations interleave with one another, and confuse practioners, users, the media and the general public alike. The original motivation, back in 1955, was to create artificial forms of human intelligence, hence Artificial Intelligence (AI). A few theorists persist with this approach, but most AI practitioners regard the notion of 'strong AI' as a wild, 'out there', even crackpot idea, and are embarrassed when entrepreneurs and marketers use that kind of language.

At the other extreme, intergovernmental bureaucrats in places like the European Commission, the Council of Europe and the OECD, refer to AI in such broad terms that it encompasses justabout all forms of software applications. Maybe we do need laws that say things like 'Organisations and individuals found to be reponsible for any software that kills people are liable for the harm done, and are subject to civil and criminal sanctions'. But if that's what regulators are saying, they can say it without confusing the meaning of AI.

Using the third interpretation of AI, practitioners do not aspire to replicate human intelligence, but rather are inspired by human intelligence in their quest for better ways to write software - and perhaps to architect and engineer artefacts that are even better than conventional computers at supporting effective software.

Over the last four decades, AI research and development has delivered some great successes. Exemplars in the pattern recognition area include applications to optical characters (OCR, automated number plate recognition, QR codes) and to sound, such as music, by means of acoustic fingerprinting techniques. Natural language 'understanding' (NLU) now copes well with the syntaxes of natural languages, although progress with semantics is more challenging. Natural language generation (NLG) uses structured representations of information to synthesise text that is acceptable and even appealing. This has been achieved using a wide array of techniques and tools, some longstanding and conventional, and others purpose-built and with quite different attributes.

Abstracting from the exemplars, techniques and tools, here's a definition that represents attitudes common among contemporary practitioners and theorists, particularly in the currently popular form of data analytics using machine learning (AI/ML), and GenAI:

A great deal has been written about the threats inherent in various forms of AI. The sources of risk can be usefully grouped into five areas. Two related areas are inappropriate assumptions about data, and about inferencing processes. Techniques for processing data depend on the input having particular attributes. Yet a great deal of the practice of AI involves the processing of data that was collected for one purpose, but is applied to another. Data is expropriated, and applied by very different organisations, in very different contexts. Data is consolidated from multiple sources. Far too little attention is paid to the meanings of the data-items that are being relied upon, and to the (in)compatibility of data acquired from disparate sources. Far too little account is taken of the limited quality assurance that was applied to the data when it was collected, and the infeasibility of improving the quality of data whose provenance is distant and poorly understood.

The resulting data-mounds are then processed -- and in several current techniques thoroughly mangled -- in order to draw inferences of various kinds. Some techniques are based on models of the real world, in the hope that the results will bear a near-enough relationship with some part of the real world. On the other hand, several recent techniques have abandoned theory and the rational modelling that theory enables. So-called 'unsupervised learning' empowers artefacts to 'make of the data whatever they make of the data'. Reliance on correlation alone is being championed as being superior to the scientific approaches of the last five centuries, which interleave observation-originated theories and models with directed experimentation and purposeful measurement of outcomes.

A third set of issues arises from the increasing opaqueness of AI techniques. The algorithmic approaches commonly adopted between 1960 and 2000 involved a problem-definition that was either express or inferrable from an express solution design. The rationale underlying any inference, any decision and any action was either readily available or could be constructed from the audit trail. That transparency was compromised by rule-based expert systems, but not destroyed. With contemporary, purely numerical approaches, on the other hand, there is no rationale. Neither the artefact nor its user can provide a humanly-understandable explanation.

Analysis has identified 20 potentially harmful attributes of GenAI techniques. They embody a simplistic form of syntactical analysis that purports to offer semantic content, but lacks any appreciation of content, context, audience or impact. This has resulted in it being described as 'a stochastic parrot'. It offers an at best very limited capability to be interrogated in relation to its sources and their meaning and relevance. It projects and amplifies existing biases, errors and planted information / rumours / 'fake news'. It generates plausible-sounding statements that are demonstrably wrong, or nonsensical (commonly referred to as 'hallucinations'). The problem is compounded by the infeasibility of tracing back to the source of the misrepresentations.

The fourth problem-area is the explosion in artefact autonomy that is accompanying the vogue management notion of 'digitalisation'. This features dependence on automated processing of data-mounds, and reduced staff-counts, enabling the abandonment of rationality. Artefacts are being delegated to infer, decide and act to an unprecedented degree. The arrogance and inanity of the Robodebt assault on welfare-recipients was the tip of the iceberg. It used no AI techniques, just (simplistic) algorithms. The rationale was pilloried in public at the end of 2016. Once the logic were exposed to the court in late 2019, the $2 billion fraud collapsed. The next Robodebt scheme will use AI/ML and GenAI techniques. Those techniques do not and cannot provide rational explanations. With 'nothing to see here', the next Robodebt fraud will survive challenge. Corporations, small business and the public are at dire risk of arbitrary decisions by governments; and small business and the public face the same threat from corporations.

