In December 2019 the International Ethics Standards Board for Accountants (IESBA) Technology Working Group (TWG) issued its Phase 1 Final Report. This recommended that:
“……the TWG believes that it would be helpful to develop non-authoritative guidance material to discuss the ethical implications of complex professional environments linked to the impact of technology.”
Subsequently, on 2 February 2021, CPA Canada, ICAS and IFAC held a virtual conference on the theme of ethics and technology and complexity was a key topic discussed. Indeed, following that event CPA Canada, ICAS and IFAC began publishing a series of papers to provide guidance to professional accountants based on the discussions which took place.
The first of these entitled: ‘Complexity and the professional accountant: Practical guidance for ethical decision-making’ published in August was developed by CPA Canada members Brian Friedrich (IESBA member and chair of IESBA’s Technology Working Group) and Laura Friedrich (IESBA technical advisor) under the direction of Gord Beal, vice-president, Research, Guidance and Support, at CPA Canada and with further insights provided by myself and Christopher Arnold, head of SME/SMP and research at IFAC.
The paper highlights that in an ever increasingly complex world where values drive decisions and diverse stakeholder groups have a significant voice, the accountancy profession’s responsibility to act in the public interest presents opportunities as well as challenges. For the profession to increase its relevance and value, we need to collectively hone our skills and ensure that our perspective aligns with stakeholder needs. Similarly, standards must continue to evolve to meet the needs and expectations of society. This is currently happening in the area of sustainability as the International Sustainability Standards Board (ISSB) will soon be established. Whilst this is likely to initially focus on climate change it will need to cover all areas of sustainability.
In general usage, the terms “complex” and “complicated” are frequently used interchangeably, but they are not the same, and the distinction matters. Certain tools and approaches are specifically designed to solve complicated problems, whereas a different set of tools and approaches are needed to effectively manage complex problems. It might be argued that blockchain as a technology is complicated, whereas predicting its impact on the economy or any given industry, is complex.
Complicated problems can have many causes that are interacting, but they can be broken down and addressed piece-by-piece. Outputs are predictable and proportionate to inputs and the resulting problems are solvable. Problems can be challenging to solve – indeed they may be more difficult to solve than some complex problems are to manage – but once solved they tend to remain solved, and the formula, algorithm, tool or approach can be readily applied the next time with predictable consequences.
Complex problems and systems, in contrast, include factors that are not just interconnected, but are also both dynamic and interactive in ways that are difficult or impossible to predict. The multiple interrelated causes cannot easily be broken down using a “divide-and-conquer” problem-solving approach, but must instead be managed holistically. Small changes in inputs can have a disproportionately large impact on outputs, and interactions between elements can lead to unexpected synergies.
Cause and effect relationships can only be fully understood and explained in retrospect. Because of the ambiguity and lack of explainability, the rules, processes and algorithms that might be effectively applied to complicated problems fall short here. It is, however, important to acknowledge that complex systems might well contain complicated elements that can – and should – be identified and solved. Additionally, technology is likely to play a key part in helping to manage complex problems. Preservation of wildlife is a complex problem and dependent on many different factors. Such problems need to be managed. One example of managing such situations can be found in Australia where artificial intelligence has been combined with drones with thermal imagery technology to better identify where koala bears are located to try and help to preserve their numbers. However, this is not a solution, the factors affecting such problems are continually evolving. This evolution and indeed in some cases potentially, revolution, also applies to technological advances.
The upcoming papers in the series will cover the following interconnected but distinct topics: