Being aware of the ethical issues that you as a mathematician may face is an extremely important step. But this is only the first in a sequence of potential steps. You can take this further, by starting to tell other mathematicians you work with or interact with. You can try and get involved with decision-making processes, by taking a seat at tables of power and authority. And you could even work towards identifying the unethical behaviour of other mathematicians completely unrelated to you, and call out their harmful actions to the community and to the public. This is fairly new and uncharted territory for mathematicians, and they're exactly the sorts of activities we shy away from. But now is the time to step up and take responsibility, because if we don't do it, then no-one else will.

Part of the 2022 Ethics for the Working Mathematician series.

All mathematicians will, eventually, form some part of the workforce. The abstract nature of mathematics may lead us to believe that our role is 'special', and that we won't need to worry about the usual workplace interactions, issues, conflicts and dangers that may arise in other professions. This is simply not true. We face the same issues, and need to know how to deal with them. Our focused and dedicated nature means that we may easily overlook instances of others trying to exploit or manipulate us at work, resulting in harm to ourselves, and our work becoming harmful to wider society. We need to know how to identify such people and situations, and to protect ourselves against them.

Part of the 2022 Ethics for the Working Mathematician series.

Just like every other academic field, mathematicians form their own community, with their own conventions, common beliefs, and schools of thought. We hand our teachings down through the generations, and this process goes all the way back to Euclid. But the ways of thinking we employ when doing mathematics in an abstract research setting may not serve us well in an industrial setting. It is important to be aware that not all the actions that make us good at mathematics will necessarily lead to us producing good solutions to industrial or social problems. In fact, some of our ways of viewing and approaching problems will hold us back when working outside academia.

Part of the 2022 Ethics for the Working Mathematician series.

The work of mathematicians in industry is now very close to its tangible applications; we produce output that is extremely quick and easy to use. Just look at machine-learned algorithms that compute credit scores. Now that we sit so close to the applications, we need to consider what sort of responsibility we have. There are things we are, and aren't, legally allowed to do. And there are consequences we might face if we fall foul of the law. Moreover, given that our work is often cutting-edge, we must self-regulate to prevent the types of harm that legislators and others have yet to realise is even possible.

Part of the 2022 Ethics for the Working Mathematician series.

Algorithms run the world, and mathematicians are designing them. Algorithms decide what people read, what they buy, and when then can get a loan. We often design these systems to remove human subjectivity from decision making processes and to make them impartial, as is being done with predictive policing algorithms and prison sentencing algorithms. But how impartial, or fair, can a system designed by humans ever be? Moreover, the internet and big data have given rise to massive new potential, from targeted political advertising as done by Cambridge Analytica, to AI technology such as deepfake videos and self-driving cars. Our 'solutions' in these instances can bring about a whole new set of problems.

Part of the 2022 Ethics for the Working Mathematician series.

Mathematicians have always played a central role in the making, and breaking, of cryptography. We also play a key role in developing surveillance tools, both for state actors and private organisations. Thus, we have several ways of enabling the infringement of the privacy of others. We can do so deliberately, by designing tools to break strong encryption, or indirectly, by creating systems and platforms which collect massive amounts of personal data of individuals. And we can do it accidentally, by being careless or sloppy in the way we store the data of others. In all of these cases, our work determines how much privacy people can have.

Part of the 2022 Ethics for the Working Mathematician series.

We all know about examples of mathematicians misbehaving in finance, and even being jailed as a result: Tom Hayes and Ke Xu are two examples. But more subtle are the modelling tools mathematicians produce. Mathematical modelling is ubiquitous in understanding the way the world works, from finance to physics to climate patterns. Understanding how to develop and use a model, as well as its limitations, and the way it interacts with the world, is indispensable in preventing it from causing harm. Unfortunately, as we saw in the financial crash of 2007, such models are sometimes poorly understood, with devastating consequences.

Part of the 2022 Ethics for the Working Mathematician series.

Mathematicians sit at the heart of technological advancement and industrial progress. Mathematics is a universal tool. It can be used for good, and it can be used for harm. To begin, we look at where harmful situations may arise, and what exactly we as mathematicians are doing to contribute to that harm. Though this harm may not (necessarily) come from intentional malice, there are many situations, and people, who can influence and manipulate us into carrying out harmful acts as mathematicians. It is important to be able to recognise and react to these scenarios, as we cannot always rely on external forces such as management to guide what we do.

Part of the 2022 Ethics for the Working Mathematician series.

Rotating savings and credit associations (roscas) are informal financial organizations common in settings where communities have reduced access to formal institutions. In a rosca, a fixed group of participants regularly contribute sums of money to a pot. This pot is then allocated periodically using lottery, aftermarket, or auction mechanisms. Roscas are empirically well-studied in the economics literature. Due to their dynamic nature, however, roscas have proven challenging to study theoretically, and typical economic economic analyses stop at coarse ordinal welfare comparisons to other credit allocation mechanisms and leave much of roscas’ ubiquity unexplained. This work takes an algorithmic perspective on the study of roscas. We present worst-case welfare approximation guarantees, building on tools from the price of anarchy. These cardinal welfare analyses help rationalize the prevalence of roscas. We conclude by discussing several other promising avenues.

