Why XAI will be the Next Big Thing

Nothing new under the sun

Or should I use the expression “plus ça change, plus c’est la même chose”? Because what’s at stake in large language models (LLM) like ChatGPT4 and others is the trade off between the model’s fit, its accuracy on the one hand and transparency, interpretability, and explainability on the other. This dilemma is as old as classical statistics: a simple regression model may be inaccurate but it is easily readable for end users without a large background in statistics.

Anyone can read from a graphical representation that there is a correlation between the office surface, the location class and the office rent.  But high dimensional analysis results from a neural network are less transparent and interpretable, let alone explainable.

The same goes for LLMs: there is no way a human domain expert can fathom the multitude of weight matrices used in determining the syntax relationship between words.

About hard to detect hallucinations

Anyone can see nonsense coming out of ChatGPT like responses inconsistent with the prompt. But what about pure fiction represented in a factual consistent and convincing way? If the end user is not a domain expert he will have trouble recognising the output.

The mitigation is called RAG (Retrieval Augmented Generation. It’s a technique that enables experts to add their own data to the prompt and ensure more precise generative AI output. But… then we’re missing the whole point of generative AI: to enable a broader audience than domain experts doing tasks for which they had little or no training or education.

Domain expertise is needed in most cases

Generating marketing and advertising content may work for low level copy like catalogue texts but I doubt, it will deliver the sort of ads you find on Ads of the World https://www.adsoftheworld.com/

I grant you the use case of enhancing the shopping experience as a “domainless” knowledge generator.  But most use cases like drug discovery, health care, finance and stock market trading or urban design to name a few require domain knowledge to prevent accidents from happening.

Only 7% of the citations were accurate!

Take health care: a study from Bhattacharyya et al in 2023[1]  identified an astonishing number of errors in references to medical research. Among these references, 47% were fabricated, 46% were authentic but inaccurate, and only 7% were authentic and accurate. My friend, a medicine practitioner, was already frustrated by people googling their symptoms and entering his cabinet with the diagnosis and the treatment; with this tool I fear his frustrations will only increase… Many more examples can be found in other domains[2].

Hallucations galore in Generative AI

Another evolution in AI is about moving away from tagging by experts and replacing this process by using Self-supervised Learning (SSL, no, not the network encryption protocol).  Today’s applications in medicine produce impressive results but again, this approach still requires medical expertise. In the context of generative AI, self-supervised learning can be particularly useful for pre-training models on large amounts of unlabelled data before fine-tuning them on specific tasks. By learning to predict certain properties or transformations of the data, such as predicting missing parts of an image (inpainting) or reconstructing corrupted text (denoising), the model can develop a rich understanding of the data distribution and capture meaningful features that can then be used for generating new content.

Enter XAI

The European Regulation on Artificial Intelligence (AI Act)[3] which is in the final implementation process is a serious argument for avoiding sorcerer’s apprentices. Especially in high-risk AI applications, such as those used in healthcare, transportation, and law enforcement, the AI Act will make those applications subject to strict requirements, including data quality, transparency, robustness, and human oversight. Additionally, the Act prohibits certain AI practices deemed unacceptable, such as social scoring systems that manipulate human behaviour or exploit vulnerabilities.

This will foster the use of explainable AI at least for domains where already existing legislation is requiring transparency, e.g. Sarbanes Oxley, HIPAA and others. Professionals in banking, insurance, public servants deciding on subsidies and grants, HR professionals evaluating CVs are just a few of the primary beneficiaries of XAI.

They will need models where humans can understand how the algorithm works and tweak it to test its sensitivity. By doing so, they will get a better understanding of how the model came up with a certain result.

In short, XAI models may be simpler but better governed and they will grow in usability as new increments are added to the existing knowledge base. As we speak, sector specific general models are being developed, ready for enhancing them with your specific domain knowledge.

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[1] High Rates of Fabricated and Inaccurate References in ChatGPT-Generated Medical Content.

Bhattacharyya M, Miller VM, Bhattacharyya D, Miller LE.

Cureus. 2023 May 19;15(5):e39238. doi: 10.7759/cureus.39238. eCollection 2023 May.

PMID: 37337480 Free PMC article.

[2] Athaluri SA, Manthena SV, Kesapragada VSRKM, Yarlagadda V, Dave T, Duddumpudi RTS. Exploring the Boundaries of Reality: Investigating the Phenomenon of Artificial Intelligence Hallucination in Scientific Writing Through ChatGPT References. Cureus. 2023 Apr 11;15(4):e37432. doi: 10.7759/cureus.37432. PMID: 37182055; PMCID: PMC10173677.

[3] Source: https://ceridap.eu/the-impact-of-the-ai-act-on-public-authorities-and-on-administrative-procedures/?lng=en