AI with a Conscience, India’s Journey Toward Ethical and Responsible Innovation

India is rapidly adopting artificial intelligence (AI) across industries, and the need for responsible AI is imperative. Ethical considerations such as privacy, security, fairness, and transparency are central to building AI strategies. The government’s IndiaAI mission underscores the importance of trustworthy AI in India’s digital future. Businesses are now adapting, and 42% of the companies surveyed are dedicating over 10% of their AI budgets to responsible initiatives. Despite advancements in machine learning, deep learning, and generative AI, concerns about data misuse, bias, and accountability persist.

How can we keep AI under control so that it doesn't hurt, deceive, or misinform people? How can we make sure that the AI models being incorporated into goods don't violate copyright, introduce bias, or negatively impact people's ability to make a living? How can we give AI the autonomy and self-sufficiency it needs while safeguarding businesses and customers at the same time?

These are questions that are not so straightforward to answer, especially as the value of AI seemingly increases and potential use cases multiply. The good news is these are among the questions that many AI engineers, academics, legal experts, policy makers, and business leaders are actively sorting through as new regulations seek to balance responsible AI with innovation. But before we can address the question of the day, how can companies put responsible AI into practice?  We first need to ask and answer another question: What is responsible AI?

Can we define ‘Responsible AI’?

Many different definitions generally align, but the International Organization for Standardization (ISO) provides a solid base-level definition. ISO states that “Responsible AI is an approach to developing and deploying artificial intelligence from both an ethical and legal standpoint. The goal is to employ AI in a safe, trustworthy, and ethical way. Using AI responsibly should increase transparency while helping reduce issues such as AI bias.”

Though the intent of responsible AI is pretty straightforward, putting this theory into practice is where stakeholders struggle to find consensus. As Tess Valbuena, interim CEO of Humans in the Loop, has said, the need for AI oversight – and the magnitude of oversight – is not as objective as many would probably like it to be.

Along with companies and individuals, governments are also collaborating to develop responsible AI frameworks and determine how to comply with the responsible AI ethics standards and oversight processes. Professional licensing boards, government regulatory bodies, and other standards organizations are already making an effort to offer guiding frameworks.

According to reports, the European Union and India have reaffirmed their commitment to creating AI that is trustworthy, safe, secure, human-centered, sustainable, and responsible, with the goal of advancing these values internationally. The India AI Mission and the European AI Office will strengthen their collaboration to promote an innovation ecosystem and share knowledge on unresolved research issues in reliable AI. They will work together on cooperative projects to develop ethical AI frameworks and tools, utilizing AI to advance human growth. This collaboration expands on current research and development initiatives in bioinformatics, climate change, and high-performance computing for natural hazards. Additionally, India and the EU will enhance cooperation in large language models to drive innovation and tackle shared AI challenges.

Putting a definition into practice in the workplace

Businesses can implement ethical AI processes by guaranteeing human oversight, ethical sourcing, and transparency. They must understand the sources of training data, check AI providers for ethical standards and intentions, and protect sensitive data. Verifying AI outputs helps avoid hallucinations, and incorporating human judgment (HITL, also known as Human-In-The-Loop) increases AI reliability. Third-party data use requires explicit consent that complies with intellectual property and privacy laws. Because proper attribution separates authorship from ownership, it improves transparency in AI-generated content.

Keep in mind that ethical AI activities go beyond just compliance. They are about integrity – about one’s character and (more broadly) culture. And while additional training and supplemental procedures are appropriate to address the nuances of AI and generative AI (GenAI), organizations should have a strong set of corporate ethics that serve as a backstop.

Author: Subramaniam Thiruppathi, India & Sub-continent Head, Zebra Technologies

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