As with engineering for safety and security, we need to move to unpack the concept of privacy to categorise and quantify the requirements likely to arise from ethical, professional, regulatory and legal considerations. In this talk I will discuss some of those requirements and some of the techniques that might be deployed to meet them in areas such as Privacy Enhancing Technologies and Higher-Order Cryptography.

Much of the recent literature in machine learning (ML) fairness has focused on statistical group-based notions of fairness, where the goal is to achieve or equalize some model performance metric across protected groups. While this framework has received widespread mathematical attention, this talk will discuss several of its practical and philosophical limitations. First, there is a practical issue of enforcing group-based fairness constraints when the data on protected groups is incomplete or noisy. Second, even with perfect data, we discuss rule-based notions of fair treatment that group-based fairness notions still cannot philosophically capture. Finally, even the most heavily fairness-constrained ML model might still fall short in satisfying societal needs due to choices in problem formulation and downstream interventions. Thus, we argue that the typical view of the ML life cycle in ML research needs to be expanded to capture a full spectrum of societal impacts.

In this talk, I will make the case for why and how mathematicians should consider using their quantitative skills to work on biological problems. We will discuss how these problems might be extremely important to work on, but also why one should be very careful with its ethical implications. I will share my experience so far in my PhD, researching (covid) epidemic and evolutionary dynamics, and more broadly being involved in the field of epidemic modelling, where mathematicians now have become involved with public-health policy and science communication.

A traditional problem of ethics in mathematics is the denial of ethical responsibility. Mathematics is viewed as neutral and value free, and therefore free of ethical responsibility. Yet many mathematicians develop theories that are hugely powerful in their applications. However, the refusal to let external values intrude into research serves to protect the directions of mathematical development from outside interference.. Is there any way of protecting mathematics from such encroachments while acknowledging the ethical issues and responsibilities involved? Macintyre’s virtue ethics provides one such approach.

Granting autonomy to mathematical researchers is in no way a validation or screen with which to hide problematic applications of mathematics across society. The abuses and misuses of mathematics have reached an all time high, and I will consider three types. 1. The use of mathematics in persuasion of the public by governments and corporations (mathwashing). 2. Overtly problematic or unethical applications of mathematics. 3. Performative applications of mathematics that change how people are treated and can incorporate hidden injustices and harms in the enaction of policies and practices.

I will mention two possible avenues of protection and improvement. First, the introduction of a Hippocratic-type oath for all mathematicians. Second, the introduction of ethical considerations into mathematics teaching at all levels (primarily via examples and case studies).

See here for further details, including the meeting link.

Being aware of the ethical issues that you as a mathematician may face is an extremely important step. But this is only the first in a sequence of potential steps. You can take this further, by starting to tell other mathematicians you work with or interact with. You can try and get involved with decision-making processes, by taking a seat at tables of power and authority. And you could even work towards identifying the unethical behaviour of other mathematicians completely unrelated to you, and call out their harmful actions to the community and to the public. This is fairly new and uncharted territory for mathematicians, and they're exactly the sorts of activities we shy away from. But now is the time to step up and take responsibility, because if we don't do it, then no-one else will.

See here for further details, including the meeting link.

All mathematicians will, eventually, form some part of the workforce. The abstract nature of mathematics may lead us to believe that our role is 'special', and that we won't need to worry about the usual workplace interactions, issues, conflicts and dangers that may arise in other professions. This is simply not true. We face the same issues, and need to know how to deal with them. Our focused and dedicated nature means that we may easily overlook instances of others trying to exploit or manipulate us at work, resulting in harm to ourselves, and our work becoming harmful to wider society. We need to know how to identify such people and situations, and to protect ourselves against them.

See here for further details, including the meeting link.

Just like every other academic field, mathematicians form their own community, with their own conventions, common beliefs, and schools of thought. We hand our teachings down through the generations, and this process goes all the way back to Euclid. But the ways of thinking we employ when doing mathematics in an abstract research setting may not serve us well in an industrial setting. It is important to be aware that not all the actions that make us good at mathematics will necessarily lead to us producing good solutions to industrial or social problems. In fact, some of our ways of viewing and approaching problems will hold us back when working outside academia.

See here for further details, including the meeting link.

The work of mathematicians in industry is now very close to its tangible applications; we produce output that is extremely quick and easy to use. Just look at machine-learned algorithms that compute credit scores. Now that we sit so close to the applications, we need to consider what sort of responsibility we have. There are things we are, and aren't, legally allowed to do. And there are consequences we might face if we fall foul of the law. Moreover, given that our work is often cutting-edge, we must self-regulate to prevent the types of harm that legislators and others have yet to realise is even possible.

