Responsible and Explainable AI
AI brings potentially huge opportunities to reduce costs, improve decision making and gain better insight into customer behaviour. But there's very little awareness of some of the challenges associated with AI.
The $320 billion question: Can you trust your AI?
Artificial intelligence (AI) is a transformational $320 billion opportunity in the Middle East. Yet, as AI becomes more sophisticated, more and more decision making is being performed by an algorithmic ‘black box’. To have confidence in the outcomes, cement stakeholder trust and ultimately capitalise on the opportunities, it may be necessary to know the rationale of how the algorithm arrived at its recommendation or decision – ‘Explainable AI’. Yet opening up the black box is difficult and may not always be essential. So, when should you lift the lid, and how?
There is anxiety in the Middle East about the progress of technology: 75% of CEOs regard “the speed of technological change” as a business threat, second only to “changing consumer behaviour” (79%).
Emerging frontier
The emerging frontier of AI is Machine Learning (ML). Through it, a variety of ‘unstructured’ data forms including images, spoken language, and the internet (human and corporate ‘digital exhaust’) are being used to inform medical diagnoses, create recommender systems, make investment decisions and help driverless cars see stop signs.
Operating in the dark
The central challenge is that many of the AI applications using ML operate within black boxes, offering little if any discernible insight into how they reach their outcomes.
For relatively benign, high volume, decision making applications such as an online retail recommender system, an opaque, yet accurate algorithm is the commercially optimal approach. However, the use of AI for ‘big ticket’ risk decisions in the finance sector, diagnostic decisions in healthcare and safety critical systems in autonomous vehicles are of high stake for businesses and society, requiring the decision taking AI to explain itself.
Benefits of Explainable AI
There are significant business benefits of building interpretability into AI systems. As well as helping address pressures such as regulation, and adopt good practices around accountability and ethics, there are significant benefits to be gained from being on the front foot and investing in explainability today.
The greater the confidence in the AI, the faster and more widely it can be deployed. Your business will also be a stronger position to foster innovation and move ahead of your competitors in developing and adopting new generation capabilities.
- Optimise
- Retain
- Maintain
- Comply
Optimise
Model performance
One of the keys to maximising performance is understanding the potential weaknesses. The better the understanding of what the models are doing and why they sometimes fail, the easier it is to improve them. Explainability is a powerful tool for detecting flaws in the model and biases in the data which builds trust for all users. It can help verifying predictions, for improving models, and for gaining new insights into the problem at hand. Detecting biases in the model or the dataset is easier when you understand what the model is doing and why it arrives at its predictions.
Decision making
The primary use of machine learning applications in business is automated decision making. However, often we want to use models primarily for analytical insights. For example, you could train a model to predict store sales across a large retail chain using data on location, opening hours, weather, time of year, products carried, outlet size etc. The model would allow you to predict sales across my stores on any given day of the year in a variety of weather conditions. However, by building an explainable model, it’s possible to see what the main drivers of sales are and use this information to boost revenues.
Use Case Criticality
Explainable AI works in conjunction with PwC’s overarching framework for the best practice of AI: Responsible AI, which helps organisations deliver on AI in a responsible manner. Upon evaluation of the six main criteria of use case criticality, the framework will recommend for new use cases the optimal set of recommendations at each step of the Responsible AI journey to inform Explainable AI best practice for a given use case. Whilst for existing AI implementations, the outcome of the assessment is a gap analysis showing an organisation’s ability to explain model predictions with the required level of detail compared to PwC leading practice and the readiness of an organisation to deliver on AI (PwC Responsible AI, 2017).
The total of the economic impact of a single prediction, the econmic utility of understanding why a single prediction was made, and the intelligence derived from a global understanding of the process being modeled.
The number of decisions that an AI application has to make e.g. two billion per day versus three per month.
The robustness for the application, it's accuracy and ability to generalise well to unseen data.
The regulation determining the acceptable use and level of functional validation needed for a given AI application.
How the AI application interacts with the business, stakeholders, and society and the extent a given use case could impact business reputation.
The potential harm due to an adverse outcome resulting from the use of the algorithm that goes beyond the immediate consequences and includes the organisational environment: executive, operational, technology, societal (including customers), ethical, and workforce.
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