Machine Learning Q.A.
Consultancy services to support Responsible AI in your AI development
Machine Learning Explainable AI
Achieve Responsible AI with ML Quality Assurance consulting services using Machine Learning Explainability such as LIME, SHAP and EBM to expose embedded BIAS in your modelling and provide human understanding of proposed recommendations and decision making essential to Responsible AI.
We provide a parallel Machine Learning QA service to your model development, using proxy modelling to analyse your model’s decision making. This provides Responsible AI through an agile feedback loop on how your models are making their predictions or recommendations during development. Bias not only causes social responsibility issues, but also causes models to focus on the wrong features. So you to focus on intended outcomes without unintended consequences.
Responsible AI using ML Explainability Quality Assurance:
meet targeted outcomes
Most AI projects don't make it to production. An accurate model is no benefit if it focuses on the wrong issues. Make sure it is focusing on the face not the glasses.
Build trust
People don't trust what they don't understand. Showing users what factors contribute to predictions & recommendations builds trust in your AI solution.
Improve Acceptance
By explaining models in simple cause-effect terms and talking users language allows user involvement thru development that leads to greater acceptance in production.
MinimiSe Bias
All data has biased hidden in the relationship between features. Minimising bias by balancing data prevents discrimination and exposure to civil & regulatory claims.
Provide transparency
Not just for the EU GDPR "right to explanation" but for good governance, boards need to know the basis of machine learning decision making affecting staff and clients,
Drive accuracy
Models can be more granularity tuned for greater accuracy and performance when the key features driving outcomes are known and understood by developers.
Gaining Management Trust in AI
Unfortunately, high value AI models like Boosted Trees, Random Forests, and Neural Net, although good at predicting outcomes, are not easy to understand how the models reach their conclusions (commonly referred to as black-box solutions). This leads to a lack of trust by operational management, resulting in a poor take-up of AI.
AI insight breaks down this barrier to success by empowering managers with understanding the underlying drivers for strategic and operational informed decision making. This leads to greater confidence in Machine Learning models to predict the likely outcome based on changes in those factors.
How we go about it
Every data set is flawed! Not just high risk medical situations or loan approvals but even standard business problems like churning modelling. Now with AI Insights you can find these flaws and adjust for them to build more robust models as a result.
Utilising Microsoft Azure Machine Learning interpretation tools allows you to see exactly what an AI model learns, feature by feature, and really understand how it’s making its decisions.
It produces interactive plots to help you dive into this model and help you understand how it’s working starting with a ranking of what features an AI model would find important overall.
Extract from Microsoft Research demonstration of the Microsoft Machine Learning interpretation tools.
As a precursor to AI modelling
Understanding your data is the most important step in the path to developing correctly targeted Machine Learning models. AI Insight allows you to understand what questions you should be asking and what outcomes you really should be looking for.
or Explainability of existing models
If you aren't happy with the adoption rate of your existing modelling, or not seeing the promised returns, taking a step back to look at what you're working with and your goals, maybe worth considering.