See here for further details, including the meeting link.

Algorithms run the world, and mathematicians are designing them. Algorithms decide what people read, what they buy, and when then can get a loan. We often design these systems to remove human subjectivity from decision making processes and to make them impartial, as is being done with predictive policing algorithms and prison sentencing algorithms. But how impartial, or fair, can a system designed by humans ever be? Moreover, the internet and big data have given rise to massive new potential, from targeted political advertising as done by Cambridge Analytica, to AI technology such as deepfake videos and self-driving cars. Our 'solutions' in these instances can bring about a whole new set of problems.

See here for further details, including the meeting link.

Mathematicians have always played a central role in the making, and breaking, of cryptography. We also play a key role in developing surveillance tools, both for state actors and private organisations. Thus, we have several ways of enabling the infringement of the privacy of others. We can do so deliberately, by designing tools to break strong encryption, or indirectly, by creating systems and platforms which collect massive amounts of personal data of individuals. And we can do it accidentally, by being careless or sloppy in the way we store the data of others. In all of these cases, our work determines how much privacy people can have.

See here for further details, including the meeting link.

We all know about examples of mathematicians misbehaving in finance, and even being jailed as a result: Tom Hayes and Ke Xu are two examples. But more subtle are the modelling tools mathematicians produce. Mathematical modelling is ubiquitous in understanding the way the world works, from finance to physics to climate patterns. Understanding how to develop and use a model, as well as its limitations, and the way it interacts with the world, is indispensable in preventing it from causing harm. Unfortunately, as we saw in the financial crash of 2007, such models are sometimes poorly understood, with devastating consequences.

See here for further details, including the meeting link.

Mathematicians sit at the heart of technological advancement and industrial progress. Mathematics is a universal tool. It can be used for good, and it can be used for harm. To begin, we look at where harmful situations may arise, and what exactly we as mathematicians are doing to contribute to that harm. Though this harm may not (necessarily) come from intentional malice, there are many situations, and people, who can influence and manipulate us into carrying out harmful acts as mathematicians. It is important to be able to recognise and react to these scenarios, as we cannot always rely on external forces such as management to guide what we do.

See here for further details, including the meeting link.

Being aware of the ethical issues that you as a mathematician may face is an extremely important step. But this is only the first in a sequence of potential steps. You can take this further, by starting to tell other mathematicians you work with or interact with. You can try and get involved with decision-making processes, by taking a seat at tables of power and authority. And you could even work towards identifying the unethical behaviour of other mathematicians completely unrelated to you, and call out their harmful actions to the community and to the public. This is fairly new and uncharted territory for mathematicians, and they're exactly the sorts of activities we shy away from. But now is the time to step up and take responsibility, because if we don't do it, then no-one else will.

See here for further details, including the meeting link.

All mathematicians will, eventually, form some part of the workforce. The abstract nature of mathematics may lead us to believe that our role is 'special', and that we won't need to worry about the usual workplace interactions, issues, conflicts and dangers that may arise in other professions. This is simply not true. We face the same issues, and need to know how to deal with them. Our focused and dedicated nature means that we may easily overlook instances of others trying to exploit or manipulate us at work, resulting in harm to ourselves, and our work becoming harmful to wider society. We need to know how to identify such people and situations, and to protect ourselves against them.

See here for further details, including the meeting link.

Just like every other academic field, mathematicians form their own community, with their own conventions, common beliefs, and schools of thought. We hand our teachings down through the generations, and this process goes all the way back to Euclid. But the ways of thinking we employ when doing mathematics in an abstract research setting may not serve us well in an industrial setting. It is important to be aware that not all the actions that make us good at mathematics will necessarily lead to us producing good solutions to industrial or social problems. In fact, some of our ways of viewing and approaching problems will hold us back when working outside academia.

See here for further details, including the meeting link.

The work of mathematicians in industry is now very close to its tangible applications; we produce output that is extremely quick and easy to use. Just look at machine-learned algorithms that compute credit scores. Now that we sit so close to the applications, we need to consider what sort of responsibility we have. There are things we are, and aren't, legally allowed to do. And there are consequences we might face if we fall foul of the law. Moreover, given that our work is often cutting-edge, we must self-regulate to prevent the types of harm that legislators and others have yet to realise is even possible.

See here for further details, including the meeting link.

Algorithms run the world, and mathematicians are designing them. Algorithms decide what people read, what they buy, and when then can get a loan. We often design these systems to remove human subjectivity from decision making processes and to make them impartial, as is being done with predictive policing algorithms and prison sentencing algorithms. But how impartial, or fair, can a system designed by humans ever be? Moreover, the internet and big data have given rise to massive new potential, from targeted political advertising as done by Cambridge Analytica, to AI technology such as deepfake videos and self-driving cars. Our 'solutions' in these instances can bring about a whole new set of problems.

See here for further details, including the meeting link.

