07 February 2022 14:04:40 IST

Leveraging explainable AI for decision making

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Explainable Artificial Intelligence or XAI is an aggregation of methodologies and processes that facilitate humans to trust and comprehend the output and outcomes produced by machine learning algorithms. It describes an AI framework, its potential prejudices, and the expected impacts, and furthers transparency, fairness, and precision in AI-enabled decision making.

Unlike the “black box” concept in machine learning, XAI can explain outcomes of specific decisions. You might wonder why XAI matters, and what is the need for it. Here goes:

As companies’ chart out their strategies in artificial intelligence, explainability must hold the central reflection to protect against superfluous risks. But all along, AI frameworks have gained a name for notoriety due to their failures in lending visibility to how and why decisions are being made. It is here where XAI becomes important as it renders decisions that can be interpreted and perceived very well by the users. The visibility within XAI facilitates users to evaluate their algorithms and data for deviation and prejudices thereby creating robust and precise outcomes that regulators, clients and customers can conveniently understand.

For example, several implementations in artificial intelligence — specifically within the financial and healthcare domains —are concerned with personal data. It is important for the clients to feel safe while sharing this data with companies. With XAI, organisations will have access to enhanced transparency and explainability in all the aspects of their operations.

XAI framework helps business entities to display the origin of data. Besides, businesses can use XAI to perceive AI frameworks better and utilise them for important aspects such crucial scenario planning for eventualities.

How XAI works?

XAI aims to generate a set of techniques in machine learning which have the potential to create more powerful explainable models, and at the same time, maintain an optimum level of prediction accuracy. When using the models of machine learning in any decision making or automation process, it becomes inevitable to perceive how and why the predictions are being made. XAI utilises an explainable framework with an interface for explanation that helps the users perceive the model.

Graphics by V Visveswaran

Applied in myriad fields

XAI is adaptable and extremely versatile, with varied applications right from drug revelation, patient information investigation, to fraud and scam recognition, client commitment, and work process streamlining. Inside medical care, a machine utilising XAI could save the clinical staff a lot of time, permitting them to zero in on the interpretive functions of medication, rather than going through with an assignment that is repetitive. They could thus attend to more patients and lend them more consideration.

In the manufacturing domain, the processing of natural language, enabled by XAI, can assist with examining unstructured information. For example, gear manuals and support principles, and sensor readings in IoT.

Insurance, as a sector can be identical to the healthcare and medical services, where XAI can have expansive effects, such as, in enhanced compliance and risks, policy adjustments, cross-selling events, customer and client servicing, underwriting, claims prevention activities, productivity of agents, and acquisition of clients and customers.

Moreover, XAI possesses unimaginable prowess in electrifying the automobiles sector and the emerging field of self-driven units. In the administration sector inside the organisation, automakers can follow the informational collection by following and clarifying how the model went from choice guide A towards point Z, subsequently making it simpler for them to evaluate whether these results guide to the moral position they mean to undertake.

Challenges in XAI

Most novel research and work in XAI emanate from the field of academics where the data scientists are working upon innovations to overcome the challenges that XAI faces such as confidentiality, complexity, and transparency. For this, they must display a robust perception of the intricacies of the models at work and need to find answers to questions such as — Does the output of the model make sense? Does the explanation make sense? Is there any bias in the model?

Also, users who are non-technical and business teams such as executives, managers, lawyers, cops, judges, bankers, and professionals in other streams need to realise the manner with which they can leverage upon the XAI explanations for real-time applications and commercial gains. For the research in XAI to proceed in the right direction, it becomes imperative for researchers and data scientists to collaborate with business teams and non-technical users for effective evaluation and proven outcomes. 

Career opportunities

The future of XAI, job opportunities and demands, and what students can focus on:

Probably the most captivating AI trials and research that could have repercussions for the near future is occurring in two regions: reinforcement learning, which bargains in remunerations and discipline rather than marked information; and GAN or, generative adversary networks, that permit algorithms to make instead of simply surveying by setting two nets in opposition to one another.

On a far more fabulous scale, AI is ready to majorly affect environmental change, sustainability, and ecological issues. Preferably, and somewhat using complex sensors, urban communities will turn out to be less polluted and clogged. Demands and opportunities galore in this realm for students. The application of XAI protocol to assess big data is already the next big thing for budding professionals to pursue. The Internet of Things (IoT), deep learning aided by explainability and reinforcement learning of the XAI, is set to spread its tentacles on each aspect of organisational dynamics. The concept of probabilistic programming can have wide implications from financial forecasting to customer service operations.

XAI-enabled automation within robotic processes can revolutionise the global supply chain aiding delivery and logistics, payment service functions, consumer behaviour prognosis, detection of intrusion, creation of content, digital marketing and much more.

XAI epitomises the great advancement that AI has made and does present opportunities for a diverse occupation to erect applications based on artificial intelligence that is fair, transparent, and reliable. For the XAI to be really smart, it has to honour human values like privacy. The widespread potential of XAI can have far-reaching implications for numerous verticals across industries.

(The writer is Associate Professor, Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur.)