The fifth group of risk sources derives to a large degree from the third problem-area, opaqueness. In the absence of humanly-understandable explanations for decisions, affected parties cannot submit counter-argument and evidence, and reviewers, tribunals and courts cannot resolve disputes. This undermines accountability. No party in the supply chain can be held liable for harm caused by some kinds of AI-based inferencing, decisions and actions. This particularly affects the less powerful (typically, consumers, citizens and small business enterprises), and has enormous implications for regulators, courts, policy agencies and parliaments, and for risk management and the insurance industry.

Superficially, it seems good for business enterprises if they can escape liability, and easier for government agencies if they have no need to justify administrative decisions. On the other hand, such technology-driven laissez faire represents the loss of social licence, and would quickly result in the collapse of consumer and citizen loyalty, and of public confidence in its institutions. Law enforcement and military agencies, and their suppliers, contractors and strategic partners, are largely immune from the constraint of public opprobrium. On the other hand, a breakdown in public trust will be seriously harmful to the rest of the business sector, to the broader economy, and to society and polity.

That bleak picture emerges from unsceptical faith by organisations in AI technology. However, an AI-induced trust deficit is not inevitable. An alternative scenario can be built by recognising the unsuitability of the notion of 'Artificial Intelligence', and re-shaping the field of endeavour to align with the needs of people, organisations, society, economy and polity.

For the first 70 years, the focus has been on inventing intelligence that is 'similar to human intelligence, but not real'. What would actually benefit society is a form of intelligence designed into artefacts that is 'usefully different' from human intelligence. Such software would perform intellectual functions that humans do poorly or not at all, perform them within socio-technical systems that include both humans and artefacts, and interact effectively with both humans and other artefacts. Let's call that idea 'Complementary Artefactual Intelligence' (CAI).

Claims about AI are couched in terms of competition with humans, with substitution of humans, and with what the entertainment world conceives as robot dominance and that wild theorists call 'a technological singularity' that will replace homo sapiens with robotica supra sapiens. Switching the focus from AI to 'complementary artefactual intelligence' lays the foundations for synergy between artefacts and humans. We can apply the principles of decision support systems in which artefacts are a tool, or perhaps an artefactual assistant. The notion of a fruitful combination of human and artefactual intellectual abilities orginated at the same time as AI, and has been brewing quietly. The term used for it has long been 'augmented intelligence' -- humans working smarter, with the support of intellectual tools.

The alternative, positive scenario has one further feature. The reconception of intellectual endeavours needs to extend to physical action. There are now 4 million robots in the world. Consumer appliance robots are proliferating, and many functions are performed in robotic-like manner in trains and boats and planes, and cars. Each generally operates for periods of time largely independently of humans, exercising a significant delegation to perform tightly engineered actions within constrained contexts. Another category exists, with an important additional feature very relevant to our futures.

The term 'cobot' was coined to signify active artefacts designed for collaboration with a person. A human hand striking a hammerstone against a piece of flint long ago gave rise to a far more capable augmented person. Rather than conceiving of a robot as a standalone artefact, it can be thought of as a repository of artefactual capability that is complementary to human capability. The cobotics notion is consistent with the idea of 'Complementary Artefactual Capability'. When combined with human capability, it delivers 'augmented capability'.

As the excessive enthusiasm for current forms of AI wanes, we can do better than usher intellectual technologies back to an Arctic Winter for another quiet, dark decade or two. We can abandon the naive notion of AI, and thank it, but bury it. We can reorient R&D towards complementary artificial intelligence and capability, embedded in intellectual and physical tools, co-working-by-design with humans. There is then no need to encroach on the entertainment industry's love affair with super-intelligences and the robot apocalypse. We can get on with improvements to human life and organisational effectiveness and efficiency.

Acknowledgements

This paper is supported by a fuller Opinion Piece, at https:rogerclarke.com/EC/AIOP.html, and an index at https:rogerclarke.com/EC/AIC.html. These provide access to underlying references numbering in the hundreds.

Author Affiliations

Roger Clarke is Principal of Xamax Consultancy Pty Ltd, Canberra. He is also a Visiting Professorial Fellow associated with UNSW Law & Justice, and a Visiting Professor in Computing in the College of Systems & Society at the Australian National University.

The content and infrastructure for these community service pages are provided by Roger Clarke through his consultancy company, Xamax. From the site's beginnings in August 1994 until February 2009, the infrastructure was provided by the Australian National University. During that time, the site accumulated close to 30 million hits. It had passed 80 million by the end of 2025. Sponsored by the Gallery, Bunhybee Grasslands, the extended Clarke Family, Knights of the Spatchcock and their drummer, and her Dr Nurse site

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Created: 2 November 2025 - Last Amended: 24 January 2026 by Roger Clarke
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