Mathematicians have always played a central role in the making, and breaking, of cryptography. We also play a key role in developing surveillance tools, both for state actors and private organisations. Thus, we have several ways of enabling the infringement of the privacy of others. We can do so deliberately, by designing tools to break strong encryption, or indirectly, by creating systems and platforms which collect massive amounts of personal data of individuals. And we can do it accidentally, by being careless or sloppy in the way we store the data of others. In all of these cases, our work determines how much privacy people can have.

See here for further details, including the meeting link.

We all know about examples of mathematicians misbehaving in finance, and even being jailed as a result: Tom Hayes and Ke Xu are two examples. But more subtle are the modelling tools mathematicians produce. Mathematical modelling is ubiquitous in understanding the way the world works, from finance to physics to climate patterns. Understanding how to develop and use a model, as well as its limitations, and the way it interacts with the world, is indispensable in preventing it from causing harm. Unfortunately, as we saw in the financial crash of 2007, such models are sometimes poorly understood, with devastating consequences.

See here for further details, including the meeting link.

Mathematicians sit at the heart of technological advancement and industrial progress. Mathematics is a universal tool. It can be used for good, and it can be used for harm. To begin, we look at where harmful situations may arise, and what exactly we as mathematicians are doing to contribute to that harm. Though this harm may not (necessarily) come from intentional malice, there are many situations, and people, who can influence and manipulate us into carrying out harmful acts as mathematicians. It is important to be able to recognise and react to these scenarios, as we cannot always rely on external forces such as management to guide what we do.

James Wright discusses how researching how to develop a stronger mathematical mindset lead to the realisation that the metacognative model of reflective practitioners could be mapped onto work done by Pólya and Mason. He will go over the mapping process between the two models with the aim of providing an insight into how mathematicians have most of the tools they need to solve ethical dilemas. He will also present a conjecture on why he thinks mathematicians and "hard" scientists don't normally apply their metacognitive practices to ethical problems, before presenting some final thoughts on how the process of modelling generates ethical conflicts.

In last term's seminar series, we saw that mathematicians don't always quite understand the way in which the law works. In this talk we shall hear something of the lawers' grasp of mathematics. From judges banning mathematical arguments in court to cases being overturned on appeal on the grounds that explaining Bayes' theorem to a jury is equivalent to telling them how to think, this talk promises to be a fascinating (if not frightening) look at what happens when mathematics meets the legal system.

Alongside her work in number theory, Leila Schneps has recently taken an interest in forensic mathematics (the use of probability and statistics in forensic science). In 2013, along with her daughter, mathematician Coralie Colmez, she published the book 'Math on Trial: How Numbers Get Used and Abused in the Courtroom'.

Being aware of the ethical issues that you as a mathematician may face is an extremely important step. But this is only the first in a sequence of potential steps. You can take this further, by starting to tell other mathematicians you work with or interact with. You can try and get involved with decision-making processes, by taking a seat at tables of power and authority. And you could even work towards identifying the unethical behaviour of other mathematicians completely unrelated to you, and call out their harmful actions to the community and to the public. This is fairly new and uncharted territory for mathematicians, and they're exactly the sorts of activities we shy away from. But now is the time to step up and take responsibility, because if we don't do it, then no-one else will.

All mathematicians will, eventually, form some part of the workforce. The abstract nature of mathematics may lead us to believe that our role is "special", and that we won't need to worry about the usual workplace interactions, issues, conflicts and dangers that may arise in other professions. This is simply not true. We face the same issues, and need to know how to deal with them. Our focused and dedicated nature means that we may easily overlook instances of others trying to exploit or manipulate us at work, resulting in harm to ourselves, and our work becoming harmful to wider society. We need to know how to identify such people and situations, and to protect ourselves against them.

Just like every other academic field, mathematicians form their own community, with their own conventions, common beliefs, and schools of thought. We hand our teachings down through the generations, and this process goes all the way back to Euclid. But the ways of thinking we employ when doing mathematics in an abstract research setting may not serve us well in an industrial setting. It is important to be aware that not all the actions that make us good at mathematics will necessarily lead to us producing good solutions to industrial or social problems. In fact, some of our ways of viewing and approaching problems will hold us back when working outside academia.

The work of mathematicians in industry is now very close to its tangible applications; we produce output that is extremely quick and easy to use. Just look at machine-learned algorithms that compute credit scores. Now that we sit so close to the applications, we need to consider what sort of responsibility we have. There are things we are, and aren't, legally allowed to do. And there are consequences we might face if we fall foul of the law. Moreover, given that our work is often cutting-edge, we must self-regulate to prevent the types of harm that legislators and others have yet to realise is even possible.

Mathematicians have always played a central role in the making, and breaking, of cryptography. We also play a key role in developing surveillance tools, both for state actors and private organisations. Thus, we have several ways of enabling the infringement of the privacy of others. We can do so deliberately, by designing tools to break strong encryption, or indirectly, by creating systems and platforms which collect massive amounts of personal data of individuals. And we can do it accidentally, by being careless or sloppy in the way we store the data of others. In all of these cases, our work determines how much privacy people can have.

