Credit, Collections & Insolvency News
It’s no secret that Artificial Intelligence (AI) and Machine Learning technology has made huge advances in recent years. In fact, 83% of businesses state that AI is a strategic priority for their business today and the growth of AI in the UK is high on the Government’s agenda, as outlined in this recent whitepaper.
As AI-based solutions change in complexity, sophistication and autonomy, a huge number of opportunities are opened up to both businesses and society as a whole. However, as these opportunities are created and AI becomes more prominent as a decision-making component of our daily lives, the need to explain how AI programs come to a decision is becoming more crucial than ever.
Particularly in the Risk sector, not only is it crucial to be able to explain the decisions made by AI models, it’s also important to be able to control them, understand the biases that may be built into underlying algorithms and how they may behave in different situations. There is a huge need for firms to focus on explaining and understanding AI models.
The ‘black box’ problem surrounding AI is already raising questions around how we can ensure that it is being applied in an ethical way, and this is (rightly) holding back more widespread use of AI systems in some areas. When the decisions that AI systems make affect people’s lives or health, having systems that are trusted and provide accountability for the decisions made has never been more crucial.
Why is this hindering AI adoption?
Due to the lack of explainability and control surrounding AI and machine learning, it is difficult to know how an algorithm arrives at its decisions and therefore, it is difficult to know when a mistake or error has occurred. If that decision determines whether a person is able to buy a house, receive treatment for cancer or be released from prison for example, then it’s critical that the basis for the decision is understood and stands to reason.
There is also a common misconception that AI is completely objective but, it is only objective in relation to the data fed into it. Machine learning relies heavily on data so if the data input is biased, then the outcome will also be biased. If care is not taken to prevent it, machine learning systems can encode and replicate extant human biases, potentially propagating them at greater scale. This makes it possible for sexism, racism and other forms of discrimination to be built into the algorithms of intelligent systems that shape how humans are categorised and communicated to.
What is explainable AI?
Most AI and machine learning systems are unable to explain the process or reasoning behind its decisions and, generally speaking, the inner workings of these systems are too complex for us to examine or rationalise.
Explainable AI is a field that is focused on shedding light on the way that AI systems make decisions. This might include any of the following:
In the light of recent regulation changes such as GDPR, business risk and ethical concerns, the use of explainable AI will play an increasing role.
Why is explainable AI so important?
A lack of visibility into how AI algorithms work leads to challenges, particularly in fields where the wrong decision being made can cause irreparable harm or where decision makers are required to provide explanations for the outcome.
In the worlds of finance, insurance and banking where the business must explain each and every decision taken by the model to both regulators and customers. There are also many real-world scenarios in which biased or incorrect models could have significant effects. In fields such as predicting potential criminality, and in credit scoring, fraud detection, loan assessment and self-driving cars, understanding of the model and interpretation are extremely important.
Where does AI fit into the Risk sector?
Being able to understand, explain and control the results generated by AI models is of particular importance in the Risk sector. Having the ability to understand why a customer has been rejected for a lending application is essential. Just as importantly within Risk is the need to control the model in the first place, ensuring that it doesn’t exhibit unwanted behaviours for any unexpected combination of data.
Historically, firms within the Risk sector have been prevented from fully adopting AI due to a lack of explainability and the highly regulated nature of the sector. However, recent advances mean that these barriers can now be overcome. These twin goals – control and explainability have now been solved. And so, when it comes to assessing credit risk, AI has the potential to bring about radical change.
As AI becomes more explainable, trust and confidence in its abilities builds, which should rapidly increase adoption rates. This in turn puts businesses in a strong position to innovate and stay ahead of competitors while being able to remain transparent and ethical. Explainable, controlled, more responsible AI will be the backbone of the intelligent systems of the future that enable the intelligent enterprise. Explainable AI won’t replace people but will complement and support them so they can make better, faster, more accurate and more consistent decisions.
To find out more about the future of AI, download Jaywing’s mini guide.
Martin Smith, Head of Product Development, Consulting, Jaywing
