(calm music)
Sam: Ethical use of technology is
and should be a concern for organizations everywhere,
but it's complicated.
Today we talk with Elizabeth Renieris,
Founding Director of
the Notre Dame IBM Technology Ethics Lab,
about what organizations can do today
without waiting for the perfect answer.
Welcome to Me, Myself, and AI,
a podcast on artificial intelligence in business.
Each episode we introduce you to someone innovating with AI.
I'm Sam Ramsbotham, Professor of Information Systems
at Boston College.
I'm also the guest editor for the
AI and Business Strategy Big Idea program
at MIT Sloan Management Review.
Shervin: And I'm Shervin Khodabandeh,
Senior Partner with BCG
and I co-lead BCG's AI practice in North America
and together MIT SMR and BCG have been researching AI
for five years, interviewing hundreds of practitioners,
and surveying thousands of companies on what it takes
to build and to deploy and scale AI capabilities
across the organization
and really transform the way organizations operate.
Sam: Today we're talking with Elizabeth Renieris.
Elizabeth is the Founding Director
of the Notre Dame IBM Technology Ethics Lab
as well as founder and CEO of HACKYLAWYER.
Elizabeth, thanks for taking the time to talk with us today.
Welcome.
Elizabeth: Thanks for having me.
Sam: Let's start with your current role,
your current new role at the Notre Dame IBM.
Elizabeth: I was going to ask which one?
(all laughing)
Sam: Well, I was thinking about the ethics lab
but you actually can start with whatever you like.
Elizabeth: Sure, so as you mentioned,
I've been recently appointed as the Founding Director
of a new technology ethics lab
at the University of Notre Dame.
It's actually called
the Notre Dame IBM Technology Ethics Lab
as the generous seed funding is actually from IBM.
My appointment is a faculty appointment with
the University of Notre Dame and the intention of the lab
is to compliment Notre Dame's existing
Technology Ethics Center which is a very traditional
academic research center focused on technology ethics.
So you can imagine there are many tenured faculty members
affiliated with the center and they produce sort
of traditional academic research,
peer-previewed journal articles.
The lab in contrast to that is meant to focus
on practitioner-oriented artifacts.
So the things that we want to produce are
for audiences that include companies themselves,
but also for law and policy makers,
also for civil society and other stakeholders.
And we want them to be very tangible and very practical.
So we're looking to produce things like
open source tool kits, and model legislation,
and explainer videos, and model audits,
and a whole array of things that
you wouldn't necessarily find from a traditional
academic research center.
What we really need in this space is we need centers
and institutions that can translate
between academia and practice.
The beauty of housing the lab in the university
of course is having access to the faculty
that's generating the scholarship
and the theoretical foundations for the work.
Shervin: Can you comment a bit more
on how you guys make that happen?
Because I know there's a lot of primary research
and then you have the faculty's point of view,
and I assume that there's also industry connections
and some of these applications in real life come in to play,
which is really important as you say.
What are some of the ways you guys enable that?
Elizabeth: Right now as we're getting up and running,
really what we're focusing on is convening power.
So we're looking to convene groups of people
who aren't necessarily talking to each other
and to do a lot of that translation work.
So right now the intention is to put out
an official call for proposals to the general public
and to be sourcing projects from all the different
stakeholders that I outlined consisting of teams
of individuals who come from different industries
and sectors and represent different sectors of society.
And to have them focus on projects that actually try
and solve real-world challenges.
So for example, right now during the pandemic,
those challenges might be something
like returning to work or returning to school.
And then of course,
what we want to do as the lab is we want to
take the brilliant work that the faculty
at Notre Dame is doing, and eventually elsewhere,
and leverage that to sort of underpin
and to inform the actual projects that we're sourcing.
And we can hopefully build some kind of narrative arc
around how you start translating that theory into practice.
Shervin: It seems like you're looking at ethics in AI
from two sides, right?
One is the ethics of the technology itself,
as in, is what the technology doing ethical
and how do you make sure it is ethical
and how can technology help the ethics conversation itself?
Elizabeth: I think it's absolutely both.
In my mind, you cannot separate
a conversation about technology ethics
from a conversation about values,
both individual values and collective societal values.