We all know about examples of mathematicians misbehaving in finance, and even being jailed as a result: Tom Hayes and Ke Xu are two examples. But more subtle are the modelling tools mathematicians produce. Mathematical modelling is ubiquitous in understanding the way the world works, from finance to physics to climate patterns. Understanding how to develop and use a model, as well as its limitations, and the way it interacts with the world, is indispensable in preventing it from causing harm. Unfortunately, as we saw in the financial crash of 2007, such models are sometimes poorly understood, with devastating consequences.

Mathematicians sit at the heart of technological advancement and industrial progress. Mathematics is a universal tool. It can be used for good, and it can be used for harm. To begin, we look at where harmful situations may arise, and what exactly we as mathematicians are doing to contribute to that harm. Though this harm may not (necessarily) come from intentional malice, there are many situations, and people, who can influence and manipulate us into carrying out harmful acts as mathematicians. It is important to be able to recognise and react to these scenarios, as we cannot always rely on external forces such as management to guide what we do.

This conference, organised by Dr Maurice Chiodo with the assistance of Dr Piers Bursill-Hall, brings together a number of speakers and guests from Cambridge, the UK, and the rest of the world, to discuss various topics relating to ethics in mathematics.

The event has limited capacity and is now fully booked, but video recordings of the talks will be available online after the event.

For more details, visit http://www.ethics.maths.cam.ac.uk/EiM2.

The Snowden revelations in 2013 shook up the cryptographic community when documents showed evidence of actions to subvert standards and restrict “indigenous cryptography”. This talk will shine a light on the history of the most famous standardized back door, the Dual-EC pseudo-random number generator, and how it came into being a standard. Dual-EC is also a textook example, though not the only one, of how back doors go bad. The talk will also cover some lesser known issues with standards and that it is sometimes hard to distinguish sabotage from bad, but benign, cryptographic designs.

Tanja Lange works on cryptography and number theory. She is the chair of the Coding Theory and Cryptology group at the Technische Universiteit Eindhoven in the Department of Mathematics and Computer Science. She is also scientific director of the Eindhoven Institute for the Protection of Systems and Information.

Daniel J. Bernstein is a research professor in the department of computer science at the University of Illinois at Chicago. He is the designer of the "Curve25519" public-key system used by WhatsApp for end-to-end encryption, many more tools used throughout the Internet infrastructure, and new tools designed to protect against the threat of future quantum computers. His current mission is to cryptographically protect every Internet packet.

There are many threats to freedom in the digital society. They include massive surveillance, censorship, digital handcuffs, nonfree software that controls users, and the War on Sharing. Computers for voting make election results untrustworthy. Other threats come from use of web services. Finally, we have no assured right to make any particular use of the Internet; every activity is precarious, permitted only as long as companies are willing to cooperate with our doing it.

Follow-up notes from Martin Hellman

As computers and computing, and their underlying algorithms, become more pervasive in our lives, ethical decision making is becoming ever more important in mathematics. This talk hopes to help mathematicians, as developers of these algorithms, rise to that challenge. It does so first by demonstrating how easily we fool ourselves, using a personal example where I did that when confronted with the inadequate 56-bit key size of the Data Encryption Standard (DES). It then uses another personal example, Stanford's patent fight with RSA Data Security, to show how difficult it was for me to make ethical decisions even after I had committed never to fool myself again. The resolution of my dilemma demonstrates the value of getting input from outside parties, of lowering the bar for what constitutes unethical behavior, and of working to make society more ethical as a whole.

Being aware of the ethical issues that you as a mathematician may face is an extremely important step. But this is only the first in a sequence of potential steps. You can take this further, by starting to tell other mathematicians you work with or interact with. You can try and get involved with decision-making processes, by taking a seat at tables of power and authority. And you could even work towards identifying the unethical behaviour of other mathematicians completely unrelated to you, and call out their harmful actions to the community and to the public. This is fairly new and uncharted territory for mathematicians, and they're exactly the sorts of activities we shy away from. But now is the time to step up and take responsibility, because if we don't do it, then no-one else will.

All mathematicians will, eventually, form some part of the workforce. The abstract nature of mathematics may lead us to believe that our role is "special", and that we won't need to worry about the usual workplace interactions, issues, conflicts and dangers that may arise in other professions. This is simply not true. We face the same issues, and need to know how to deal with them. Our focused and dedicated nature means that we may easily overlook instances of others trying to exploit or manipulate us at work, resulting in harm to ourselves, and our work becoming harmful to wider society. We need to know how to identify such people and situations, and to protect ourselves against them.

