Instant Genius - Pragya Agarwal: When does bias become prejudice?

Episode Date: June 8, 2020

No matter how open-minded we consider ourselves to be, all of us hold biases towards other people. Dr Pragya Agarwal is a behavioural and data scientist, ex-academic, and a freelance writer and journalist, who runs a research gender equality think tank The 50 Percent Project. Her new book, Sway: Unravelling Unconscious Bias (£16.99, Bloomsbury Sigma), unravels the way our implicit or 'unintentional' biases affect the way we communicate and perceive the world, and how they affect our decision-making, even in life and death situations. In this week’s podcast, she explains where these biases come from and why it’s important for us to recognise and unlearn them to help make the world a better, fairer place. Let us know what you think of the episode with a review or a comment wherever you listen to your podcasts. Subscribe to the Science Focus Podcast on these services: Acast, iTunes, Stitcher, RSS, Overcast Why you should subscribe to BBC Science Focus Listen to more episodes of the Science Focus Podcast: Adam Rutherford: Can science ever be rid of racism? Angela Saini: Is racism creeping into science? Robert Elliott Smith: Are algorithms inherently biased? Caroline Criado Perez: Does data discriminate against women? Marcel Danesi: Why do we want to believe lies? Camilla Pang: How can science guide my life? Hosted on Acast. See acast.com/privacy for more information. Learn more about your ad choices. Visit podcastchoices.com/adchoices

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Visit namea audio.com to learn more. Because a lot of these biases are learned and shaped through our experiences, the way we have been brought up, the cultural, social context, the media we've been exposed to the messages we get on social media. The things that our tribe and our community tells us, our parent tells us, our friends tells us. The things we talk about, we read in newspapers, a lot of these biases are shaped by those things. So we are learning them. We learn them through our lifetime. And once we learn, because we learn them, we can unlearn them as well. So I do believe that once we become aware of them and we reflect on them and we acknowledge them, then we can start unlearning them as well and we can change our attitudes accordingly.

You're listening to the Science Focus podcast from the BBC Science Focus magazine team. With the UK's bestselling science and technology monthly, available in print and in several digital formats throughout the world. Find out more at sciencefocus.com or look out for us in your house. App Store. Hello, I'm Alexander McNamara, online editor at BBC Science Focus. No matter how open-minded we consider ourselves to be, all of us hold biases towards other people. Dr. Pragya Agarwal is a behavioural scientist and a data scientist, ex-academic and a freelance writer and journalist, who runs a think tank called the 50% project, which is for gender equality. In this week's podcast, she explains to our editorial assistant Amy Barrett where these biases come from and why

it's important for us to recognize and unlearn them to help make the world a better, fairer place. So my name is Dr. Pragya Garwal and I am a behavioral and data scientist, I'm ex-academic and I am a freelance writer and journalist now, science journalist, and I also run a research think tank called the 50% project, which is for gender equality. And let's talk about your upcoming book's way. How did that book come about? I think it's been happening for a long time. So the book is kind of emerged out of my own academic research, which was all about. I used to work. My work was based in data bias and how technology has bias in it and how when we try and integrate different people's perceptions in it, we cannot have a very kind of deterministic view of it and all those kind of things. How do we model mental maps and people's notions of place? And so I was really interested in

how bias and uncertainty gets built into data and technology. But then also, it's also based in my personal experience as a woman growing up in India and also here in a very largely predominantly white male environments of technology and STEM as an academic, being the first woman lecturer in a leading engineering department in one of the top five UK universities, being a single parent, and just being a woman of color and how that kind of shaped my sense of belonging and the kind of biases and prejudices that both implicit and explicit that I've faced through my life. So I think it was a culmination of all those things, personal and professional that came about. And you mentioned both the implicit and explicit biases. Can you just explain both of those terms for

me? In a lab context, in the STEM world, what do those two mean? So yes, so explicit is something that's very, so you can say, this is, it's very clear that, for instance, in a very simplistic way if I purposefully somebody discriminates between two people based on their race or skin color or the university they went to. And it's very, very clear that this discrimination or is happening or this prejudices exist. That is an explicit bias. So for instance, sometimes, for instance, in data, these explicit biases are also modeled in for the purpose of the model itself. But for in real world, for instance, a hate crime is an explicit prejudice or discrimination. But implicit ones are which are more difficult to tell that these are biases because these affect our decisions and these affect are actions,

but they are not very clear that these decisions and actions have been affected by our biases and our prejudices. For instance, making fun of somebody or subconsciously, you're subconsciously discriminating. or preferring one person over the other or one thing over the other because of perhaps, for instance, if a person looks at a CV and says, oh, I think this person is more qualified than the other just because perhaps they went to a preferred university or the university that they went to. So all of us carry a conformity bias. We are more attracted to people who are more like us. And those kind of biases are more implicit because they're.

