Importance Of Ethical AI: Why Responsible AI Practices Are A Must In Digital Age

Responsible AI practices should ideally include five pillars of ethical AI comprising: fairness, transparency, accountability, privacy, and robustness.

Ethical AI is increasingly becoming a part of the corporate vision and strategy of most businesses.

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By Tarun Dua

As the use of artificial intelligence (AI) and machine learning (ML) expands to medicine, human resources, service businesses, media and other industries, it is important to ensure trust in AI systems to mitigate malfunction and bias, and increase the credibility of AI systems. 

Why Is Ethical AI Important?

With Deep Learning and Generative AI, we have moved from building rule-based systems that used to make decisions based on a predefined set of rules to probabilistic deep learning networks that are trained on massive amounts of data, with millions of parameters. These operate like a "black box" making it hard to pinpoint the reasons behind their decisions. Responsible AI practices should ideally include five pillars of ethical AI comprising: fairness, transparency, accountability, privacy, and robustness. 

Fairness ensures unbiased outcomes, and is ensured through diversity in data, bias detection and mitigation, and constant monitoring and feedback ;
Transparency focuses on explainability and is achieved through the use of interpretable models and AI that can be explained. Accountability holds individuals responsible and can be built through creating user feedback mechanisms, using Explainable AI systems, and regular monitoring of performance. Privacy safeguards personal data, and to achieve this, AI systems need to ensure data anonymization and privacy by design. Robustness ensures reliability and safety and is attained through training the model to perform well under various conditions and resist adversarial attacks. 

These principles can be used by companies developing or implementing AI systems as a guide in the development and use of AI to promote ethical practices.

Many Stakeholders In AI Systems

On the one hand, business leaders need to ensure that their AI systems are functioning reliably and accurately and that they can trust the data being used. On the other hand, organizations also need to build trust with their customers, suppliers and policy-makers. These external stakeholders would like to know when they are interacting with AI, what kind of data it is using, and for what purpose. Regulators are also looking for AI to have a net positive impact on society, and they have begun to develop enforcement mechanisms for human protection and freedoms.

Ethical AI And Indian Ecosystem 

In order to ensure that AI development and deployment in India takes place in an ethical and responsible fashion, IIT Madras launched the Center for Responsible AI (CeRAI) on 15th May 2023. NASSCOM has also launched a Responsible AI initiative to create course material, upskilling programs, and toolkits to help businesses. 

In recent years, our platform has been a provider of advanced Cloud GPUs to startups, businesses and university labs to train Machine Learning and Generative AI workloads – and we have witnessed active efforts by developers and corporates to reduce bias in data, while choosing AI models that are more interpretable, and build more responsible AI practices. In fact, ethical AI is increasingly becoming a part of the corporate vision and strategy of most businesses – typically including elements of fairness, reliability, safety, privacy/security, inclusiveness, transparency, and accountability. Over the next few years, as adoption becomes more widespread, Ethical AI will play an extremely important role in building trust and accountability in AI systems amongst all stakeholders.

(The author is the CEO of E2E Networks Ltd, an accelerated Cloud computing platform)

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