So what I find really fascinating about this space
is that you're right, while we're looking at the
ethical challenges presented by specific technologies,
we're also then confronted with having to identify
and prioritize and reconcile competing values
of different people and communities
and stakeholders in the conversation.
And you know, when we have a specific challenge
or a specific technology, it actually really turns
the mirror back on us as a society and forces us
to ask the question of what kind of society do we want to be
or what kind of company do we want to be,
or what kind of you know, individual
or researcher do we want to be
and what are our values and how do those values align
with what it is that we're working on
from a technology standpoint?
So I believe it's absolutely both.
And I think that's also been part of the evolution
of the ethics conversation in the last couple of years
is that while perhaps it started out with the lens very
much on the technology, it's been very much turned around
and focused on, you know, who's building it?
Who's at the table, what's the conversation?
What are the parameters?
What do we count?
What values matter?
And actually from my standpoint, those are the really
important questions that hopefully technology
is an entry point for us to discuss.
Shervin: Sam, I was just going to ask Elizabeth
to maybe share with us how she ended up here,
like the path that you took, that-
Elizabeth: -How much time do you have?
(all laughing)
Elizabeth: I'll give you the abbreviated version.
So I was classmates with Mark Zuckerberg at Harvard
and I've been thinking about these issues ever since.
But more seriously,
my sort of professional trajectory was that
after law school,
I worked at Department of Homeland Security
for a couple of years in their General Counsel's office.
And this was a long time after 9/11,
and I actually am from New York
and have vivid memories of the event.
And I was really struck by how much
of the emergency infrastructure was still in place
more than a decade after it was initially rolled out.
And I subsequently went back to obtain an LLM in London
and accidentally having arrived in the year 2012,
started working on the first draft
of what became the General Data Protection Regulation
or the GDPR.
And through that process gained a lot of exposure
to the ad tech industry, the FinTech industry,
somewhere along the way read the Bitcoin white paper,
came back to the states
just before the referendum, and was branded
a blockchain lawyer
because I had read the Bitcoin white paper.
(all laughing)
Elizabeth: So then I had this interesting dance
in trying to be a data protection and privacy lawyer
and also, you know, split my time
with the sort of blockchain distributed ledger folks.
And I quickly picked up on some of the unsavory,
unethical behavior that I saw in the space
and I was really bothered by it
and it also sort of triggered these memories
of the experience with Mark Zuckerberg
scraping faces of my classmates in university.
And it was just an interesting thing that
I didn't appreciate at the time
but sort of bubbled in the background.
And that led me to actually work
in-house at a couple of companies'
startups based in Silicon Valley and elsewhere.
And there was more of this sort of unsavory behavior.
And I thought if only we could talk about technology
we can engage with technology and be excited about it
without all of these terrible downsides.
I think part of the reason I was observing that
is because you didn't have the right people in the room.
So you had technologists that were talking
past and over lawyers and policy makers.
And that was my idea in late 2017
to start my HACKYLAWYER consultancy.
And the idea with that was, you know
I'm fairly technically savvy
but I have this great training, these legal skills,
these public policy skills.
I'd like to be able to translate across those groups
and bring them together and
built up a pretty successful consultancy
around that for a couple of years thereafter.
Shervin: That's a very inspiring story from the beginning
to here.
Sam: Kind of want to ask about the long version,
but I don't know that we have time for that.
(laughing)
Let's follow up on a couple of things that you mentioned.
One is, I think you and Shervin both talked
about this briefly, but you know, there's a little bit
of excitement about some of the bad things that happen.
You know when we see these cases of AI bias coming out
and they make headlines, you know, there's also
a silver lining and that lining is pretty thick
that it's really highlighting some of these things
that are already existing or already going on
and these seem like opportunities.
But then at the same time, you also mentioned how
when we react to those, we put things in place
and more than a decade later,
the DHS protections were still in place.
So how do we balance between reacting to these things
that come up between addressing biases
and not putting in draconian measures
that stifle innovation?
Elizabeth: You're right that there are opportunities.
I think the idea is that depending
on the challenge presented, I don't like the frame
of stifling innovation or the tension between, you know,
innovation and other values
like security or privacy or safety.
I think we're seeing this play
out again in the pandemic, right?
Where we are being often pushed a narrative
around technologies that we need to deploy
and technologies that we need to adopt
in order to cope with the pandemic.
