I am a senior researcher at Yale Intering Institute and a co-director for OLS, open life science,
depth stocked about it.
But today, also, we're working after 5 PM, so I'm not representing any of my affiliation.
I'm representing myself.
I am from India.
I'm from North part of India, northeast from a small town called Ranchi.
It's small town for Indian standards.
We have about 1 million people.
And I moved to Germany.
I moved from North India to South India very early on because I wanted to study
computation and I wanted to do something which was emerging and wasn't really available everywhere.
So I went to Bangalore.
I'm sure you might have heard about that.
And I was very convinced that I wanted to move abroad and learn about
mathematics, change my field, and I moved to Germany.
And that's where I spent about a decade.
And after living there and after really reflecting back on my life,
I really think that my attraction towards open science, open source, was really driven by this
perspective, this identity being in a country that is not mine, trying to fit in,
and open source of science felt like something that was enabling access.
So being in Germany, I had a lot of access to different resources, which I didn't have
back in India and my colleagues still don't have.
So I went to do my PhD.
That's where I got a lot more involved with open source.
And I was very convinced that I'm going to move towards community management, community building.
And that's where I'm working now.
That has become my profession, and I'm really proud to even build next generation
of community managers who are part of my team.
So I am a co-founder for OLS, which came out of Mozilla.
It's a happy game.
We were part of cohort, which were incubated when Mozilla opened leadership could not be funded anymore.
And a lot of people like us who wanted to make open science open source accessible,
went on to build something that we thought our community needed.
I'm also a lead for the touring way, and a lot of the contributors are in the room,
which is a community like guide for data science best practices.
So there's a book, but there are people who are working in this book.
And the process of building has really generated momentum on the kind of culture we want.
Kind of kind, equitable practices that we want to promote in open source.
So I've been involved in quite a lot of open science open source projects,
and you can call me a quite compulsive open science community facilitator.
So sometimes I do things that I don't know why I do them.
So I came across do no harm project few years ago.
The do no harm project talks about how sometimes,
thoughtless data science can lead to really outsized damage on communities,
particularly from marginalized community.
So they built a series of guides which invites analyst researchers,
artists and activists to apply equity diversity and inclusion lens
to encourage the hardful way of doing data science.
In a way that it does not do any harm.
So of course it was very close to what I was working on.
I got very excited, I heard about do no harm in different context,
and this time it was first time for data science.
So all the projects that I found, all the reports that I found,
sorry, see this in there.
We're around how to make equitable data narrative,
how to improve perspective, how to do better visualization,
and they are really, really amazing.
But across those reports, I did not see any mention of open source open science,
despite it was a lot about technology and data.
So really, maybe wonder, what is it signaling?
Is it signaling that open source community does not have to care about
do no harm principle, or was it signaling that we're doing really well?
Because we're all lovely community people and like each other.
So we are never even thinking about harm.
So I got very, very curious, and I really had to solve that problem.
So I reached out to Urban Institute and proposed this idea
and this time do no harm in the context of open source and open science.
Yeah, that's a highlight.
So this report is published now that you can read,
but I'll talk you through so you don't have to read.
So the first part of this research was to understand
is open practice at all compatible with do no harm.
Is it the right route to go towards,
or am I looking at do no harm from a very community perspective
that may not apply in open science?
So do no harm has its origin in apocratic oath.
So anybody who becomes a doctor has to take this oath
of not harming their community or not mistreating their patients
or not doing something that can harm them.
So this is almost like a loyalty towards their profession.
And this does not have been in research.
It does not happen in research suffering engineering.
It does not happen in open source.
So there's a lot of mutual trust.
We believe that we are all going to behave properly,
all we find.
So we don't need to tell anything.
But do no harm was then adopted in humanitarian aid work
because humanitarian aid works were often supplied in places with wars
and conflicts.
A lot of people's lives were in danger.
So they started to use that as a way to promote similar ideology.
So there are many different definitions.
And a lot of time do no harm can be understood differently
based on where people are.
But this is one of the things, one of the definitions that I really like
which is about stepping back, taking a look at what we are doing
and identifying is there a place where my work might have been causing harm
to the local community, to their environment
or the fabric of society and economy.
