Over at the MIT Sloan Management Review, H. James Wilson, Paul R. Daugherty, and Nicola Morini-Bianzino strike an optimistic note as they respond to the “distressing picture” created by “the threat that automation will eliminate a broad swath of jobs across the world economy [for] as artificial intelligence (AI) systems become ever more sophisticated, another wave of job displacement will almost certainly occur.” Wilson, Daugherty, and Morini-Bianzino suggest that we’ve been “overlooking” the fact that “many new jobs will also be created — jobs that look nothing like those that exist today.” They go on to identify three key positions:
This first category of new jobs will need human workers to teach AI systems how they should perform — and it is emerging rapidly. At one end of the spectrum, trainers help natural-language processors and language translators make fewer errors. At the other end, they teach AI algorithms how to mimic human behaviors.
The second category of new jobs — explainers — will bridge the gap between technologists and business leaders. Explainers will help provide clarity, which is becoming all the more important as AI systems’ opaqueness increases.
The final category of new jobs our research identified — sustainers — will help ensure that AI systems are operating as designed and that unintended consequences are addressed with the appropriate urgency.
Unfortunately for these claims, it is all too evident that these positions themselves are vulnerable to automation. That is, future trainers, explainers, and sustainers–if their job descriptions match the ones given above–will also be automated. Nothing in those descriptions indicates that humans are indispensable components of the learning processes they are currently facilitating.
Consider that the ’empathy trainer’ Koko is a machine-learning system which “can help digital assistants such as Apple’s Siri and Amazon’s Alexa address people’s questions with sympathy and depth.” Currently Koko is being trained by a human; but it will then go on to teach Siri and Alexa. Teachers are teaching future teachers how to be teachers; after which they will teach on their own. Moreover, there is no reason why chatbots, which currently need human trainers, cannot be trained–as they already are–on increasingly large corpora of natural language texts like the collections of the world’s libraries (which consist of human inputs.)
Or consider that explainers may rely on an algorithmic system like “the Local Interpretable Model-Agnostic Explanations (LIME), which explains the underlying rationale and trustworthiness of a machine prediction”; human analysts use its output to “pinpoint the data that led to a particular result.” But why wouldn’t LIME’s functioning be enhanced to do the relevant ‘pinpointing’ itself, thus obviating the need for a human analyst?
Finally, note that “AI may become more self-governing….an AI prototype named Quixote…can learn about ethics by reading simple stories….the system is able to “reverse engineer” human values through stories about how humans interact with one another. Quixote has learned…why stealing is not a good idea….even given such innovations, human ethics compliance managers will play a critical role in monitoring and helping to ensure the proper operation of advanced systems.” This sounds like the roles and responsibilities for humans in this domain will decrease over time.
There is no doubt that artificial intelligence systems require human inputs, supervision, training, and maintenance; but those roles are diminishing, not increasing; short-term, local, requirements for human skills will not necessarily translate into long-term, global, ones. The economic impact of the coming robotics and artificial intelligence ‘revolution’ is still unclear.