are very not easy to explicitly mark out as biases and prejudices. Where do these kind of biases come from? Like, why does our brain create biases? So, evolutionarily, we were evolutionary basis of that. We were designed to differentiate between, make these quick decisions between people who belong to our group, our tribe, and those who didn't, and that was kind of a survival strategy, because resources were limited, and people had to say, this person is a threat to me or to the limited resources,

and so this person is an out-group, and they were different markers to these. So our brain is kind of creates these on a very superficial, when the information is processed, before it goes to the rational and logical part of the brain, we make these very quick decisions, about what information is important to us, what information should we take into account whether this person belongs to our group or an outside group, whether this person or an object is a threat or should we fear this person.

And these kind of in-group, out-group demarcations are made very, very quickly. Because we have to process so much information, so much information around us, that there's not time to take every bit of information, on a very rational, logical level. And so a lot of this information is processed on a basis of our previous experiences. And we make these very quick matches between our previous experiences and say, in the past,

this kind of person or this kind of product or this kind of situation was a threat to us. So that is what it would be. And that's how these immediate stereotypes are formed, that we very quickly make these kind of demarcations and distinctions and labels assign these labels. And so that's a way of in processing this information really quickly before we can take it to a very rational level in our brain. So there can be benefits to being able to do this for our brains working in this way. Absolutely. So I give an example and sway about a very simple example, like if I go and choose a brand of cereal in the supermarket, if I go shopping, if I made every

decision, um, in a kind of took every bit of information around me and weighed it up and try to make an independent decision based on logic and clear analysis that there's not enough time. I would be stuck with every decision in the world. So there are advantages to it because at times we cannot process every information, every bit of information so carefully. Um, and so it helps us make quick decisions in life.

And there are situations. and where we have to make really quick decisions, but there are obviously, as I discuss more in detail, there are obviously negative sides to it in certain situations and in decisions where these decisions actually make an impact. They have life and death impact or they are important. They're more important than just choosing a brand of cereal.

So it's easy to see how an explicit bias could have a life or death impact, but can unconscious bias, Is go that far? Yes, absolutely. And for instance, racial and racial discrimination has a huge impact. So for instance, if a jury is weighing up the evidence against a person, no person, even though however much we say we are neutral, we can never be completely neutral. We all carry certain biases.

So if a person has certain biases against a particular person who's being, who's on trial based on their accent or the way they look or whether they have more tattoos or whether they have a particular accent or whether they belong to a certain nationality or whether they're a particular gender, all those things. if the members of the jury have these implicit kind of preferences and biases against or these stereotypes saying that a person who looks like this or speaks like this or belongs to a particular group would be like this. And if they make these associations, then they might weigh up the evidence in a different way because that would, that might mean that they would give more weightage to a particular piece of evidence than the other. And so, Those implicit kind of are unconscious preferences and biases and stereotypes that we carry can have life and death impact as well.

And how can we go about identifying our own unconscious biases? So I think I discuss it more in sway. I think the important thing to be aware of and to acknowledge is that we all carry biases. And I think it's difficult for us to say that. because we all want to be egalitarian, we all want to be fair-minded, and we all want to be very equitable. But that's not the case. As long as we say we all carry these biases and preferences,

and some of these biases and preferences can have really huge impacts, and some might be okay for us to live with. And then that's the first step to do that. And then if we are more careful in situations where it is important to make these fair decisions, For instance, if I am on a hiring committee, it is more important for me to be aware of my biases, these implicit biases, in the way that I assess different people's CVs or in the way I sit on an interview panel and I assess different candidates. In those situations, I think it's really important for me to be, for a person who is making

these decisions, to be conscious of these, to reflect on their own biases and to actually not make any hasty, rushed decisions and to be conscious of any kind of hasty, rushed kind of stereotypes or associations or decisions that this person is making. So in those situations, I think it's important to take your time to step back and to reflect on your decisions and see whether any kind of unconscious biases are affecting those decisions. So is it the case that once you've started being aware of your own unconscious biases that you can kind of train yourself out of them and eventually remove them completely, or is it something that once you've kind of learned, you're always going to be struggling and coming up against?

