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Explainable AI for cooperation with humans.

Explainable AI makes both humans and machines perform better

We have been introduced to AI and GenAi. Now it’s time to learn a new abbreviation: XAI, short for “explainable AI”. It will make both humans and machines perform at their best. By demystifying the “how” and “why” behind each algorithmic recommendation, XAI helps bring out the best of human problem-solving capabilities powered by AI-driven insights, argues three researchers n a blog post for World Economic Forum.

Explainable AI works as an interpreter that bridges the gap between complex algorithmic processes and human understanding. Much like how an interpreter can make complex information accessible to a layperson, XAI demystifies the intricate logic of complex algorithms, write professor Stefan Feuerriegel, Institute of AI in Management, LMU Munich, professor Torbjorn Netland, ETH Zurich and CEO Julian Senoner, EthonAI.

“The popular debate should not be stuck in a ‘humans versus AI’ narrative, but instead explore the abundant opportunities that the synergy offers. Our research underscores that collaborations between AI and humans can be especially effective when solving problems that neither could tackle alone.”

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“We thus call for a paradigm shift: rather than “replacing” humans with AI, it will be necessary to “augment” humans. This requires a completely different set of tools, and we deem one to be particularly relevant: XAI.”

“Artificial intelligence has revolutionized the world of work, but the adoption of AI in settings such as manufacturing has been slow. Explainable AI, or XAI, can help address ‘algorithm aversion’ by providing insights into decisions made, thereby building trust.”

The adoption of AI in various work settings, such as manufacturing, has been slow. One key reason is that humans are often averse to trusting opaque algorithms. While the technical advances in AI are well-documented and subject to much hype, behavioural factors effectively hinder AI’s large-scale adoption in manufacturing.” 

The report argues that explainable AI (XAI) is needed to clear the roadblocks. AI holds the potential to augment human intelligence to solve work tasks more effectively, while also enriching the work experience. 

“The key to unlocking this potential lies in explainable algorithms, which contrast the often-opaque decision-making processes in conventional AI systems.”

The authors argue that explainable AI (XAI) offers a significant breakthrough: ”Our multi-year research journey in the manufacturing sector leads us to a unifying conclusion: explainability is the missing ingredient that catalyses AI adoption in manufacturing.”

“By transforming the AI’s ‘black box’ into recommendations with clear explanations, XAI fosters greater trust and enables more effective human-AI collaboration. Not only is XAI making AI more ethical and accountable, but our research shows that it also improves work experience and job performance.”

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“While AI has the capacity to sift through massive datasets and identify patterns far beyond human capability, it is the symbiosis of human expertise and AI recommendations that truly unlocks productivity gains.”

“Our research strongly suggests that the future of manufacturing is not a battle of humans versus machines, but rather a collaborative enterprise that leverages the unique strengths of both.”

“Many work tasks will not and cannot be replaced or delegated to an AI. However, AI can augment human work and create more effective and efficient tasks – especially when the AI’s decisions are explained.”

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