Multidisciplinary advances towards AI for greater good.
Hosted by the University of Sydney and held at Doltone House in Pyrmont NSW, the international conference on the Ethics of Data Science brings together world-renowned experts from multiple disciplines to discuss the use and misuse of our data.
CTDS has partnered with the Gradient Institute, the Humanising Machine Intelligence group from the Australian National University and QuantumBlack to bring this conversation to Sydney.
Our goal is to design ethical evidence based decision-making frameworks. This can only be achieved by understanding the morality, law and politics of data and artificial intelligence, drawing on world-class research in data science, law, philosophy and beyond. This conference is a unique opportunity to engage with the cutting edge of research in these fields, and to make progress on understanding the viability and legitimacy of algorithmic decision-making.
The conference aims to bring established global leaders in these fields together with the emerging talent that will define these debates for years to come. Key themes explored by our keynotes will include fairness, privacy, algorithmic regulation, and how we receive and process information in the age of AI.
Alongside a high-level program of cutting-edge research, the conference will provide a forum for two-way knowledge transfer between researchers and practitioners, 'masterclasses' in which leading Australian scholars in ethical questions related to AI will engage with leaders in government and industry to both pinpoint the central problems faced in the deployment of algorithmic systems, and identify the paths to solving those problems.
|Early bird price||Full price|
Early bird pricing ends 31 January 2020. Full pricing from 1 February 2020.
Conference dinner is an additional $50.
Over the last few years, the world has awoken to the incredible power that we have all vested—often without thought or care—in the people and systems that collect, aggregate, analyse, and act on our data. Governments, universities, corporations and philanthropists the world over have recognised the urgent need to bring all of our intellectual tools to bear on charting a course through this opaque new normative territory. We've seen press release after press release, and funding announcement after funding announcement. But (with some notable exceptions) the actual interdisciplinary research on this topic has yet to reach its full potential. Scholars have been struggling to find a common language.
The early days of interdisciplinary work on the ethics of data science are now behind us. Fully-fledged research programs are well underway, or else swinging into action. Our goal with this conference is to bring together the fruits of that progress—to find the scholars, worldwide, who are genuinely pushing the boundaries of understanding in all areas related to the morality, law and politics of data and artificial intelligence. We are not looking for overviews or position papers, funding applications or project launches, or epicycles to existing debates. We're looking for the research that will frame the next phase of research in this area that will itself frame so much of the rest of our lives.
We are seeking papers from all academic disciplines relevant to understanding the morality, law, and politics of data and AI, with each of those terms understood inclusively. Our centres of gravity are in computer science, law, and philosophy, but our focus is on work that advances our understanding, not on disciplinary pigeonholes. We are committed to the view that these questions cannot be adequately understood without bringing together a diverse range of skill-sets, and building the community necessary for us to work towards a common goal. Authors of accepted papers will have enough time to state their thesis clearly, and start a genuine discussion on its merits.
So, submit your paper to the second edition of the international conference on the Ethics of Data Science on 25-27 March 2020 at the University of Sydney. We have enough travel scholarships to ensure that resources are no obstacle to presenting your world-leading research. If you're in doubt about whether your paper fits our conference profile, ask yourself this: is this in any way connected to better understanding the morality, law, or politics of data or AI? Is it world-class research? And can you convey its central insights to an interdisciplinary audience of very smart people? If you've answered 'yes' each time, then send us your paper. We're not going to list example problems. You already know what they are. And the point of this conference is to rejuvenate discussion on this topic, not reproduce frozen accidents.
Deadline for submission is 20 January 2020 AoE. Acceptance will be via double blinded review process. Full length papers should be submitted to the Open Review conference management system, with a limit of 6-8 pages. Papers will be assessed for their ability to engage with and inspire researchers in a broad range of disciplines, and to learn from and make an impact on real data science practice and/or its real world application.
Accepted papers with be notification on 10 February 2020.
The conference will provide travel grants to cover for registration, flights and/or accommodation fees. Applications open on 27 January 2020 and close 31 January 2020 AoE. Successful applications will be notified 14 February 2020.
The conference will give a Best Paper prize, based on the potential impact of the research. This paper will receive the special opportunity to present in an extended session oriented to attendees from government and industry.
The fire emergency situation across Australia has brought great difficulties in the working environment for many authors submitting to the conference within the original timeframes. We have decided to push back the dates of submission and paper review one week from the original dates. New dates are now reflected on the website.
Dr Roman Marchant, Senior Research Fellow and Lecturer, Centre for Translational Data Science
“Important decisions are being made by government and big corporations based on data. This conference will help guide the analysis and decisions to be ethical and beneficial for society as a whole.“
Dr Tiberio Caetano, Chief Scientist, The Gradient Institute
We know computers run the world, but we don't know how. The complex dynamics between algorithms, data, AI systems and people is effectively giving rise to a new social order yet to be understood. At the core of EDSC is the purpose of understanding of what has to be done, from a truly multidisciplinary perspective, to direct this dynamics towards a world of increasing human wellbeing, fairness and autonomy.
