Designing Delegation to Optimize Artificial Intelligence and Human Interaction

Even those who believe that humans will be obsolete in value chains must explore a transitory period when computers must still learn from humans. New work arrangements might facilitate the computers’ ability to examine billions of alternatives. At the same time, humans could contribute their ability to generate new alternatives from connecting otherwise unintelligible dots.

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Editor’s Note: Given the increasing discussion about and deployment of artificial intelligence (AI) offerings in support of data discovery and legal discovery tasks, the following two information pieces may be beneficial for informing and expanding the dialogue on AI and human interaction and workflow to optimize the unique characteristics of each of these essential elements of intelligence.

How Can Humans Work With Artificial Intelligence?

Extract from an article by Andreas Fügener, Jörn Grahl, Alok Gupta, and Wolfgang Ketter

We also would like to point out that the answer to questions like, “Who will win: humans or machines?” is clear. Considering current advances in computing, and acknowledging that human performance is not a serious upper bound or benchmark for many tasks, it is quite obvious that humans will be outperformed by computers in a vast majority of cases. This will also happen for tasks that currently appear demanding and require intuition and human experience. Pitching humans against AI emphasizes frictions that arise from the adoption of AI, and it supports a gloomy outlook on employment.

We believe that not enough attention has been paid to other possibilities. For example, could humans augment the capabilities of machines or vice-versa? Even those who believe that humans will be obsolete in value chains must explore a transitory period when computers must still learn from humans. New work arrangements might facilitate the computers’ ability to examine billions of alternatives. At the same time, humans could contribute their ability to generate new alternatives from connecting otherwise unintelligible dots.

A better question to ask might be, “How should humans and AI work together?” It is quite possible that in some work arrangements humans and AI working together outperform humans and AI working alone. Simple economics would then dictate that managers not replace the human with an AI, but let her work with the AI in a team. There are two requirements for this to happen:

  1. Humans and AI must have complementary skills (i.e., the humans must know things the AI does not, and vice versa)
  2. If there are complementary skills, the work must go to the party most competent to do it. In our research, we considered a simple model for this: We split the workload between humans and AI, and work can be moved to the other party through delegation.

Read the complete article at How Can Humans Work With Artificial Intelligence?

Collaboration and Delegation between Humans and AI: An Experimental Investigation of the Future of Work

Abstract from a study by Andreas Fügener, Jörn Grahl, Alok Gupta, and Wolfgang Ketter

A defining question of our age is how AI will influence the workplace of the future and, thereby, the human condition. The dominant perspective is that the competition between AI and humans will be won by either humans or machines. We argue that the future workplace may not belong exclusively to humans or machines. Instead, it is better to use AI together with humans by combining their unique characteristics and abilities. In three experimental studies, we let humans and a state of the art AI classify images alone and together. As expected, the AI outperforms humans. Humans could improve by delegating to the AI, but this combined effort still does not outperform AI itself. The most effective scenario was inversion, where the AI delegated to a human when it was uncertain. Humans could in theory outperform all other configurations if they delegated effectively to the AI, but they did not. Human delegation suffered from wrong self-assessment and lack of strategy. We show that humans are even bad at delegating if they put effort in delegating well; the reason being that despite their best intentions, their perception of task difficulty is often not aligned with the real task difficulty if the image is hard. Humans did not know what they did not know. Because of this, they do not delegate the right images to the AI. This result is novel and important for human-AI collaboration at the workplace. We believe it has broad implications for the future of work, the design of decision support systems, and management education in the age of AI.

Read the complete study at Collaboration and Delegation between Humans and AI: An Experimental Investigation of the Future of Work

Additional Reading

Source: ComplexDiscovery

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Business as Unusual? Eighteen Observations on eDiscovery Business Confidence in the Summer of 2020

The results of the recent Summer 2020 eDiscovery Business Confidence Survey present the unfortunate and continuing impact of COVID-19 on the business of eDiscovery. However, for these pandemic-driven results to be fully understood, they should be viewed through the contextual lens of the results of all nineteen surveys that have been administered to eDiscovery professionals since the inception of the eDiscovery Business Confidence Survey in early 2016.



Check Out the Observations Now!

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