And so we saw this in the debate
over exposure notification and contact tracing apps.
We're seeing this right now, very prominently
in the conversation around things
like immunity certificates and vaccine passports.
I think the value of ethics there again,
is that rather than look at the kind of narrow particulars
and tweak around the edges of a specific technology
or implementation, to step back and have that conversation
about values and to have the conversation
about what will we think of this in five or 10 years?
So the silver lining of what happened
after 9/11 was we've learned a lot of lessons about it.
We've seen how, you know
emergency infrastructure often becomes permanent.
We've seen how those trade-offs in the moment
might not be the right trade-offs in the long run.
So I think if we don't take lessons from those,
and this is where it's really interesting
in technology ethics,
how there's so much intersection with other things
like STS and other fields around history
and anthropology and why it's so critical
to have this really interdisciplinary perspective.
Because all of those things,
again go back to a conversation about values and trade-offs
and the prioritization of all of those.
And some of that also of course
has to do with time horizon,
going back to your question before.
So it's easy to take the short view,
it can be hard to take the long view.
I think if you have sort of an ethical lens
it's important to balance both.
Shervin: Yeah, and also I think you're raising
an interesting point that is with AI particularly,
the consequences of a misstep is very longterm
because the algorithms keep getting embedded
and they multiply
and by the time you find out, it might not be as easy
as you just replace it with a different one
because it has a cascading effect.
On the point about innovation,
AI can play a role in helping us be more ethical.
We've seen examples,
I think one of our guests talked about MasterCard, right?
How they're using AI to understand the unconscious
or unintended biases that their employees might have.
What is your views on that, on AI specifically
as a tool to really give us a better lens
into biases that might exist?
Elizabeth: I think the challenge with AI is
that it's so broad and definitions of AI
really abound and there's not entire consensus
around what we're even talking about.
And so I think there's the risk that we sort of
use this broad brush to characterize things
that may or may not be beneficial.
And then we run the risk of decontextualizing.
So we can say, you know, we have a better outcome
but relative to what?
Or what were the trade-offs involved?
And I think it's not just AI
but it's the combination of a lot of new
and advanced technologies that together are more
than the sum of their parts, right?
So AI plus network technologies,
plus some of the ones I've mentioned earlier,
I think are that much harder to sort of unwind
or course correct, or, you know,
remedy when things go wrong.
So one of the challenges I see in the space
is that again we can tweak around the edges
and we'll look at a specific implementation
or a specific tech stack
and we won't look at it in the broader context.
It's how does that fit into a system
and what are the feedback loops
and what are the implications for the system as a whole?
And I think that's one of the areas where
the technology ethics conversation is really useful
particularly when you look at things like relational ethics
and things that are a lot more concerned
with systems and relationships
and the interdependencies between them.
I worry that we're a little too soon
to declare victory there
but definitely something to keep an eye on.
Shervin: Yeah I mean as you say,
the devil's in the detail.
This is the beginning of having a dialogue
and having a conversation on a topic that otherwise,
you know, would not even be on the radar
of many, many people.
What is your advice to executives and technologists
that are right now building technology and algorithms?
Like what do they do in this early stages
of having this dialogue?
Elizabeth: Yeah, that's a tough question.
Of course it depends on their role.
So you can see how the incentives are very different
for employees versus, you know,
executives, versus shareholders, or board members.
So thinking about those incentives is important
in terms of framing the way to approach this,
that being said, there are a lot of resources now
and there's a lot available in terms of self-education.
And so I don't really think there's an excuse
at this point to not really understand
the pillars of the conversation, the core texts,
the core materials, the core videos,
some of the principles that we talked about before.
I think there's so much available by way of research
and tools and materials to understand
what's at stake, that to not think about one's work
in that context feels more than negligent at this point.
It almost feels reckless in some ways.
Nevertheless, I think the importance is to
contextualize your work, to take a step back.
This is really hard for corporations,
especially ones with shareholders.
So we can understand that, we can hold both as true
at the same time, and think about taking it upon yourself.
You know, there are more formal means of education
so one of the things that we are doing, the lab of course
is trying to develop a very tangible curriculum
for exactly the stakeholders that you mentioned,
and with the specific idea
to take some of the core scholarship
and translate it into practice,
that would become a useful tool as well.