Professor Kimberlee Weatherall, Professor of Law, Sydney Law School
“This conference will help us address decisions around the use of new data science technology, where and how are they are appropriate to be used."
Professor Seth Lazar, Head of School Philosophy, Australian National University
“The world is clamouring for research to help better understand the morality, law, and politics of data and AI, but while there have been many strategic announcements and statements of ethical frameworks and principles, real interdisciplinary research in this area is in its infancy. EDSC2020, uniquely, is grounded in a genuine collaboration between computer scientists, lawyers and philosophers. It is interdisciplinary at its heart."
In ancient Greece, the basanos or touchstone had multiple meanings: a literal stone that tests the authenticity of gold by revealing its characteristic mark upon striking it, or metaphorically, a moral test of the authenticity of a life or a ruler. It also referred to a method of extracting truthful testimony by means of torture; specifically, of non-Greek slaves. The basanos thus embodies the interweaving of truth-telling with virtue, violence, and power in Western moral, political, and technical thought. In this talk I explore how contemporary uses of AI and data science have retraced and reconstituted the basanos in myriad ways, while also revealing a critical opportunity for the invention of new, more just and sustainable means of truth-telling.
Speaker: Shannon Vallor, Baillie Gifford Chair in the Ethics and Data of Artificial Intelligence, The University of Edinburgh.
Since it was introduced in 2006, differential privacy (DP) has become accepted as a gold standard for ensuring that individual-level information is not leaked through statistical analyses or machine learning on sensitive datasets. In recent years, it has seen large-scale deployments by Google, Apple, and the US Census Bureau, all organisations with the resources and expertise to implement their own custom DP systems. In this talk, I will describe our efforts to foster wider adoption of DP, including a system ("PSI") we are developing to enable researchers in the social sciences and other fields to share and explore privacy-sensitive datasets through data repositories, a new community effort ("OpenDP") to build a trusted open-source suite of DP tools, and work analysing the relationship between DP and legal requirements for privacy. This is joint work with many collaborators in the Privacy Tools Project. (http://privacytools.seas.harvard.edu/).
Speaker: Salil Vadhan, Vicky Joseph Professor of Computer Science and Applied Mathematics, Harvard University.
Interdisciplinary Professorial Fellow in Law, Ethics and Informatics at the University of Birmingham, based in Birmingham Law School and the School of Computer Science.
Details of Karen's keynote presentation coming soon.
A day full of insights and tutorials that introduce the audience to the main areas of research covered in the conference. A key aspect of a truly multidisciplinary conference, the first of its kind, where you get to hear from experts in each area and foster the exchange of information across disciplines.
Details on this masterclass coming soon.
Speaker: Associate Professor Julia Powles, Associate Professor of Technology Law & Policy, University of Western Australia.
As governments and companies rush to develop principles for the ethical use of data and AI, with equal alacrity academics and activists lament the focus on 'soft' ethics, at the expense of 'hard' law. But law isn't always hard—in fact, it is often indeterminate, and often with good reason. And ethical principles can be very demanding. But what's the right way to think about the connection between morality and law? And how do either relate to the realities of politics, and power? Philosophy can help make sense of these questions. In this co-learning class, I'll be soliciting people's views on how the morality, law and politics of data and AI interact in their daily practice, and offering insights from philosophy on how to make sense of the subtle connections between these different sources of normativity—each of them irreducible to the others.
Speaker: Associate Professor Seth Lazar, Head, School of Philosophy. Project Lead, Humanising Machine Intelligence Grand Challenge, Australian National University.
Researchers interested in employing large data sets are typically given broad access and trusted to operate honourably - an approach that has proven manifestly inadequate (c.f. Cambridge Analytica). In this class, we will introduce the Private data analysis language which allows researchers to analyse data without having access to the data. Private is a probabilistic declarative language based on Python and BUGS which blocks from release any results that are sensitive to the data provided by any given individual. We will also argue that users/participants/citizens should retain ownership of and derive ongoing benefit from their data and introduce the Unforgettable data marketplace, as an example of how this can be achieved.
Speaker: Professor Simon Dennis, The University of Melbourne.
This master class will discuss the challenges inherent in designing algorithms to be fair and non-discriminatory from a computer-science perspective. We will describe current approaches to formalising fairness in the machine learning literature, explain what these metrics do and do not capture, and how they can be implemented. Finally, we will place algorithmic fairness in the wider context of Australian anti-discrimination law and algorithmic transparency. The tutorial will focus on concepts and practical algorithms, and is intended for a multidisciplinary audience.