Just like every other academic field, mathematicians form their own community, with their own conventions, common beliefs, and schools of thought. We hand our teachings down through the generations, and this process goes all the way back to Euclid. But the ways of thinking we employ when doing mathematics in an abstract research setting may not serve us well in an industrial setting. It is important to be aware that not all the actions that make us good at mathematics will necessarily lead to us producing good solutions to industrial or social problems. In fact, some of our ways of viewing and approaching problems will hold us back when working outside academia.

The work of mathematicians in industry is now very close to its tangible applications; we produce output that is extremely quick and easy to use. Just look at machine-learned algorithms that compute credit scores. Now that we sit so close to the applications, we need to consider what sort of responsibility we have. There are things we are, and aren't, legally allowed to do. And there are consequences we might face if we fall foul of the law. Moreover, given that our work is often cutting-edge, we must self-regulate to prevent the types of harm that legislators and others have yet to realise is even possible.

Algorithms run the world, and mathematicians are designing them. Algorithms decide what people read, what they buy, and when then can get a loan. We often design these systems to remove human subjectivity from decision making processes and to make them impartial, as is being done with predictive policing algorithms and prison sentencing algorithms. But how impartial, or fair, can a system designed by humans ever be? Moreover, the internet and big data have given rise to massive new potential, from targeted political advertising as done by Cambridge Analytica, to AI technology such as deepfake videos and self-driving cars. Our "solutions" in these instances can bring about a whole new set of problems.

Mathematicians have always played a central role in the making, and breaking, of cryptography. We also play a key role in developing surveillance tools, both for state actors and private organisations. Thus, we have several ways of enabling the infringement of the privacy of others. We can do so deliberately, by designing tools to break strong encryption, or indirectly, by creating systems and platforms which collect massive amounts of personal data of individuals. And we can do it accidentally, by being careless or sloppy in the way we store the data of others. In all of these cases, our work determines how much privacy people can have.

We all know about examples of mathematicians misbehaving in finance, and even being jailed as a result: Tom Hayes and Ke Xu are two examples. But more subtle are the modelling tools mathematicians produce. Mathematical modelling is ubiquitous in understanding the way the world works, from finance to physics to climate patterns. Understanding how to develop and use a model, as well as its limitations, and the way it interacts with the world, is indispensable in preventing it from causing harm. Unfortunately, as we saw in the financial crash of 2007, such models are sometimes poorly understood, with devastating consequences.

Mathematicians sit at the heart of technological advancement and industrial progress. Mathematics is a universal tool. It can be used for good, and it can be used for harm. To begin, we look at where harmful situations may arise, and what exactly we as mathematicians are doing to contribute to that harm. Though this harm may not (necessarily) come from intentional malice, there are many situations, and people, who can influence and manipulate us into carrying out harmful acts as mathematicians. It is important to be able to recognise and react to these scenarios, as we cannot always rely on external forces such as management to guide what we do.

CUEIMS hosted Edward Snowden via videolink. He gave a talk followed by a moderated question and answer session with active audience participation.

This conference, organised by Dr Maurice Chiodo with the assistance of Dr Piers Bursill-Hall, brings together a number of speakers and guests from Cambridge, the UK, and the rest of the world, to discuss various topics relating to ethics in mathematics.

A live stream of this event was available via our Youtube channel.

For more details, visit http://www.ethics.maths.cam.ac.uk/EiM1.

Mathematics is frequently used in competitive environments, to help people optimise their situation by finding the best tactical position to take. This is often very technical work, taking in to account all sorts of real-world factors to find the "winning" position. So how do we go about identifying all these factors, how do we weigh them all up, and what does it even mean to "win"? We can't solve a problem if we don't have an understanding of what we are solving and why we are solving it.

The notions of "leadership" and "mathematics" are seldom mentioned together in conversation. Yet there are mathematicians who choose to be in, or inadvertently find themselves in, positions of management and leadership. These scenarios can arise in both in academia and in industry. But is it even possible for a mathematician to lead effectively? How do we reconcile our training in axiomatised mathematics with the interpersonal dynamics and social considerations that we must deal with as leaders? There is much more to leadership than bean-counting and number-crunching.

Bonnie Shulman’s focus is in Mathematical Physics but she takes a strong interest in philosophy of science and mathematics. Throughout her academic career she has devoted attention to ethical issues that arise in mathematics alongside her mathematics research, which includes Mathematical Biology and Game Theory. Publications she has authored include “Is There Enough Poison Gas to Kill the City?: The Teaching of Ethics in Mathematics Classes” and "Using Original Sources to Teach Mathematics in Social Context".

Professor Bryson’s first and third degrees were in Psychology, while her 2nd and 4th were in Artificial Intelligence, so she approaches AI for the purpose of understanding human behaviour. Joanna has worked in AI ethics since 1996, and helped author the UK research councils’ Principles of Robotics in 2010. Just in the last two months she’s consulted to The Red Cross on autonomous weapons, Chatham House on the impact of AI on the nuclear threat, and she’s currently advising the British Parliament, European Parliament, and the OECD regarding the regulation of AI.

