'Burnout is becoming increasingly common in responsible AI teams.'

Responsible and ethical artificial intelligence has become the hot-button issue of our times, especially as AI seeps into every aspect of decision making and automation. 35% of companies now report using AI in their businesses and 42% are exploring the technology, as reported by ZDNET. 

The same survey by IBM finds that trust is extremely important -- four in five respondents cite being able to explain how their AI arrived at a decision as important to their business. 

However, AI is still code -- ones and zeros. It doesn't carry the empathy and often is missing context, as I and my co-author Andy Thurai, strategist with Constellation Research, explained in a recent Harvard Business Review article. 

It has the potential to deliver biased and harmful results. As AI moves up the decision chain -- from simple chatbots or predictive maintenance to assisting executive or medical decisions -- there needs to be a reckoning. 

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That is, AI's developers, implementers, users, and proponents need to be able to show their work, explain how decisions are made, and be able to continually adapt to new scenarios.

Responsible AI, however, is not easy. It means pressure -- especially on AI teams. As Melissa Heikkilä points out in MIT Technology Review, "Burnout is becoming increasingly common in responsible AI teams." The largest organizations have "invested in teams that evaluate how our lives, societies, and political systems are affected by the way the

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The speed of AI adoption in recent years has ratcheted up the pressure to intense levels. AI has moved from the lab to the production level "faster than expected in the last few years," says Thurai, who has been a vocal advocate for responsible AI. Managing responsible AI "could be particularly draining if they are forced to moderate content, decisions, and data that are biased against their beliefs, viewpoint, opinions, and culture, while trying to maintain a fine line between neutrality and their beliefs. Given the fact AI works 24x7x365 and the decisions made by AI sometimes are life-changing events, the humans in the loop in those areas are expected to keep up with that which can lead to burnout and exhaustion, which can lead to error-prone judgments and decisions."

se systems are designed, developed, and deployed." For small-to-medium companies and startups, it means these responsibilities fall to developers, data engineers, and data scientists. 

The result -- even at the largest companies -- is that "teams who work on responsible AI are often left to fend for themselves," Heikkilä finds. "The work can be just as psychologically draining as content moderation. Ultimately, this can leave people in these teams feeling undervalued, which can affect their mental health and lead to burnout .