No, I think this whole debate about whether unconscious biases are something we are born with or whether we can unlearn them. And I firmly believe that, personally, that because a lot of these biases are learned and shaped through our experiences, the way we have been brought up, the cultural, social context, the media we've been exposed to the messages we've been, exposed to the messages we get on social media, the things that our tribe and our community tells us, our parents tells us, our friends tells us, the things we talk about, we read in newspapers, a lot of these biases are shaped by those things. So we are learning them. We learn them through our lifetime. And once we learn, because we learn them, we can unlearn them as well. So I do believe

that once we become aware of them and we reflect on them and we acknowledge them, then we can start unlearning them as well. And we can change our attitudes. accordingly. So it's sort of things in your childhood that will then appear later in life as unconscious biases? Well, yes. I mean, for instance, this is why I think I talk about in sway about development of psychology and how children as they're growing up, they start forming this sense of in-group and out-group associations and that's a very natural response for children because they're making this sense of their own identity in the world, their own place in the world. And it's largely shaped by who they see around us, who they see as foes, who they see as friends,

who they see find comfort with. And so there's no real prejudice involved in it at that stage as they're growing up. But those prejudices are bolstered and reinforced by the messages they might get from their parents and they might get from the educators or the books that they read and the TV that they watch. So if a particular child thinks that a person who's a specific skin color is somebody to be feared with or they are not our friends or is my parents who I really trust do not like a certain kind of person, then these biases get reinforced into prejudices. And once they get, once a child learns them, then that can take form of. discrimination against people around them based on these prejudices and biases as well. So a child has these biases as a natural form of growing up,

are forming these in-group, out-group associations, but they only turn into prejudices and discrimination based on the things they learn from the society and culture, society around them, the environment. So do you see ever a future without these biases? is? No. I don't think. I think it's really important and it's really crucial that we're talking about it. And as I say, the more we talk about it, the more we become aware of things like microaggressions, for instance, and things that were acknowledged and just ingrained at part of our culture and just accepted as okay, even though it hurt and upset the person who was

being marginalized or victimized or targeted. and we just thought, oh, that's how things are. But it's good that we are talking about them. And it's good that we are trying to understand it from the perspective, the person who's the marginalized community rather than the person who is inflicting these kind of microaggressions. So I think change will happen and change is happening slowly, very slowly, because there is always resistance to change.

There's always resistance to any kind of change in status quo because people who half the privilege or people who have a certain status will always resist the change because that threatens their status and that means that they are worried about what their position and place in society will be once their status changes. So I talk about privilege and how that can be a threatening word to people. But we cannot do away with all our biases because some biases are not, biases not always negative. Biasis can have negative impacts. But as I talk about some biases are important to make really quick decisions in life. And we cannot just go do away with all our cognitive biases, all our implicit biases. But we can do away with stereotypes and we can

do away with prejudices and discrimination that is linked to some of the biases that we carry. So yeah, I think the change will come slowly. Do you think that sometimes it goes too far in one direction in that we go down the wrong route. The conversation can get nasty. I mean, council culture is a huge thing now on Twitter. But that just seems to kind of further divide people and further reinforce biases. Is there a right way we can go about it? Well, yes, I think there are, you're right that sometimes people are not engaging in conversation and discussion in a Hale Diva Manor. Sorry.

But I also think that, yes, a lot of things, these divides are being reinforced, by the way, our politics is working now because it's very partisan. And it's built on these divides and it benefits from these divides. So it's an interest of a lot of our politicians and the kind of climate we live in. that these divides exist and these divides are strengthened. So it, it, these divides are, and that's, that's not a very good way to go, obviously, and that's the negative side of these discussions happening. But as I said, there's always going to be push and resistance against any kind of change

happening from the communities that have had experienced certain privileges or from the communities that have held power because the person who's held the power, will always resist the kind of uprising from the people who have been oppressed historically, for instance. So if the marginalized communities start talking about and pushing back against these prejudices, and then there is going to be further divides initially. But I think the important thing is for people to be more open-minded. And I think, yes, social media is great.

these echo chambers and these filter bubbles that I talk about in Suey as well, about it's strengthening these, this kind of sense of belonging in a particular community that I belong in a particular tribe, so I cannot engage with anybody who does not belong in that. So again, we are falling back on very kind of primal in-group, out-group tendencies through these medium. So yes, I think having more scientific and evidence around these discussions, having more open-minded, non-judgmental platforms for these discussions is very important. And in terms of the scientific research, how do you go about studying people's biases, whether implicit or explicit? And that's really a tricky one, obviously, because, I mean, it's very difficult to measure and quantify these things.

