Hi I'm Tara Capel and I'm Margot Brereton. We're from the Queensland University of
Technology in Brisbane, Australia and we are going to present our work on What is
Human-Centered about Human-Centered AI: A Map of the Research Landscape.
The term human-centered AI is increasing in use, responding to growing concerns about
AI's potential to exploit and mislead. The term human-centered AI creates the impression that
AI is to serve the people but human-centered AI means different things to different people.
Is the human the subject of an AI algorithm, the user of AI products, an agent in the design of
the AI system and so on? Is human-centered AI being used as an aspirational term like
sustainable mining in the resources sector or trusted autonomy with great debate as to
how and whether it can be achieved? There are various and broad definitions of what it means
to frame, design and evaluate human-centered AI. Let's look at some existing definitions.
Xu proposes human-centered AI as AI solutions that are ethical, they avoid discrimination,
maintain fairness and justice and do not replace humans; the AI technology fully reflects human
intelligence, and ensuring that AI is explainable, comprehensible, useful and usable. They're
focused on the role that HCI professionals should play in its design. Shneiderman defines
human-centered AI in designing for high levels of human control and computer automation to
increase human performances; avoid the dangers of excessive human or computer control; and
advocates for participatory design methods being used with a diverse range of stakeholders.
We conducted a review to further understand what the community is defining as human-centered
AI. So we conducted a search in five different databases, the search included any result that
was published anytime up until July 1st 2022 that either use the term human-centered AI,
human-centered artificial intelligence, human-centered machine learning or
human-centered ML, using the OR logic operator and also accounting for both
the British and U.S spellings of the word centered. This resulted in 2,357
initial items and after two review rounds we had 257 papers remaining.
We found significant breadth in what is considered to be human-centered AI. We presented the research
on this map as maps and useful for showing the complexity and change in the research landscape.
We chose axes of the human-centered AI research space in a way that would bring clarity and light
to the landscape, after Liz Sanders. We use the horizontal axis to depict the extent to which
research is led by a strong technical focus (so AI-led towards the left) to human values focused
research (human-values led towards the right). We then use the vertical axis to reflect the spectrum
of whether AI was under design or situated in use. Research at the bottom is more strongly
lab-focused design research whereas research at the top is designed in use or studies use. We
acknowledge that there could be higher dimensional representations, but these two axes and the 2D map
help to show the primary distributions of research. We have four quadrants within this map: explainable
and interpretable AI, humans teaming with AI, human-centered approach to design and evaluate
AI, and ethical AI. We also notice an emergence of the field of interaction with AI, the purple region in the middle.
To the lower left, explainable AI is largely technical research that seeks to explain AI
decision making processes. It's a huge field but only a small number of papers identify as
being human-centered AI. There is much more interpretable AI research closer to the
center of the map which focuses on ensuring human interpretability of AI decisions and explanations.
In the upper left area of the map we see a spectrum of research on humans teaming
with AI. On the far left AI simulates a human, so there's a little human involvement at all.
Progressing towards the right we see tools to support data scientists and people in the AI
loop, so verifying and helping the AI. Research that truly considers how both humans and AI
can learn together and what should and should not be automated sits farthest to the right.
Human-centered design research is shown in the lower right area of the map, reflecting
its human values led and design led focus. Within our corpus 49% of the papers used human-centered
design methods, which are outlined in the paper. Research is emerging in speculative design and
interaction design with AI, looking at how AI is represented in the user interface and how
people use it. The top right corner of the map shows an agglomeration of research in ethical
AI which seeks to understand issues of values, ethics, fairness and power embedded in AI projects.
Ethical AI projects seek to understand and reveal the contextual details of how AI is
made and how it impacts people. It advocates for transparent processes in the construction of AI
models - documenting how data is gathered, what it comprises, who is represented within it, assumptions
and mechanisms behind models, how they represent different user groups and perform in relation to
those groups and so on. Ethical AI also advocates for consideration of how resources are deployed,
for example creating huge dominant language models versus supporting endangered languages.
That brings us to the emergent middle area of interaction with AI.
AI uses models based on statistical inference from big data and this research looks at how
people will interact with inferred models in embodied and situated contexts. How will
they use them creatively? How can AI system use in the wild inform better and different interaction
design in ways that might be unimagined by the AI creators? This research can uncover how to restore
agency to users that may be lost to AI models derived from digital data contexts. Research is
beginning to ask how people can compose AI for themselves. Contestable AI is a kind of
interaction with AI where people compose their own scenarios to and challenge the results of the AI.
Research in this middle ground of interaction with AI demands multidisciplinary skilled
teams that can draw together technical, design and human-centered researchers.
Returning to the early definitions of HCAI, we offer a revised definition that reflects the research
landscape we uncovered. Here it is: human-centered artificial intelligence utilizes data to empower
and enable its human users, while revealing its underlying values, biases, limitations, and the
ethics of its data gathering and algorithms to foster ethical, interactive and contestable use.
Early definitions leave open who decides what is good for humans and who benefits,
which means that AI researchers often make these decisions themselves from their own viewpoint.
Incorporating ethics into the definition focuses attention on the need to consider
all who may be impacted, including any other species, as any ethics panel would insist.
By incorporating interaction in the definition, consideration of actual use is foregrounded.
So to our implications. HCAI needs greater collaboration between AI and
HCI researchers to develop new conceptual constructs that account for human relations
with AI, guide more coherent interface design, and reflect relationships between user intentions and
actions and inferred models from data. We build on views offered by Richard Harper and Alan Blackwell.
We hope the map might inform researchers about the breadth of research happening in HCAI, gaps in
the way research projects are formulated, areas of HCAI that might be embraced to strengthen
a team and a project, and that it may lead to research into new HCAI constructs and methods.
Thank you for listening and thank you to those who supported us.