The humble CAPTCHA is a good way to keep bots honest. CAPTCHA -- created in 1997 -- is a contrived acronym for "Completely Automated Public Turing test to tell Computers and Humans Apart. "It's a type of challenge/response test used in computing to determine whether or not the user is human," says Wikipedia. In the past, they were simple: a string of letters and numbers in different sizes and fonts. Nowadays, they often present as a grid of nine thumbnails, with instructions to check off all images of cars, bicycles, or crosswalks. No one is overly fond of this task -- we squint at our laptop screens, trying to figure out if a grainy image contains a traffic light or not. We complete the task, only to face another screen of thumbnails. What we're doing is essential, though. Computers are run by algorithms, while humans have real-world training. When we scan a street for motorcycles or crosswalks, we're not thinking of sun interference or how much of the image our retinas capture.

RPA and AI undoubtedly create efficiencies, but with those efficiencies comes an added human responsibility: monitoring results and ejecting, if not preventing, biases. It is clear that 2020 will bring forth a tipping point in enterprise adoption of smart automation – artificially intelligent software bots that work with human workers to automate manual, repetitive tasks. Prior to the COVID-19 pandemic, more than half of U.S. businesses were already using this technology in daily operations. As businesses and governments continue to respond to the pandemic and the economic aftermath, automation will be even more pivotal as the pace of maturity accelerates, and the global economy reacts to it. According to the McKinsey Global Institute, automation and advances in artificial intelligence (AI) will lead as many as 375 million workers, or roughly 14 percent of the global workforce, to reskill themselves by 2030 – more applicable than before as industries look to speed their recovery.

Abstract--Advanced automation is involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal, and there may exist a responsibility gap. Understanding human responsibility is particularly important when systems can harm people, as with autonomous vehicles or, most notably, with Autonomous Weapon Systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human interaction in automated systems and demonstrate its applications on decisions involving AWS. The analysis reveals that human comparative responsibility is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in advanced automation. Our responsibility model can guide system design decisions and can aid policy and legal decisions regarding human responsibility in highly automated systems. Financial markets largely function through algorithmic trading mechanisms [1, 2], semiconductor manufacturing is almost entirely automated [3], and decision support systems and aids for diagnostic interpretation have become part of medical practice [4, 5]. Similarly, in aviation, flight management systems control almost all parts of the flight [6, 7], and in surface transportation, public transportation is increasingly automated, and the first autonomous cars appear on public roads [8, 9]. Manuscript submitted October 30, 2018; (Corresponding author: Joachim Meyer) N. Douer with the Department of Industrial Engineering at Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel (email: nirdouer@mail.tau.ac.il).