Why the AI-Human Partnership is Important
In an age where artificial intelligence (AI) increasingly shapes our daily experiences, the question of how humans should work alongside these powerful systems has never been more urgent. As organisations rush to adopt AI solutions to stay ahead of the competition, a critical concern emerges: How do we harness AI’s remarkable capabilities while safeguarding human autonomy and societal values?
A recent commentary by ISB Professor Manish Gangwar, along with Praveen K Kopalle and Abhinav Uppal, offers valuable insights into this complex relationship.
Responding to a broader exploration of the transformative potential of AI, their analysis dissects three fundamental premises underpinning successful human-AI partnerships:
- The superiority of human-AI collaboration over either working alone
- The dependence of AI quality on training data
- The troubling phenomenon of AI “hallucinations”—when systems produce convincing but factually incorrect outputs
In this interview, which has been slightly edited for clarity, Professor Gangwar, Executive Director of the Institute of Data Science and Business Analytics at the Indian School of Business, discusses these critical dimensions of our increasingly AI-driven world and the delicate balance required to navigate its potential pitfalls.
Why is a human-AI collaboration superior to either working independently?
A partnership between humans and AI can outperform either working alone. Humans bring judgement, ethical reasoning, and contextual understanding—especially important in tasks involving empathy—while AI offers scale, speed, and data-processing capabilities. But these strengths must be combined thoughtfully. Human oversight is essential to ensure accuracy and accountability. In domains such as healthcare, marketing, or education, this complementarity can yield better outcomes than either side working solo.
How does AI’s effectiveness depend on the quality of its training data?
AI learns patterns from training data, so if the data is biased, incomplete, or flawed, the resulting outputs will reflect those problems. For example, in hiring, if historical data contains discriminatory practices, AI systems trained on that data may reproduce those biases. The same concern applies to applications that attempt to mimic human empathy, such as chatbots or recommendation engines. Without diverse and representative training datasets, AI may fail to respond appropriately in different cultural or emotional contexts.
What are the risks of AI “hallucinations”, and how significant are they?
Hallucinations refer to instances when AI systems produce outputs that appear plausible but are factually incorrect. These are especially common in complex models such as neural networks and large language models (LLMs).
While such errors might be benign in some contexts, they can be dangerous in areas such as healthcare, finance, or customer service—where incorrect outputs can mislead users or cause harm. The risk becomes even more serious when AI is used in empathetic roles, such as mental health support.
How might AI transform or potentially damage human connections?
As AI becomes more capable of mimicking human emotion, there is a risk that individuals may start forming bonds with AI tools instead of real people. At a time when social isolation is already growing, this could weaken the fabric of society. If AI substitutes for human companionship, especially among vulnerable populations, it could reduce genuine human connection. Therefore, AI companions should be designed to support—not replace—human relationships.