And I know there I talk about in my book about some of the methods that people have used. And so this is why it was really important for me to look at it from a perspective of a very broad interdisciplinary lens because I could not just focus on one particular discipline or one particular methodology and what particular tool or technique that has been used to study this. To try and bring together these different studies, these disparate studies, even some of the studies where measuring bias or quantifying bias or assessing. bias wasn't the main goal of the study, but in the way the results can be interpreted in that, and they can tell us something about a particular group or the particular bias that people

might carry. And so I've tried to bring together all these different things in the book for this reason. And I also kind of critique some of the tools and methods which have been considered like the absolute goal or absolute one way that we can. can measure bias. And things like IAT, for instance, is a useful tool. But some people think that this is giving us a very measurable value for what my implicit bias is. They do not understand how the tool works.

And we do not take that into account when people use that. So even now when I go and do these diversity talks and workshops, people say, can we do the Harvard test beforehand so that I have a number for what my implicit bias is. and that is absolutely not what the test is telling us. So I think there are lots of myths and misunderstandings around what unconscious and implicit biases, how it can be measured, how we can tell what kind of biases we carry. And so I propose in my book about the kind of strategies that I take and the kind of methods that I use and those cannot be just one,

taking one computer-based test. So there are tests out there. that claim to help you identify your biases, but that's not something that you'd recommend just anyone in the general public should do? No, what I'm saying is that there is this test, yes, and it was proposed by these Harvard psychologists who first came up with the whole idea of measuring implicit bias.

And for a long time, IAT has been one that has been used by everybody because we don't have any other test. And there can be different, people have designed different kinds of IAT. implicit, it's called an implicit association test. So it works on the basis of association, associating different things and in that way it tells us what

our implicit biases are if I associate certain thing with certain thing. If I say apple green all the time, then obviously I believe firmly that apples are always green and they can never be read, you know, those kind very simply speaking. And it gives a value.

But, and I see that that's being used a lot by organizations and they call it diversity training and they call it implicit bias training. But that's not just, that's not training. That's not training for you to understand anything about what implicit bias is and how to tackle implicit bias because that number doesn't really give you an absolute marker for what kind of biases we carry. So I think what I'm saying is that it has to be a really deeper understanding for how we tackle our unconscious biases and how we unlearn them.

And you've already mentioned sort of racism and sexism, but those are perhaps the most politicized of unconscious biases. But what other areas have you looked at, maybe surprising areas that people can hold biases in that they might not realize? Yeah, I look at a really big range of biases because some of the biases we don't even think about, for instance, height.

Let's take one example. And people don't think about how power, or traditionally has been associated with people who are a certain height, how what kind of stereotypes are associated with people with a certain height. And so that's, and how during hiring or during interviews, there are certain associations made with people who are taller to be better leaders, things like that. And media sometimes or films can reinforce these stereotypes as well.

So that's one of the things in cases that I discuss. I also discuss age and ageism. And although we are having a more deeper, broader and standing in discussion and discourse around ageism now and how age-based discrimination is happening. But we're still not looking at the nuances and kind of more subtleties of ageism and how that impacts in healthcare diagnosis, in obviously workplaces, but also in our society, how that really impacts people's lives. I look at accents, which is one which more talked about, I suppose, but I looked at more deeply about how these biases are shape and form and looks. And I talk about, for instance, colorism, which is something that still exist. And that's really interesting because it's kind of ingrained colonialism or ingrained,

kind of this racism, reverse racism, where people of your own, in certain committees in China, Indian nation countries, still consider people who are more white passing, who have a bright, fairer skin to be better looking. And I discussed that as well. So yeah, some of the other names, for instance, name bias. And that's really interesting one, because we don't even think about that. But it's something that happens. a lot. There's been some studies. I've done my own informal studies and questionnaires and surveys around it. I have personal experiences of it. And there is psychological basis to how we associate certain things to certain names. And so there's a