So here's a little bit history.
As I was saying, it started in apocratic oath quite early on.
I don't even know the dates.
And in 90s it was then expanded into humanitarian aid work.
CDA was looking at do no harm to understand in conflict
and war zone.
And then around that time, a lot of people should objection
for using do no harm out of context.
But luckily in 2014, a lot of disciplines including research,
academic research where do no harm was being applied to.
But one thing to say, do no harm really can not be achieved.
Even a doctor cannot think about doing no harm.
Even the act of taking a bit of blood from your body
causes a little bit of harm.
So it's understood that do no harm literally does not mean do no harm.
It's about doing not no significant harm or no intentional harm.
So that's where I'll be also looking at.
Can we think about intentional or significant harm rather than no harm at all?
Especially as we know, data technology will always harm somebody.
So what the good news was that looking at literature and publications,
I could find that there were a lot of alignment between do no harm
area of scope and what we were doing in open science.
So there are a few areas where there are known harms of data and technology.
So abuses of human rights.
We are hearing a lot about it, conflicts and wars
where data is used to oppress people or there.
Now AI is being applied in making war in a different scale.
Environmental impact of the work people do and the production of abusive products.
So open source community does not have a say that if we release a software,
nobody can use them for any of those purpose.
We do have licenses and we do want people to apply our code of conduct.
But there's no way we can monitor that our open source tools are not applied to any of these.
And it's very scary thing to think about that some of the work we might have published may have gone into building something that we're not proud of.
But there are way more other things that we don't talk about,
which could be as simple as job displacement.
The automation is causing lots of job displacement without any commitment from organizations to re-skilled their worker.
There are things like threats of taking data use on for incentives.
I'm going to let you read and I actually ran out of space so I didn't even look for more things.
There are a lot of harms that we are not looking at on our day-to-day basis for whatever reason.
We're busy, we're writing grants all the time.
We have a PhD to finish.
A lot of things that we think are our life goals.
But the act of doing a harm is to really then step back and look at our life goal in the context of society.
So which may be more motivated to see okay, these things are where we can think about doing a harm principle.
So there are three main factors of doing a harm.
One is intervention.
So let's say an aid work that we are supplying.
Environment, the location where this aid work is being supplied to, and beneficiary.
This aid work should benefit somebody.
If you look at it in the open science perspective or open source or any kind of open practice,
you can think about each practice as its own intervention.
So open practice, for example, you have different kind of open source, open data, open access,
open education, citizen science, think about all these different kinds of practice as one of the interventions.
The environment is local environment, natural environment, as well as the workplace, the impact of our working condition.
And the beneficiary should include always include diverse researchers as well as social actors.
So we have been talking about working with communities where our communities are also really confined to the area that we are working in.
And not so much from outside world where let's say an aid technology will be applied to impact local community.
So there's another thing to note that open science itself is highly relative, highly contextual,
depending on people where they are.
It is affected by local realities of who is using this intervention and for what purpose.
As well as it also impacts, it gets impacted by the intervention, right?
We heard really good talk from Dan talking about funding and talking about people's need and what their focuses are.
But also we have governance and community and policy and legality.
A lot of different things that are intervention on open science which further impacts how we are applying that into a work.
So I'm not the first person who was thinking about it.
I was listening to sustained podcast and someone talked about to know harm license and that's how I found out that there was indeed a do know harm license.
However, that license never became legalized.
There's a group of people who are working on ethical source licensing and trying to think about how can we enforce some of these,
enforce against these misuses of our open source software.
So part two, so I'm going to share a bit of insight.
So we talked about that there are compatibility we can think about intervention environment and beneficiaries in the context of open science.
So there are four categories that I would invite you to think about do know harm.
So intervention in the context of rights of people.
So all actors of open source communities and their human rights to science.
The second is functioning of the community, governance participation and recognition of people.
The third is environmental context.
So we explained about where they are as well as the environmental impact and the local economy of researcher.
There's something I really care about.
It's not just enough to say, here you go, I'm going to give you authorship.
And I'm going to build a product that's going to make me very, very rich.
We have two wealth wise, share our wealth with people.
Especially my marginalized communities who are often just a participant of technology building rather than the people who get to set the agenda.
