top of page

Unlocking the Power of Explainable AI: Why Organisations Need It and How to Implement it



Introduction


Explainable AI is a hot topic, but it can be hard to understand the real benefits and how to implement them. This article will help you understand what explainable AI is, why is it important for your business, and how to offer it in order to improve trust.


Three stages of AI explainability


AI explainability is a process, not a product. The goal of explainable AI is to be able to explain what your model did, but that’s not something you can just create once and then put into production. Instead, the goal should be to build up a company’s ability to offer explainable AI over time through three stages:


Transparency: The ability to understand the inputs, processes, and outputs of an AI system. This includes understanding how the system makes decisions and what data it is using.


Interpretability: The ability to understand the reasoning behind the decisions made by an AI system. This includes understanding the logic and rules used by the system, as well as the factors that influenced its decision-making.


Explainability: The ability to communicate the reasoning and decision-making processes of an AI system to stakeholders in a way that is understandable and actionable. This includes providing clear explanations of the system's behaviour and its potential biases.


How to offer explainable AI


In order to offer explainable AI, your organisation should:

• Provide an explanation of the model. In other words, it should be possible for someone who is not familiar with machine learning algorithms or statistical techniques to understand how the algorithm made its predictions. This can be done by providing a list of features that were used and those that were not used in making predictions from data

• Provide a list of features that were removed from the dataset during feature selection,

• Provide a list of features that were added during feature selection

• Provide details about how each feature was coded (for example, whether it is categorical or continuous). These are a fee suggestions but they are not standard rules. Based on what the company is offering these can be changed.


The future of explainable AI


In the future, explainable AI for business users is likely to become increasingly important as more organisations adopt AI technologies to improve their operations and decision-making. Some possible developments in this area include:

• Greater use of explainable AI in business applications, such as customer service, fraud detection, and marketing.

• The development of more user-friendly interfaces that make it easy for business users to understand and interact with AI systems.

• Greater use of natural language processing and other techniques to make it easier for business users to communicate with AI systems and understand their decision-making processes.

• The use of explainable AI to help business users identify and mitigate bias in their decision-making processes.

• The development of tools and platforms that allow business users to easily train and deploy their own AI models. • Greater collaboration between AI researchers and business experts to help improve the explainability and usability of AI systems for business users.


Explainable AI is essential if you want to improve trust in your business.


Explainable AI can play an important role in improving trust in a business, as it allows stakeholders to understand the decision-making processes of AI systems and to identify and mitigate potential biases. When a business is able to provide clear explanations of how its AI systems work, and how decisions are made, it can help to increase the confidence of customers, partners, and regulators in the system and the business as a whole.


However, it's worth noting that explainable AI alone is not sufficient to build trust in a business, it also requires other factors such as transparency, accountability, and ethical behaviour. Furthermore, Explainable AI can be a complex and challenging field, so it may require significant investment in research and development, and expertise to implement it effectively.


Conclusion


To summarise Explainable AI can be an essential part of building trust in a business, but it is not the only factor that needs to be considered. Businesses should also focus on building transparency, accountability, and ethical behaviour in order to build trust with their stakeholders

1 view0 comments

Recent Posts

See All
p2.png

Collaboration

p3.png

User management by project

i1.png

CSV File

i2.png

Database

i3.png

API and more

r1.png

Auto Profile

r2.png

Compliance against rules

r3.png

Continuous monitoring

c1.png

Drag-drop data wrangling

c2.png

Upto 50 rules

c3.png

No manual work

e1.png

Drag drop data pipeline

e2.png

Custom workflows

e3.png

Aggregation and Sorting

a1.png

Automation of cleansing and engineering

a2.png

Download data

a3.png

Re-run with new/refreshed data

g1.png

Business Glossary with ownership

g2.png

DQ rules at the attribute level, tag cloud

g3.png

Data Lineage

s1.png

Slice and dice dashboard

s2.png

Publish dashboard

s3.png

Share dashboard

bottom of page