Hello everyone, my name is Gabriel, and today I'm excited to present our work titled "Human
Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making." This
work is a collaboration with Nina and Meeyoung from KAIST, IBS, and Max Planck Institute.
Well, AI systems are currently deployed in a wide range of consequential environments,
for example, loan decisions or decisions to grant or deny bail.
In these environments, these systems can cause real harm. A lot of scholars have discussed
basically who should be held responsible for any harm caused by autonomous AI systems, and many
of them actually agree that this question does not have a clear answer. Various authors discuss
different solutions and different perspectives to this question. Unfortunately, the HCI community
is yet to give a lot of attention to this issue of responsibility and has focused on trust, fairness,
which are also extremely important issues posed by autonomous systems. The few studies that actually
address responsibility often use scenarios that are not realistic, such as the trolley problem,
and treat responsibility and blame as synonyms, or responsibility as a single concept.
However, that is not really accurate. There is a lot of literature in the law, psychology,
and philosophy that discuss this pluralistic concept of what responsibility is. In this
work we actually focus on eight distinct notions that could apply to both humans and AI agents.
As a background, we actually use the COMPAS algorithm, this
system that has been used for bail decisions in the US, and that has received a lot of attention
by bias and fairness literature in the past. We basically posed a question of how do
lay people attribute responsibility to both AI systems and humans involved in bail decisions.
What we observe is that people attributed responsibility in three different clusters.
The first one is forward-looking, that actually concerns things that have not yet occurred. The
second one is this responsibility for explaining or justifying one's decisions, and the third one
is backward-looking, which actually concerns things that have already happened in the past.
We also observe clear differences between AI and human agents across these three clusters
of responsibility. First, we found that people expect both humans and AI advisors to explain
their decisions, which highlights the importance of explainable AI. On the other hand, we found
that humans were attributed higher levels of these forward-looking notions of responsibility,
and that they were attributed higher obligation, more authority, more tasks, and more skills
for making and assisting bail decisions than their AI systems counterparts. On the other hand,
what we found is that humans and AI were blamed to a similar level.
They also expected these two entities to compensate those harmed to the same extent.
We explain these results based on previous work in cognitive science, and we also discuss what
would it mean to blame an AI system. There's quite a lot of literature, particularly by
philosophers, discussing whether that would be possible and what it would actually mean.
In summary, we actually studied how lay people attribute responsibility to both humans
and AI agents in the context of bail decision-making. First, we found that
people expect these two entities to explain their decisions, which underlines the importance of
explainable algorithmic decision-making. Second, we found that humans were attributed higher levels
of forward-looking moral responsibility, while AI systems and humans were attributed similar levels
of backward-looking notions, such as blame, liability, and causal responsibility.
Well, I'm excited to hear all the questions that you might have during the live session.
If any of you are interested, feel free to to check our website, which is down there,
and also drop me an email if you want to have a chat. Thank you very much.