And of course the framework is not useful if we are not thinking about practical solutions.
So this framework should allow people to have conversation around these four areas and identify infrastructure implementation or practices and behavior change that can help them mitigate some of these harms.
So what this could look like in practice and this is a very experimental slide because I want your help in actually looking at if this is a practical thing for us.
So example word are we paying for the hidden cost of genuine and I don't know if you like AI but I thought let's talk about AI for one more time.
Chad Chippete came out in 2022 and it was really bad and we really complained about it but then it became really fun and people were very much into it.
So this is a reddit thread where someone's like, I love Chad Chippete much better than Google and then a lot of people are sharing, oh yeah, I asked Chad Chippete for what cocktails should I make or I just don't even use Google anymore.
So is it fun right it's fun is it fun and the fact is actually there are quite a really high cost for who pays the price for these fun act.
So when we put our query in Google we are using energy equivalent to running a 60 watt bulb for 17 seconds.
And we do a query search in Chad Chippete it's about three minutes of light.
So I did some unnecessary calculation despite that calculation somewhere exists. So there are 100 million queries per day actually more than that which can result into 226 giga watt per year which can have a five minutes left.
So these two entries like Belgium can have an entire of electricity with that.
Chad Chippete tree training alone emitted 550 metric ton of carbon and carbon emission of 100 cars in its entire life cycle.
I didn't even go into like really scary data just wanted to look at the environmental impact in terms of energy.
But there's also this really interesting paper on the Thursday AI. So of course these data centers require cooling cooling cannot happen with just normal water.
It has to be very very clean water and the amount of water that goes into cooling these are really interesting.
They did a couple of times during this presentation preparation, music a couple of Chad Chippete things.
And I had to remind myself of going to go to a bit of water every time because every time we're doing it we are actually wasting some water that a lot of countries don't have access to.
So it's a little impact of environmental impact of AI in general speaking.
And we're going to skip that for our purpose because we're talking about we've already covered that right.
So there are discrimination bias, there are job displacement, there are people who don't have anybody they can hold accountable.
But then we are also talking about carbon emission, we're talking about the water and carbon footprint and also the evased that affects people in the global south.
Mostly because all the evas are exported to those countries and there are manual worker who are working in really bad condition and their health and danger, their environmental and danger.
So Jen AI is amazing, it's powerful and it's no way that we're going to ever stop seeing it and we should not.
But we have to think about what is the optimum way to build these kinds of sources.
So open source committee will say oh we should create open source great we actually do have one and there are lots of debates I'm not going to mention the name.
So open source is a good alternative to chatchipity open source AI, Jen AI.
But if we can think about the environmental costs we can think about the community cost.
We might want to think about Jen AI in a different way and I don't know, I'm not a Jen AI expert, I'm just a user, but I think we can do better.
So this is where I need your help, this is going to be the part three I want to understand, can we think about do no harm just for your sake like would I.
What where I talk about do no harm in the context of the word that you're doing, do you think it's relevant should we have this conversation more than just a paper that I've published or should it not exist because it's unnecessary for this community.
So we have a buff tomorrow, birds of feather which would be open for them, I don't need to give this talk again, you can ring your ideas, you can tell me how wrong I am or you can tell me all the things that I did not treat or you can tell me that this is practical and we all should be caring about it.
The paper is published so you can read it, it can connect with me, I am an all social media, very less active, but with that I'll stop and I'm going to just tank the brilliant team member and all the wonderful people who are in this room and thank you all for staying here, I didn't think this room will have enough people, so I am so glad that you heard, thank you so much.
Thank you all for your questions.
Yes, then.
Just back on the beginning of the do no harm idea, I guess I was thinking about the distribution of the reports that tried to balance the benefit of doing something virtual, possible harm.
Yeah, yeah, I think the main reference I use for my research, yes, sorry.
So right now I was talking about the do no harm from perspective that looking at our work and thinking if it can cause harm and I think that you're asking if we can look at if this work is causing more harm than benefit.
So doing some sort of, I'm not saying he's saying it I'm rephrasing can we do some cost analysis for understanding if our work is worth pursuing because it's just going to harm people so what's what should it do.
