If your business has set its sights on growing in the next few years, you are probably aware of the varying platforms and technology you’ll need to adapt to take you there. Growing companies are starting to rely on things like artificial intelligence (AI), Machine learning (ML), and Big Data technologies to help them realise their dreams of growth. Not only do these processes help to accelerate your technological strategy, but also help to streamline processes, identify opportunities, and enhance the products and services your business offers customers.Typically, when implementing these it is wise to implement an Explainable AI Framework.
However, despite these new platforms being incredibly beneficial to businesses of all shapes and sizes, they are not always the easiest tools to implement.
Historically, there can be a lot of pushback when new technologies are introduced. Employees can worry that they are being replaced or that their day-to-day work life will be disrupted by the technology.
When these models are processing personal data, businesses need to be extremely transparent and open about how such data is being used and stored. To do this, companies should adopt something known as AI explainability.
What Is Explainable AI?
Explainable AI, which can also sometimes be shortened to XAI, is the name for the tools and processes that are put in place to increase the success of AI platforms in the workplace.
By using explainable AI, you’re making it easier for your employees to understand what AI is, how it is being used, and, most importantly, how it relates to them and their own personal data.
Following the lead of other companies that have been through technological changes, you can create best practices and procedures that explain how certain AI models are trained and how they come to make certain decisions that may be different from those that a human would make.
Because AI and technology can be something that’s inherently hard to understand for many employees, AI explainability makes the systems easier to understand and will ultimately help more employees with their daily interactions with the technology.
Why Do You Need Explainable AI?
With the growth of AI in all areas of business, it’s become more important than ever for companies to take the implementation process seriously – which includes making sure that everyone within your firm understands AI and how it relates to them.
Businesses need AI explainability because it ensures the success of new AI technology in your business, which ultimately results in streamlined processes, higher returns on investment, more efficient collaboration within your teams, and higher revenue.
Not only does it result in material benefits for your business, but AI explainability will also help you to be more transparent with every party that is involved with the technology. People will feel more trusting when giving you their personal data, your employees will feel confident handling data effectively, and you’ll benefit from more insight into both your customers and employees as a result of implementing a strong AI explainability framework.
How Does AI Explainability Work?
AI explainability will look very different in different businesses – it all depends on the type of technology you plan to use, how you plan to use it, and the varying education levels of your employees.
For most AI explainability programs, businesses will focus on who they have to explain the model to, how accurate the explanation needs to be, and whether they need to explain the whole model or just the particular decisions that it will be making.
Depending on how much interaction your team has with the AI, they will have very different questions. Mostly they will want to know how the model was trained, what data of theirs was used, and whether there was any bias within the training that would need to be measured and mitigated.
Although explaining how a model works and how it relates to employees may sound simple enough, in theory, it can often be a lot harder in practice.
Finding the best way to incorporate AI explainability into your workflows should be something that you consider based on the unique requirements of your business and its operations.
How To Implement Explainable AI
If you want your AI implementation to be a success, AI explainability is essential. If you want to be compliant and make sure the adoption is beneficial to everyone, you should follow the following steps to ensure you’re following legislation:
- Have a thorough understanding of the data you’ll be processing and how it will be used
- Conduct assessments prior to adoption to understand the effects of data collection
- Make sure there are no biases that could get in the way of end results
- Appoint a Data Protection Officer who can manage the collection of personal data
- Have a deep understanding of your system and accountability requirements
- Be aware of any current, or pending, legislation that may impact your operations