She'll be discussing her experiences working in AI policy with politicians and NGOs. She has said that she is willing to show and discuss some of the same slides and talks she's given in some of those meetings, as well as describing her experience of getting the Principles of Robotics taken up by government, industry and citizens.

Our understanding of mathematics comes from building on that of those who came before us; we are taught and mentored by them. We admire their work, and by extension we admire them as people. So how well do these mathematicians prepare us for the real world, and how much more do we need to know? We work very hard to emulate them, but we must be careful not to do so absolutely or without question.

The seminar will end with an open discussion with William Binney. William Binney is a cryptomathematician, and former employee and whistleblower of the NSA. He has spoken to CUEIMS previously on 'the Dangers of Success', and is with us again for a more informal discussion on the responsibilities of the working mathematician.

We hold mathematics in very high regard, as the beacon of absolute truth. Mathematics does not have any intrinsic prejudice or bias; it reveals truth. But how do we infer meaning from truth? Mathematicians design systems to remove human subjectivity from decision making processes, to make them more impartial. Does this mean that that we've removed all subjectivity from the process? We must realise the strengths, and weaknesses, of the systems we design.

Mathematics can be used to fight crime, avert destruction, and protect our society. But how far are we willing to go to do this, what are the drawbacks of such pursuits, and are they worth doing "at any cost"? In the pursuit of preventing harm and improving society, are we capable of doing even more harm in the process? There are instances when this is obvious, but also instances when it becomes somewhat opaque.

Mathematics is the most precise of all disciplines of study. We work on the basis of absolute precision, and of absolute truth. Once a problem has been "translated" into mathematics, we can manipulate it

Mathematicians have taken it upon themselves at several points in history to work for the betterment of the human race. We have applied our specialised skills and abilities to solve problems of large social, economic and political importance. These highly complex solutions that we develop can have, and often have had, unintended consequences far beyond their original design. This often comes about because mathematicians fail to think ahead and ask the question "What am I making, and what else can it be used for?"

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Some say we live in a post-truth society abounding in fake news and alternative facts, with a declining trust in 'experts'. Certainly the media are full of political and scientific claims about risks, supposedly based on science or statistics, but that may be exaggerated or even simply untrue. I will look at the 'pipelines' through which scientific evidence is propagated through the media to the public, and suggest ways of improving both the trustworthiness of the evidence being communicated, and the ability of audiences to assess the quality and reliability of what they are being told.

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Why would a mathematician or statistician need any advice on ethics? Pure mathematics surely does not need an apology for pure enjoyment of abstract beauty, unsullied by everyday responsibilities.

The American Mathematical Society has a Policy Statement on Ethical Guidelines, first approved in January 1995. I suspect it is not often read. I will compare this with the International Statistical Institute Declaration on Professional Ethics of 1985 and 2010, and with medical codes of conduct.

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Do mathematicians have the right to voice moral objection? Is it even possible in our line of work? If so, how would we recognise when to object, how would we begin to go about it, and what sort of obstacles might we encounter when trying to do so? Those who seek our services have the ability to manipulate and coerce us, and defending against such coercion is a highly non-trivial task. Handling these situations requires real-life experience of them, which is hard (but not impossible) to teach.

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What motivates us as mathematicians? Ours is one of the few professions where we enjoy our job so much that we’d probably do it for free. Our dedication and determination to solve mathematical problems is one of our greatest strengths, but can also be our undoing. Our ability for extreme focus is a double-edged sword; on the one hand it makes us excellent problem solvers, on the other hand it restricts our capacity to see the broader implications and consequences of our work.

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To begin, we look at where such ethical issues may arise. What exactly might we do as mathematicians that causes harm? Who might we harm, and what might lead us to doing so? There are many situations, and people, who can influence and manipulate us into carrying out harmful acts as mathematicians. Realising where they might lie, and the sort of damage we might end up doing, is the first step in learning to combat them.

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William Binney is a cryptomathematician and former employee of the NSA. He designed the privacy-conscious surveillance program ThinThread. After more than 30 years with the agency, he resigned in protest and became a whistleblower, exposing the massive waste in expenditure, and privacy violations, in the NSA's new Trailblazer surveillance program. He is the recipient of numerous awards for social justice, including the Joe A. Callaway Award for Civic Courage, and the Sam Adams Award for Integrity in Intelligence.

Arjen Kamphuis worked for IBM as an IT-architect in the 1990s. From 2002 to 2010 he advised several European countries on IT-strategy, opensource and open standards. Since 2006 he has helped secure the information systems of corporates, national government and NGOs. His work ranges from regular privacy-compliance and security-awareness up to countering espionage against companies, journalists and governments. To keep up technically Arjen is involved with the global hacker-scene and keeps in touch with (former) employees of spy agencies and other professionals who work at the front of critical infrastructure protection.

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Thomas Drake was a senior executive of the NSA from 2001 to 2007. In that time, he made public disclosures about the surveillance program "Trailblazer" being developed by the NSA. This led to him being charged under the Espionage Act in the US. He has since received numerous awards, including the Ridenhour Prize for Truth-Telling and the Sam Adams Award.

