Hi, my name is Rie Kamikubo.
I’m excited to share with you our work on Contributing
to Accessibility Datasets: Reflections on Sharing Study Data by Blind People.
This is joint work with my fellow PhD student Kyungjun
Lee and our PhD advisor Hernisa Kacorri.
We are members of the Trace Center and the Human-Computer Interaction
Lab at the University of Maryland, College Park.
I want to first define what we mean by accessibility datasets.
They are datasets sourced from disabled people that have the
potential for training and testing machine learning models.
For example, in this work we focus on images taken by blind people.
So why do such datasets matter?
Well, they are central to innovation.
Photos taken by blind individuals, can be used to train a mobile phone to recognize
everyday objects, increasing access to the visual world.
Such datasets could also help in mitigating AI bias so that objects can be
recognized even when photos are taken by people who may face difficulty in camera
framing and avoiding object occlusion.
Thus, they could help broadly deployed AI systems such as Google Lens be
effective for diverse user populations.
However, if you are interested in finding accessibility datasets, you will quickly realize
they are scarce.
Looking at the challenges, we quickly see that one has to carefully consider the
potential risks for creating and sharing such datasets:
First, collecting data from small populations increases the risk of
re-identification.
This can amplify concerns for non-consenting disclosure, leading to further
discrimination risk pertaining to sensitive disability status.
Sharing accessibility datasets also poses risks of data abuse and misuse;
people could reuse these data with the wrong intent such as building an algorithm to
detect or misdetect a disability without one’s consent.
In the face of these challenges, our goal is to contribute to discussions around
increasing the availability of accessibility datasets, where data sharing is critical.
We engaged with 13 blind people to have direct
conversations on how data that they contributed in a study should be shared through
an AI dataset, specifically involving photos and labels of everyday objects.
Specifically, we conducted a pair of studies.
Blind participants were first invited to evaluate an object recognizer
deployed on smartglasses in their homes.
They generated photos and labels of objects to personalize the application.
This evaluation study helps us situate our work in a real-world data sharing scenario
that is challenging.
Since the data are collected in naturalistic settings, they may
capture more information about one’s surroundings.
More so, those contributing the data, our blind participants, may not
be able to inspect them when deciding to share.
We conducted a follow-up interview where participants reflected with and without probing scenarios
on various factors that influence their decision to share
their study data.
They reacted to benefits, risks, and other contexts such as data
access methods helping us surface their motivations and concerns relating to data
sharing.
After eliciting their perspectives, our participants were called to decide
whether they want the researchers to share their study data and participate in a
follow up co-design session for an accessible data inspection system.
In this work, we report findings from the interview contextualized within this larger
study.
We gained many insights from the interview and we invite you all to read our paper.
At a high level, we found that Many blind participants supported sharing
of their study data to improve AI-infused technology for greater benefit
to both disabled and non-disabled people.
Risks were often perceived as minimal: “I think the benefits far outweigh any minor
drawbacks that could occur...If someone says, oh, there was a 71-year-old guy that
took pictures of Lay’s potato chips, that’s all that ever happens to me.” - said P12.
Yet, they expressed concerns such as sharing demographic metadata along
their study data.
“Why would you even be collecting that information?...It’s not
relevant to you to the task of the AI” said P9.
We see that this is not only due to privacy and safety but also the ambiguity
of its value for building AI-infused technology.
For other concerns related to data use, many actually favored to restrict the use cases of their
study data being shared such as for research purposes only or for object recognition
technology.
Some participants even expressed hesitancy to contribute their study
data for commercial purposes.
However, they posed mixed feelings considering perceived benefits and
everyday risks they face in the digital world: “I can’t control, you know, and nobody
else can really control.
If people use this information for something else, honestly,
that happens everywhere” said P6.
Indeed, most of them frequently used commercial AI-infused applications such as to read text
and identify objects or share their camera view with sighted helpers, despite their concerns
given there were no alternatives.
To wrap up, Blind participants’ willingness to share
their study data were impacted by various factors, sometimes revealing a paradox between
their expressed concerns versus their behaviors as end users of AI-infused
applications.
We therefore see the need for better assessment of benefit risk tradeoffs;
more transparency and oversight structures to support an ongoing communication
with data contributors; and a participatory data stewardship framework,
ensuring that data contributors have some power to be able to affect the outcome of
the process, rather than being simply informed.
We leveraged the empirical insights gained from the blind community to discuss how
we can better configure data practices to align with individual concerns and values.
We also see how our approach of eliciting participants’ perspectives could be
leveraged by future researchers who want to engage their own participants in
decisions around sharing of their study data.
To facilitate such practices, we have shared our interview questionnaire at go.umd.edu/datasharing_questionnaire.
This work is supported by Nidilrr.
Thank you for listening.