broad spectrum of biases that affect our lives besides racism and sexism, which are obviously quite prominent ones and ones that get most platform. And is the act of holding a bias? Is that a strictly human thing? Are there any other animals that do it? And this is interesting because I was trying to think about it. Obviously, we haven't really done studies in terms of biases per se in animals or animal behavioral studies. But obviously, animals learn through their experiences to have certain preferences as well, you know? So I think we could say that, yes, they form these biases or preferences and certain associations.

when they, but that could be that because they associate certain feelings or emotions to a certain person or fear or threat or love or food or comfort to certain things. And so those kind of signals become their cues for those kind of behaviors or emotions as well. So yes, I don't know if you can call them biases, but yes, preferences for sure and that they learn and that shapes their decisions and actions as well. But I'm not an animal behaviourist, so that's something that's an interesting question to think about. But the way you sort of described biases is at the beginning is that it sounded very similar to kind of machine learning and in the way that computers use, you know, previous, not experiences as such,

but what they've got correct previously to then make quicker, faster assumptions. So can a computer be biased? Yes, and you're right, that's what happens in technology, and that's something where initially my interest was aroused about biases because I did a lot of work in that area. And yes, I have a huge section in my book, which is dedicated to AI and tech and machine learning and about how biases get ingrained into our technology because of the way the data has been designed or because of previous. previous experiences or previous incidents or previous instances that have been built into the data. And I give numerous examples of how that affects future performance and future behavior of these algorithms as well. And we might think that AI is neutral and it is bias free.

And that is certainly how people promote AI-based, AI-based hiring and recruitment apps and technology and platforms saying that. that because it's technology, it will not have any bias and we're going to do away with all the human biases. But that's completely incorrect because our machines are, they're not black boxes, they are being built and designed by humans, the data and it's building on the data that exists.

And so all the biases from the team, developers, from the data are being reinforced and kind of built into, built into the system itself. But when these systems and technology can then again create these biases, which can perpetuate the biases that already exist in the society as well, so that it becomes kind of a vicious cycle. It becomes kind of a cycle where they are taking in the biases, but they're also perpetuating

biases as well. So I talk about facial recognition systems and I talk about voice assistance and how sexism and affect these and how racism or racial prejudices affect facial recognition systems. And I talk about a lot of other examples about how AI and tech can take these biases in and why we need to be so careful when we use technology and machine learning. And this isn't just sort of high-tech stuff. This is stuff like voice assistants that we're using on a daily basis in our homes and in our phones. Yes, absolutely. It's because technology is all around us. And we don't often realize that the technology that we trust is not just built in isolation. It's a part of society. And so we do not understand its implications. For instance, in a very simple example, how I talk about more in the book, but voice assistants, for example, they always have very female.

male names. They were mostly built by teams. I mean, the problem with tech and one of the things, obviously, in STEM is that most of these developer teams are largely white male teams or just male dominated. So any kind of prejudices, and people have talked about, and I talk about these studies in my book about how there have been instances of sexism and misogyny in Silicon Valley in these kind of developer teams and coding teams. And so those kind of biases in the teams itself can get built into the technology or the systems that they're creating.

So the voice assistants, giving the very feminine voices or female names, creates and reinforces this kind of notion that women are in a subservient role or they are going to be assistant role. And they can be talked to in a job dominating way and they would not retort or they would just not stand up for themselves because that's the kind of image that they're projecting. So that's a very simple example. And I think

a lot of these companies and organizations are now beginning to take these concerns on board. So I know that there have been changes in the way voice assistance are being designed and things they can say in reply to sexual harassment statements or things like that. So there was a report by UN done a few years a couple of years ago which says I'd blush if I could in which in reply to something very sexually demeaning a voice system then would only say I'd blush if I could rather than actually saying anything else and so that's kind of reinforcing those the views the misogynistic views that can exist in society right at the point now where we need to address these things at the beginning before it kind of gets too late and before the tech kind of