But at the end of the day, I think it's a matter
of perspective and accepting responsibility for the fact
that no one person can solve this,
at the same time, we can't solve this
unless everyone sort of acknowledges that they play a part.
Sam: And that ties into the things your lab
is doing because you know,
I think the idea of everybody learning a lot
about ethics kind of makes sense at one level,
on the other hand, we also know we've seen with privacy
that people are lazy and we are all somewhat lazy,
we'll trade short term for long term.
And it seems like some of what your lab is trying to set up
is making that infrastructure available
to reduce the cost, to make it easier for practitioners
to get access to those sorts of tools.
Elizabeth: Yeah and I think education
is not a substitute for regulation.
So I think ultimately
it's not on individuals, it's not on consumers.
My remarks should be taken
as saying that the responsibility to really, you know
reduce the mitigate harms is on individuals entirely.
I think the point is that we just have to be careful
that we don't wait for regulation.
One of the things that I particularly like
about the technology ethics space
is that it takes away the excuse to not think
about these things before we're forced to, right?
So I think in the past
there's sort of been this luxury in tech
of waiting to be forced into taking decisions
or making trade-offs, or confronting issues.
Now, I would say with tech ethics,
you can't really do that anymore.
I think the zeitgeist has changed,
the market has changed.
Things are so far from perfect, things are far from good
but at least in that regard,
you can't hide from this.
I think in that way
they're at least somewhat better than they were.
Shervin: I also feel like
part of that is many organizations, to Elizabeth's point,
not only they don't have the dialogue,
even if they did, they don't have
the necessary infrastructure, or investments,
or incentives to actually have those conversations.
And so I think I go back to your earlier point, Elizabeth,
that is like, you know, we have to have the right incentives
and organizations have to have with or without
the regulation have the investment and the incentives
to actually put in place the tools and resources
to have these conversations and make an impact.
Elizabeth: You have to also align the incentives.
Some of these companies,
I think actually want to do the right thing,
but again, they're sort of beholden to quarterly reports
and shareholders and resolutions
and they need the incentives,
they need the backing from the outside to be able to
do what it is that is probably
in their longer-term interest.
Sam: You mentioned incentives a few times.
Can we get some specifics for things that we could do
around that to help align those incentives better?
What would do it?
Elizabeth: I think the sort of process oriented
regulations make sense, right?
So what incentive does the company have right now
to audit its algorithms
and then be transparent about the results?
None, they might actually want to know that,
they might actually want an independent third-party audit
that might actually be helpful from a risk standpoint.
If you have a law that says you have to do it,
most companies will probably do it.
So I think those types of, you know,
they're not even nudges.
I mean they're clear interventions, are really useful.
I think the same is true of
things like board expertise and composition.
And we may want to think about
is it useful to have super class share structures
in Silicon Valley where basically no one has
any control over the company's destiny apart from,
you know, one or two people.
So I think these are all again,
common interventions in other sectors and other industries.
And the problem is that this sort of
technology exceptionalism was problematic before,
but now when every company is a tech company,
the problem is just metastasized
to a completely different scale.
Sam: The analogy I think about is food.
I mean, anything that sells food now we want them
to follow food regulations.
That certainly wasn't the case a hundred years ago
when Upton Sinclair had the jungle.
I mean, it took that to bring that sort
of transparency and scrutiny to food related processes
but we don't make exceptions now for, oh well
you know, you're just feeding a hundred people.
We're not going to force you
to comply with health regulations.
Elizabeth: Exactly.
Shervin: Yeah, I think that's actually
a very good analogy Sam,
because as I was thinking about what Elizabeth was saying,
my mind went into just also ignorance.
I mean, I think many users
of a lot of these technologies, highly, highly
you know, senior people, highly,
highly educated people may not even be aware
of what the outputs are or what the interim outputs are
or how they come about, or like what all
of the hundreds and thousands of features that give rise
to what the algorithm is doing is actually doing.
And so it's a little bit like the ingredients
in food where we had no idea
some things are bad for us and some things would kill us
and some things that we thought were better for us
than the other bad thing are actually worse for us.
So I think all of that, it's bringing some light
into that education as well as regulation and incentives.
Elizabeth: The point is we acted
before we had perfect information and knowledge.
And I think there's a tendency in this space to say,
we can't do anything,
we can't intervene until we had to know exactly
what this tech is,
what the innovation looks like.
