Amplifier - challenges with generative AI explainability
- Complexity and non-determinism: Generative AI models are highly complex, with a black-box nature and hidden patterns. Their outputs are unpredictable, often varying even when given the same input (due to stochastic sampling techniques).
- Opaque training and data influence: The training process is opaque, making it hard to trace which data points or patterns influence specific outputs. There is a lack of clear, linear input-output pathways, making it difficult to explain why models generate certain outputs, especially if they reflect biases.
- Causal ambiguity: Generative AI doesn’t follow straightforward, rule-based processes. Instead, it synthesizes learned patterns without clear causal links between in-puts and outputs, complicating attempts at causal explanations.
- Interpretability gap: Even when explanations are possible, they are too technical for users, particularly non-experts, to understand. This creates an "interpretability gap," where technical explanations fail to make the model’s behaviour meaningfully clear.
Amplifier - emergent behaviours
As models scale, they often develop unexpected abilities, such as performing tasks like translation or coding, even without explicit training. These emergent behaviours which may contain biases or discriminate are difficult to explain, as they weren’t an-ticipated by the model's developers, making it nearly impossible to understand how or why the model acquired them.
Loss of trust
If an AI system is perceived as biased, people will lose trust in its fairness and accuracy.
The challenges in explainability of generative AI systems in particular creates a trust gap. Without clear explanations for the behaviour of the generative AI systems, it becomes difficult to identify and mitigate biases in outputs; ensure accountability, especially in high-stakes domains like healthcare, law, or finance; or satisfy regulatory demands, such as those from GDPR or emerging AI regulations.
This can lead to a reluctance to adopt AI technologies in fields where they could be beneficial, such as healthcare or education.
- Gender-neutral language: Developers avoid gender bias by providing neutral or balanced responses.
- Partnerships with ethical AI researchers: Collaborations with AI ethics organisations to establish best practices and reduce bias.
- Industry standards: Adhering to industry guidelines on fairness and responsible AI development.
- Voice recognition and accessibility: Using inclusive voice recognition by training the AI system on diverse accents and speech patterns to reduce discrimination against non-native speakers; and, the AI system is designed to accommodate users with speech impairments.
- Guardrails and censorship of harmful content: Including prohibited content filtering where filters are used to block offensive or discriminatory content, and in uncertain situations, the AI system is programmed to give neutral responses to prevent bias or harmful information.
Transparency & explainability
AI systems should be transparent and explainable, helping stakeholders understand decisions and detect bias. New methods to make generative AI more explainable are required and are becoming available, such as:
• Post-hoc analysis: Trying to provide explanations after a model has generated its output, though this is still a developing area.
• Model distillation: Simplifying generative models to create more interpretable versions.
• Human-in-the-loop review: Involving (i) bias audits and annotation, where hu-man reviewers analyse the AI system's interactions to identify and correct biased or offensive responses, allowing developers to retrain the system with improved data; and, (ii) Feedback Loops where user feedback on biased responses helps re-fine the AI system’s behaviour and improve its responses over time.
However, the fundamental nature of how generative AI models function makes explainability a highly challenging, if not impossible, goal to fully achieve with current methods.
Pioneering solutions
1. Capisco AI
1. Capisco AI
1. Capisco AI
This innovative AI company is developing AI models that are:-
- Transparent
- Easier
- Powerful
- Safer
- More efficient
Transparency
1. Capisco AI
1. Capisco AI
Generative AI, especially large language models (LLMs), operates as a "black box," where inputs and outputs are visible, but internal processes are opaque. For example, an AI system trained on CVs of predominantly male employees might replicate gender bias, and addressing one bias could inadvertently introduce others. Transparency is cruc
Generative AI, especially large language models (LLMs), operates as a "black box," where inputs and outputs are visible, but internal processes are opaque. For example, an AI system trained on CVs of predominantly male employees might replicate gender bias, and addressing one bias could inadvertently introduce others. Transparency is crucial to understanding which factors influence the output and how. Capisco is transparent and able to explain its reasoning thus allowing flaws to be visible and so addressed.
Unbiased
1. Capisco AI
Corrigibility
Where an unwanted bias is detected it should be possible to remove it without having to rebuild the entire system. In the example above Capisco allows the straightforward removal of sex as a factor without rebuilding the entire system.
Corrigibility
Low carbon footprint
Corrigibility
Corrigibility is the ability to correct both the reasoning and the actions of an AI system. Capisco is corrigible in detail without the need to tear it down and start from scratch
Low carbon footprint
Low carbon footprint
Low carbon footprint
Due to the low data volumes used in training Capisco and the much more efficient mathematics used there are six orders of magnitude difference in terms of energy consumption.
No hallucinations
Low carbon footprint
Low carbon footprint
Because it is based on a well founded structured knowledge model Capisco doesn't have any subtle errors built in and so does not suffer from the hallucinations that other methods have built in.