runs away from us really. Absolutely. Yeah, you're right. Yes. I wanted to ask, where did your interest in unconscious biases come from? Is that something you've always been looking at? Yeah, so I talked about how the book was a culmination of my personal and professional interests. So yes, as I said, my research, academic research, was based a lot in very interdisciplinary discourse around uncertain. in data and technology and biases that are built into this about the human-centered interfaces, about the interaction between humans and technology. And then my personal, as I said, is very much centered around the real world,

lived experience of sexism, racism, accent, and many other things that we all experience in a lifetime. And so because of my very interdisciplinary research and interest, I started reading a lot more about the whole notion of unconscious bias. And then I was running workshops and talks and in unconscious bias after I left academia full time and started my own consultancy. And then from their own, I mean, I think just my scientific background and interests and my journalistic interests and my experience and my personal experience and interest, they all kind of came together and merged into the book. But I've read that you trained an architect. Is that right? My first degree was an architecture at India and then I came here to do a master's. and then I moved into to do my PhD, which was kind of, yeah, slightly a big transition from that first degree.

Yes. Wow. I wondered about data and if, because I've heard that it's the data that can't be biased is only how we interpret that data. I wonder, would you agree with that? Yeah, I mean, yes, absolutely. interpretation of data can be biased because so for instance when in some of my work and research we looked at how we can make data free of uncertainties and what kind how can we model those uncertainties so that when somebody's interpreting the data or visualizing the data we can visualize

the uncertainty as well so that we know that this data has got some uncertainties and biases in it but we are all humans and we as I say when we are bombarded with a lot of information it is natural for us to give more preference or weight to certain information than the other we cannot assess all the information in front of us and give it equal weight on most occasions so when we have a data set or a visualization of a data we would we are we tend to look at things that confirm our pre-existing biases in certain occasions. So we all carry, as I said, conformity bias. So we would, or status quo bias. So we would try and find evidence or look at evidence that confirms our assumptions and confirms our biases rather than negates it. And that's a natural instinct. And that

happens a lot. And so I talk about these biases in the book. And I talk about hindsight bias. So when we look back, it is a natural instinct to say, oh, I always knew that that was the case. could see the evidence for that. And so these kind of biases exist. And so human, human cognitive facilities are inherently biased. We cannot be biased free when we're interpreting data or when we're looking at information around us. Have you had like a moment like that, a moment of hindsight when you've realized what one of your own unconscious biases was? Yes, a lot. I reflect on it every day. And I think over the years I've changed and evolved as a person should and with more information that's come my way

I've tried to educate myself in so many different things so for instance I was born in India and although I resisted the pediracal structure and I fought against it and I didn't conform to that I obviously there's those ingrained certain behaviors and ingrained learned behaviors about what a girl or a woman should and shouldn't do. And so I think when I was bringing up my eldest daughter,

perhaps in some of the things I said to her, that might have come through because that is how I was brought up even without realizing it. Again, the whole notion of gender binary I think I grew up with. And so to try and reflect on how that might not be the case always, I had to really educate myself in that. And I read a lot around scientific theories and scientific research studies about sex and gender to be able to understand and formulate my own views on that as well. So, yes, I think it's our responsibility every day to take on new research, scientific research, scientific evidence on board and to assess what we know and whether what we know is right or not.

And can I have any influence over anybody else's unconscious biases? I'm thinking without naming names, I might have a racist family member, many of us might do. Is there anything I can do about that? Yes, I think absolutely. And it's a very tricky thing. It's really tricky. But I think it's our responsibility to stand up for what we believe in. And it's our responsibility to talk to them.

Because I don't think in the current climate especially, we can just be non-racist or we can just say, okay, I don't believe that, but I can't really be, I can't really influence what other thing. We can't do that. And there are many ways of doing this. There is one way of gently challenging them, questioning them, showing them evidence that their views might not be correct, putting evidence in front of them that says that what they believe in, might not be the best way to go. I know personally that I've had people in my life and in family who have said things that have been offensive to me, but also to others around me. And it's my responsibility to make them understand that their views might not harm them immediately or their immediate family members.

They are harmful in society. And I think we can keep giving very kind of clear, evidence-based information to them is, I think, our only way forward. And this is why we all need to educate ourselves so that we can do that. We can counter some of these views from others. That was Dr. Pragya Agarwal, whose book Sway is out now. In the June issue of BBC Science Focus magazine, we take a look at the bacteria that can eat plastic, chew through carbon and create food from thin air.

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