ARTICLE · 28 NOVEMBER 2024
Investigating Decision-Making Algorithms, Part 1: Conventional Algorithms And Points Of Failure
The article outlines the challenges in investigating AI decision-making systems, contrasting conventional algorithms with AI algorithms. It highlights the potential failures in conventional algorithms, such as bugs, faulty inputs, and misconfigurations, and emphasizes the complexity of investigating AI algorithms, which may lack full transparency. The article suggests that AI systems' decision-making processes are less predictable, especially with generative AI, which operates on "plausibility"
Developments in artificial intelligence (AI) over the past few
years, catalysed by the public release of ChatGPT in 2022, have
captured the public's attention and brought AI to the forefront
for decision-makers. Simultaneously, we are seeing diametrically
opposed predictions about the impacts of AI, which might be loosely
characterized as "AI as our savior" or "AI as our
overlord".
While much of the hyperbole concerns the potential longer-term
impacts of AI, regulators and lawmakers are focused on more
immediate concerns, such as the risk of AI systems exhibiting bias
or discriminatory behavior, or generative AI (GenAI) hallucinating
(i.e., inventing falsehoods), or being used for nefarious means,
such as producing realistic audio and video content
("deepfakes") that can facilitate fraud or widespread
disinformation.
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