Thomas will speak about his time at the NSA, his interactions with other mathematicians there, and the the reasons why he chose to become a whistleblower.

This will be a videolink talk, with ample time for questions at the end.

CUEIMS hosted a discussion with Julian Assange via videolink. He spoke for 30 minutes, in which he recounted a biography and shared his perspectives on ethics in mathematics. His talk was followed by a moderated 45 minute question and answer sessions with active audience participation. The event was organised in its entirety by CUEIMS.

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The FBI Director Jim Comey and the US Attorney General Jeff Sessions want government access to stored data, to communications and to the cryptographic keys used to protect them. In Britain, the Investigatory Powers Act, slipped through parliament in the post-Brexit chaos, gives the Home Secretary wide powers to order such access (though much of what the UK agencies want is on US servers). The revelations by Ed Snowden revealed significant abuses by the NSA and GCHQ of the access they already had, and Brexit makes it likely that EU governments and courts will be very wary of letting British firms process data on EU nationals. The scene is set for serious tussles over privacy between governments, the IT industry and others. How are we to make sense of all this?

We have been here before. In the "Crypto Wars" of the 1990s, the US government tried repeatedly to grab control of civilian uses of cryptography using export controls, the Clipper chip, and attempts to license the "trusted third parties" in electronic commerce. Some other governments, including Britain's, joined in. Eventually, industry saw them off, supported by academia, NGOs and the European Commission. Mathematicians suddenly found ourselves in the trenches in a battle that set freedom against state control, law enforcement against privacy, enterprise against regulation and countries against each other. We won Crypto War I in 1999 when the EU passed the Electronic Signatures Directive and Al Gore abandoned the fight to control crypto in the USA in the hope of getting elected President. What are our chances in Crypto War II?

Ross Anderson FRS FREng did maths as an undergraduate and a PhD in computing. He is Professor of Security Engineering at Cambridge, and leads the Cambridge Cybercrime Centre, which collects and analyses data about online wickedness. He was one of the designers of the international standards for prepayment electricity metering and powerline communications; one of the inventors of the AES finalist encryption algorithm Serpent; a pioneer of peer-to-peer systems, hardware tamper-resistance and API security; and one of the founders of the discipline of security economics. He wrote the standard textbook "Security Engineering – A Guide to Building Dependable Distributed Systems". He is a Fellow of the Royal Society, the Royal Academy of Engineering, and the Institute of Physics, and a winner of the Lovelace Medal – the UK's top award in computing.

How good are you at persuading people? How easily can you be persuaded? Some little tricks in how you talk to people – and pay attention to what they say - can make all the difference. Join us for this seminar/workshop and learn about some effective listening techniques used by actual hostage negotiators, that can be used to convince people and lead them to open up to you, how to use them, and how to defend yourself against them.

Tom works for Aptivate, a Cambridge based IT company developing solutions for NGOs. He will talk about his experiences working at a ethically minded organisation in the charitable sector.

Guy has worked as a quant (financial mathematician) for the past 16 years, in a range of organisations: accounting firms, the Treasury, a bank, and most recently two energy trading companies. He will be talking about some of the ways in which ethical questions have impacted his work (and are likely to impact the work of anyone working as a quant), and reflect on some of the things he tries to consider when making decisions.

Mustafa is a Part III student here, and will be recapping some of the topics discussed last term, as well as discussing some of the decisions we might make in response to our ethical concerns.

As mathematicians we possess very particular talents, skills and training. We can do some very good things with these. We can also do some very bad things with these, in particular with the tools and techniques that we create. It is important to keep this in mind and to consider it when going out and working as a mathematician in a real-world setting. The universe extends far beyond the boundary of the pages we work on.

In our daily activities as working mathematicians we run the very real risk of harming ourselves, those around us, and more broadly the society we live in. This can occur under duress from others, through sheer social obliviousness, or as a result of our single-mindedness when it comes to problem solving. It can even occur at times when we are consciously trying to do good and be helpful.

It takes a degree of understanding and thought to guard against such eventualities. There is no deterministic algorithm for this; one must learn to act and respond more as a human, and not merely as a problem solving machine.

The purpose of these seminars is to equip mathematicians, as well as other technically-trained individuals, with some of the tools required to make ethical decisions and judgements in their line of work. First we need awareness: ethical issues in mathematics can be quite well-hidden from the average mathematician, so how do we identify them? Next we need motivation: now that we know our actions may have adverse effects, how do we weigh up whether to carry them out or not? Finally we need conviction: we may find ourselves under substantial pressure to act against our moral judgement, so how do we stand our ground and defend our decisions?

These seminars will be highly interactive, highly involved, and at times challenging. Through examples and activities we will learn how to develop awareness, motivation, and conviction. These tools will serve you well as you go out into society as a working mathematician.