Care to explain? Providing effective rationalisation and explanation to this question is critical in the business world. Artificial Intelligence (AI) has opened up opportunities to boost productivity and innovation and fundamentally transform operating models. When decisions derived from (AI) and machine learning (ML) affect humans’ lives, there is a need to explain and understand how such decisions are furnished by AI methods, since the end-user will be reluctant to trust the predictions without contextual proof and reasoning.
To have confidence in the outcomes, secure stakeholder trust and ultimately capitalise on
the opportunities, it is necessary to understand the rationale of how the algorithm arrived at its decision – Explainable AI.
Algorithms are complex and efficient, at the same time opaque and non-intuitive.
While AI models are increasingly used in data management, replacing human decision-making, most are not able to explain why they reached a specific recommendation or a decision. This is where Explainable AI comes in.
Explainable AI (XAI) is about generating an explanation of data models in terms of accuracy, attributes, model statistics and features in natural language.
Without acceptable explanation, auto-generated insights or “black-box” approaches to AI cause concerns about regulation, reputation, accountability and model bias.
According to Gartner, “XAI increases the transparency and trustworthiness of AIsolutions and outcomes, reducing regulatory and reputational risk. It’s the set of capabilities that describes a model, highlights its strengths and weaknesses, predicts its likely behaviour and identifies any potential biases”.
Gartner forecasts a strong future for Explainable AI — by 2023, over 75 per cent of large organisations will hire artificial intelligence specialists in behaviour forensic, privacy and customer trust to reduce brand and reputation risk.
Also Read: Implementing Machine Learning for Data Analysis
Explainable AI for business
XAI helps companies in a secure manner, and offers enterprise value in its own right.The greater the confidence in the AI, the faster and more widely it can be deployed.
Compliance: Provides an auditable database of all the predictive factors.
Research areas Transparency: Provides transparency at local prediction level in weighted factor order to display the drivers for the decision
Ethical use of data: Reveals if surrogates for sensitive data, such as geographical, educational and occupational, result in similar exclusions.
Apart from addressing pressures such as regulation, and adopting good practices around accountability and ethics, there are significant benefits to be gained from investing in explainability. These include building trust – using XAI systems provides greater visibility over unknown vulnerabilities and flaws and can assure stakeholders that the system is operating as desired. XAI can also help to improve performance, understanding why and how an AI model works enables businesses to fine tune and optimise the model.
In terms of value for business applications, XAI benefits organisations in areas such as marketing, fraud, and detection of anomalies.
- Predicts customer needs and rationale for more precise personalised recommendation. More personalised recommendations increase conversions.
- Provides the driving factors for predicted behaviour outcomes and increased sales conversion rates
- In customer analysis, it can predict and reveal why customers will behave as predicted. Make actional predictions for the real world.
- In fraud detection and risk analytics, it can discover the factors associated with theft. Reduce false positives, enable additional verification stages. Disclosure why some should receive or deny credit
- In anomaly detection, it can predict and reveal the rationale behind anomaly
Also Read: Top 5 Trends In Data And Analytics
When developing a ML model, businesses should consider interpretability as an additional design driver that can improve its implementation for three reasons — veracity, trustworthiness and impartiality.
A multifaceted concept, XAI is also at the intersection of several areas of research in ML and AI, and one of the prominent research programs at DARPA. It’s expected to enable “third-wave AI systems”, where machines comprehend the context and environment in which they operate, and over time build core explanatory models allowing them to depict real-world phenomena.
The need for explainability is increasing. In sectors such as financial services, the use of advanced AI is already so well entrenched that risks should be at the top priority. Other sectors such as healthcare are following suit. And, as AI continues to permeate through the economy, all sectors would eventually need to judge the criticality and impact of their AI on the one side and how much faith they have in the outcomes on the other.
There are no perfect methods, and some problems are inherently not semantically understandable, but most business problems are amenable to some degree of explanation that de-risks AI, builds trust, and reduces overall enterprise risk. Making XAI a core competency and part of your approach to AI design will pay dividends today and in the future.
