Responsible AI in Banking
In a world, where AI is increasingly being used to automate tasks and make decisions, the banking sector is one area where responsible AI is critical.
Adopting Responsible AI in the banking industry is essential for creating an ethical, secure and robust digital financial system that caters to the needs of customers and businesses alike.
After all, banks handle our money — so we need to be confident that they are using AI in a responsible way.
Responsible AI is a set of principles that guide ethical decision-making for the development and use of Artificial Intelligence (AI). It emphasises on safety, fairness, transparency, accountability and privacy.
So what does responsible AI in banking look like?
In this article, we will explore key factors to be considered for the responsible use of AI detail.
Factors to consider on the responsible use of AI:
#1. Explainability:
It’s important to understand that explainability is not the same as transparency.
Transparency refers to whether or not you can see the data an AI system is using to reach its conclusions.
Explainability is about understanding *how* the AI system reached its conclusions.
Simply put, explainability is a measure of how easily we can understand the *reasoning* behind an AI system’s decisions.
Why explainability is important?
Make decisions about people:
First, when AI systems are used to make decisions about people (such as in credit scoring), it’s important that those people be able to understand how and why they were selected for whatever outcome they received.
Builds Trust:
Second, explainability can help build trust between humans and AI systems. If we don’t understand how an AI system arrived at a particular decision, we’re less likely to trust that decision.
Identifies Errors:
Finally, explainability can help us identify errors in an AI system’s reasoning so that we can correct them.
Challenges of explainability:
Explainability is a major issue in the development and use of AI systems, with experts breaking it down into three key components.
Identify what needs to be explained:
Firstly, banks must identify what needs to be explained in order to protect those whose lives may be negatively impacted by decisions made through AI.
When it comes to identifying what needs to be explained, banks should prioritise their efforts on areas that have the potential to negatively influence or impact people’s lives.
For example, algorithmic decision making tools used in financial services such as risk management or credit score assessments could drastically impact individual/s if deployed without proper explainability.
In order to maintain trust and reliability from a user perspective, ensuring that AI/ML models are properly explainable is critical for success.
Machine Learning models can change unexpectedly:
Secondly, machine learning models can change unexpectedly over time ‘learn’ or have new data inputted, which must be anticipated and prepared for.
While this process is part of their power, it can also introduce uncertainty into the model’s behaviour which may lead to undesirable outcomes.
By designing models with explainability in mind — such as through incorporating measures like fairness and transparency — potential issues can be spotted early on in development and addressed proactively before deployment.
Education and reaching consensus:
Lastly, Explaining AI/ML decisions requires education and reaching consensus between technologists, government leaders and business owners about its capabilities and limitations.
This allows for realistic expectations around explainability, transparency and accountability — all essential elements for establishing a successful AI strategy that users trust implicitly.
#2. Bias:
Bias is a major concern in AI systems because it can lead to incorrect and potentially dangerous decisions.
This is particularly true when AI systems are used for decision-making tasks such as credit scoring or fraud detection.
Types of bias:
There are several types of bias that can affect an AI system, such as selection bias, sample bias, and representational bias.
Selection bias occurs when a training dataset is not representative of the population it seeks to model;
Sample bias happens when data points are omitted from the dataset, resulting in an unbalanced representation of the population; and
Representational bias occurs when certain features have higher importance than others.
Preventing Bias:
To avoid bias in AI systems,
- Banks must adhere to ethical guidelines and ensure that they are creating datasets that accurately represent their target populations.
- Banks should also consider how algorithms are trained and deployed, taking into account data privacy laws and regulations, as well as the need for transparency and explainability.
- Additionally, banks should monitor the performance of their AI systems over time to ensure that any potential bias is quickly identified and addressed.
- Finally, Banks should regularly audit their AI systems to ensure they are compliant with existing laws and regulations.
By taking these steps, banks can create AI systems that are reliable, explainable and trustworthy but also explainable.
3. Diversity:
AI systems can also suffer from a lack of diversity. This occurs when AI models are trained with homogenous datasets that do not represent the populations they are designed to serve.
This issue is particularly relevant in fields such as healthcare, where AI can be used to diagnose and treat patients.
For example, an AI system trained on a dataset that does not represent the full spectrum of gender, race and ethnicity could lead to an inaccurate diagnosis for a patient.
Ensuring Diversity:
To ensure diversity in AI systems,
Diversity in the data:
First, banks must take steps to create datasets that are representative of their target populations.
Banks should also consider using methods such as data weighting or oversampling to ensure that data points from different populations are accurately represented.
Diversity in the Algorithm Design:
Second, banks should take steps to ensure that their algorithms are designed with diversity in mind.
This includes taking into account different groupings of data points and seeking out ways to ensure fairness across different groups.
It also means designing algorithms that are explainable and transparent, so that users have a clear understanding of how decisions were made.
Diversity in the Teams:
Last, banks should ensure that their teams are diverse, with people from a variety of backgrounds and with different experiences. This will help to ensure that potential biases in the data and algorithm design are identified early on and addressed appropriately.
By taking these steps, banks can create AI systems that are fair, equitable and unbiased. This will ensure trust in the systems, while also protecting users from potential bias and discrimination.
Conclusion:
As AI technology continues to advance, the challenge of explainability, preventing bias and ensuring diversity will become even more critical.
We must ensure that we are creating AI systems that accurately represent their target populations, while also adhering to ethical guidelines and protecting user privacy.
By taking the right steps, we can create AI systems that are reliable, explainable and trustworthy. This will ensure that AI technology can be used responsibly and ethically, while also providing real value to banks, their customers and society at large.