You know, we got food wrong, right?
We had the wrong dietary guidelines.
We readjusted them.
We came back to the drawing board
and we recalibrated, the American diet looks different
I mean it's still atrocious but can rethink it.
Sam: But we keep revisiting it, which is your point.
Elizabeth: We keep revisiting and we reiterate.
And so that's exactly what we need to do
in this space and say, based on what we know now
and that's science, right?
Fundamentally science is sort of the consensus we have
at a given time.
It doesn't mean it's perfect.
It doesn't mean it won't change, but it means
that we don't get paralyzed, but we act
with the best knowledge that we have and the humility
that we'll probably have to change this or look at it again.
So, you know, the same thing happened with the pandemic
where we have the WHO saying that masks weren't effective
and then changing course.
But we respect that process because there's the humility
and the transparency to say
that this is how we're going to operate collectively
because we can't afford to just do nothing.
And I think that's where we are right now.
Shervin: Very well said.
Sam: Well,
I really like how you illustrate all these benefits
and how you make that a concrete thing for people.
And I hope that the lab takes off and does well
and makes some progress and provides some infrastructure
for people to make it easier for that.
Thank you for taking the time to talk with us today.
Shervin: Yeah, thank you so much.
Elizabeth: Thanks so much for having me.
This was great.
(calm music)
Sam: Shervin, Elizabeth had a lot of good points
about getting started now.
What struck you as interesting
or what struck you as a way that companies could start now?
Shervin: I think the most striking thing she said,
I mean she said a lot of very, very insightful things
but in terms of how to get going, she made it very simple.
She said, look, this is ultimately about values.
And if it's something you care about
and we know many, many organizations and many,
many people and many very senior people
and powerful people do care about it.
If you care about it, then do something about it.
But the striking thing she said is
that you have to have the right people
at the table and you have to start having the conversations.
And as you said, Sam, this is a business problem.
Sam: That's a very managerial thing.
Shervin: Yeah, it's a managerial thing,
it's about allocation of resources to solve a problem.
And it is a fact that some organizations do allocate
resources on responsible AI and governance,
AI governance and ethical AI, and some organizations don't.
And so I think that's the key lesson from here
is that if you care about it
you don't have to wait for all the regulation
to settle down.
Sam: I liked her point about revisiting it as well.
And that comes with the idea of not starting perfectly
just plan to come back to it, plan to revisit it
because these things, even as you said, Shervin
if you got it perfect, technology would change on us.
Shervin: Exactly and you would never know
you got it perfect.
Sam: You never know you got it perfect.
Yeah, the perfection would be lost in immortality, so.
Shervin: I'm still trying to figure out if coffee
is good for your heart or bad for your heart
because it's gone from good to bad many times.
Sam: Well, I mean, and I think that's, you know,
some of what people face with a complex problem.
I mean, if this was an easy problem,
we wouldn't be having this conversation.
If there were simple solutions, you know,
if people are tuning in to say, alright,
here are the four things that I need to do to solve
ethical problems with artificial intelligence,
we're not going to be able to offer that.
We're not quite that Buzzfeed level
of being able to say, here's what we can do
because it's hard.
Shervin: The other thing that struck me is that,
you know she has a lot of education and passion
in this space that I think is actually quite contagious
because I think that's exactly the mentality
and the attitude that many organizations
can start to be inspired by and adopt
to start moving in the right direction
rather than waiting for government
or regulation to solve this problem.
We can all take a role in becoming more responsible
and more ethical with AI starting now.
We already have the right values
and we already know what's important.
Nothing is really stopping us
from having those dialogues and making those changes.
(calm music)
Narrator: Thanks for listening to season two
of Me, Myself, and AI.
We'll be back in the Fall
with new episodes for season three.
And in the meantime,
we're dropping a bonus episode on July 13th.
Join us as we interview Dave Johnson,
Chief Data and Artificial Intelligence Officer
at Moderna, about how the company used AI
to accelerate its development of the COVID-19 vaccine.
In the meantime, to continue the conversation with us,
you can find us in a special LinkedIn group created
for listeners just like you.
It's called AI for Leaders.
We'll put a link to it in the show notes
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Request to join and as soon as you do,
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Talk to you soon.
(calm music)