There's another Hacker News discussion about a related topic, a paper Ben Shneiderman wrote in 1993, "Beyond Intelligent Machines: Just Do It".

https://news.ycombinator.com/item?id=22742100

http://www.cs.umd.edu/hcil/trs/93-03/93-03.html

>It seems that you can hardly go to a computer conference without seeing a videotape of a futuristic computer system that talks to you from a wall, desk, or some random appliance. Is this the interface of the future? Over the last decade, Ben Shneiderman, head of the University of Maryland's Human-Computer Interaction Laboratory and author of Designing the User Interface: Strategies for Effective Human-Computer Interaction (Addison-Wesley, 1992), has been the most forceful voice against anthropomorphic interfaces. He argues that users want a sense of direct and immediate control over computers that differs from how they interact with people. He presents several examples of these predictable and controllable interfaces developed in the lab at UM.

Ben Shneiderman and his colleagues like Richard Potter, Andrew Sears, and Catherine Plaisant at his HCIL lab have performed a lot of research and user interface design with touch screens, like the "lift off" high precision pointing strategy, and early touch screen keyboards.

An experimental evaluation of three touch screen strategies within a hypertext database

https://www.tandfonline.com/doi/abs/10.1080/1044731890952595...

Clocks, calendars and schedulers on a touchscreen (1988, HCIL)

https://www.youtube.com/watch?v=kzMHqF9Pkv4

Touchscreen keyboards - Andrew Sears, Ben Shneiderman. Human-Computer Interaction Lab, University of Maryland.

https://www.youtube.com/watch?v=JDRKP2ATBRg

And he just dropped by HN to announce a new paper he's just published on a similar topic, the result of years of work, which also relates to this discussion:

Shneiderman, Ben (2020). Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy, International Journal of Human-Computer Interaction,36, 6 (Published Online March 27, 2020).

https://doi.org/10.1080/10447318.2020.1741118

Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy

>Abstract

>Well-designed technologies that offer high levels of human control and high levels of computer automation can increase human performance, leading to wider adoption. The Human-Centered Artificial Intelligence (HCAI) framework clarifies how to (1) design for high levels of human control and high levels of computer automation so as to increase human performance, (2) understand the situations in which full human control or full computer control are necessary, and (3) avoid the dangers of excessive human control or excessive computer control. The methods of HCAI are more likely to produce designs that are Reliable, Safe & Trustworthy (RST). Achieving these goals will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility.

>Introduction

>This paper opens up new possibilities by way of a two dimensional framework of Human-Centered Artificial
Intelligence (HCAI) that separates levels of automation/autonomy from levels of human control. The new guideline is to seek
high levels of human control AND high levels of automation,
which is more likely to produce computer applications that are
Reliable, Safe & Trustworthy (RST). Achieving these goals, especially for complex poorly understood problems, will dramatically
increase human performance, while supporting human selfefficacy, mastery, creativity, and responsibility. This paper
focuses on the three RST goals, which may help to achieve
other important goals such as privacy, cybersecurity, resilience,
social justice, human dignity, and environmental preservation.

>The traditional belief in computer autonomy is compelling
for many artificial intelligence (AI) researchers, developers,
journalists, and promoters. I assume a broad definition of AI
to include automated/autonomous systems using technologies
such as machine learning, neural nets, statistical methods,
recommenders, adaptive systems, and speech, facial, image,
and pattern recognition.

>The goal of computer autonomy was central in Sheridan
and Verplank (1978) ten levels from human control to computer automation/autonomy (Table 1). Their widely cited
one-dimensional list continues to guide much of the research
and development, suggesting that increases in automation
must come at the cost of lowering human control. Shifting
to HCAI could liberate design thinking so as to produce
computer applications that increase automation, while amplifying, augmenting, enhancing, and empowering people to
innovatively apply systems and creatively refine them.

>Sheridan & Verplank’s ten levels of automation/autonomy
have been widely influential (Sheridan, 1992), but critics
suggested refinements such as the four stages of automation:
(1) information acquisition, (2) analysis of information, (3)
decision or choice of action, and (4) execution of action
(Parasuraman et al., 2000). These stages refine discussions of
each of the levels, but the underlying message is that the goal is
full automation/autonomy.

>Even Sheridan (2000) commented with concern that “surprisingly, the level descriptions as published have been taken more
seriously than were expected” (see Hoffman & Johnson (2019) and
Kaber (2018) for detailed histories). However, in spite of the many
critiques, the 1-dimensional levels of automation/autonomy, which
only represents situations where increased automation must come
with less human control, is still widely influential. For example, the
US Society of Automotive Engineers adopted the unnecessary
trade-off in its six levels of autonomy for self-driving cars
(Brooks, 2017; Society of Automotive Engineers, 2014) (Table 2).

>Critics of autonomy have repeatedly discussed the ironies
(Bainbridge, 1983), deadly myths (Bradshaw et al., 2013;
Mindell, 2015), conundrums (Endsley, 2017), or paradoxes
(Hancock, 2017) of autonomy. A common point is that humans
have to spend more effort monitoring autonomous computers
because they are unsure of what it will do, often leading to
inferior performance (Blackhurst et al., 2011; Strauch, 2017).

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