And I think that it's a very good angle and wanted to just mention that the reference that I'm using for my primary references, it says that every organization and groups should have the ability to define their own definition of do no harm.
Do no harm is just a terminology and they know their community better. So I completely think that there are multiple directions we can go depending on what kind of work we're doing. Yes, please.
So there's a concept of evolution for the architecture which uses different functions because there's always play ups between things.
Have you talked about that work? So do less harm, but do it in your own context? Why do you find it in this function?
Yeah, of course it's hard.
Yeah, that's, you have beautiful minds. So I'm going to repeat that question. Thank you, Jim, for being the access warrior in this room.
So the question is very similar to what Dan is asking, but in this case, there is already some sort of architecture for understanding.
Yeah, so it can we do some modeling for what do we do that causes harm and how do we optimize for that?
I had never thought about that and I think when you were talking, I thought, it reminded me of are we talking about things like offsetting the carbon cost, right? Like when we buy purchase of light, it also says pay for this to offset the carbon cost.
I think that's an interesting interesting idea. It sounds like if there is something already in place adopting just feels very open source. So I'd be very keen to have that conversation.
Oh, yes, I don't know who was first, but I was sitting next to you, so please do. Go ahead.
So we are building a monoculture of Anglo-Sense Creek communication to the world. I'm thinking for example, and a senior, and two years ago, do you know?
And who's knowledge, and she is showing that there's another world beyond the world of people with different languages, different cultures.
And every time we talk in English on this, they're actually causing harm to them.
So when the Nashiva also wrote a lot about monoculture of mine, so I'm going to repeat the question. Yes.
So there are a lot of, so there are a lot of Western centric ways we communicate about different framework.
And when we talk about the framework that's built in Western places, we sometimes actually by the act of talking about it in the language, which is not people's own language, we are already perpetuating harm.
So I think it's a bit about English hegemony, how English is a normal language, and I have to say, like, very privileged to work with communities where people are doing translation and localization.
So they can understand, it's not just about text to text translation, but cultural integration in the text that we are building. So it's relevant for that particular communities.
So I think my response to that would be, if we have a room full of just same people with same idea, we are agreeing on everything.
That's a sign of harmful behavior, because we are not identifying different perspective that can challenge that notion.
Yeah, I think diversity is the answer, and I hope we all can continue to fight for EDI words and EDI act to remain as an important part of data science and open source.
And even in places where we have the conversation around the next framework or next textonomy or next standard. So I completely agree with you on that.
The one last question.
Yeah, I think for you. And the one thing is that exists like a group of people that could be like an extreme legalist of rule of art.
That's with a like a locate for actually forbidding by low some technologies, because they could be used in harmful ways.
My personal opinion would be that the technology is mutual and that it should avoid it.
Right.
Just one of these efforts would be a really small session.
Yeah.
So I'm going to repeat the question.
I remembered. So can can do no harm be misused?
I think it's the meta question. Can do no harm be harmful for some context where people with extreme legal ideas can try to game this in some ways.
And one statement I want to repeat is that technologies neutral and I want to say technology is not neutral at all.
And the reason why I feel comfortable with do no harm because it has proven track record and humanitarian aid work and medical context where they are not legal requirements.
They are commitment from the professional towards the discipline that they care about because they understand the impact on individual and people's life.
Something we as a community understand how our action may have those kinds of consequences, because a lot of time we just work on a get up page right like who's going to be harmed by a get up page.
So honestly, I shouldn't be caring about in those context do no harm, but I think we need to step back just the act of stepping back and saying is it just about get up page or there's something that we have missed or that was just an example.
But I think people can misuse whatever they want.
And if we have critical mass who cares about across and really operationalize it, I just think open source just signed up for misuse.
When they say I'm going to just publish it in MIT and ask you just to attribute me and do whatever you want or you know take it in commercial.
So I think we know that our work can be misused. In fact, the do no harm license was never legalized because people just couldn't agree on you know can you restrict someone from not using it for technology that you don't personally as a political person think about it's ethical but the local community thinks it's ethical.
So I think it's an interesting debate and I think there's a lot of things that I don't know some very open to have all of those conversations tomorrow.
On those ways words, thank you very much.