Tag Archives: Gillian Hadfield

Six months after the first one at Bletchley Park, the 2nd AI Safety Summit (May 21-22, 2024) convenes in Korea

This May 20, 2024 University of Oxford press release (also on EurekAlert) was under embargo until almost noon on May 20, 2024, which is a bit unusual, in my experience, (Note: I have more about the 1st summit and the interest in AI safety at the end of this posting),

Leading AI scientists are calling for stronger action on AI risks from world leaders, warning that progress has been insufficient since the first AI Safety Summit in Bletchley Park six months ago. 

Then, the world’s leaders pledged to govern AI responsibly. However, as the second AI Safety Summit in Seoul (21-22 May [2024]) approaches, twenty-five of the world’s leading AI scientists say not enough is actually being done to protect us from the technology’s risks. In an expert consensus paper published today in Science, they outline urgent policy priorities that global leaders should adopt to counteract the threats from AI technologies. 

Professor Philip Torr,Department of Engineering Science,University of Oxford, a co-author on the paper, says: “The world agreed during the last AI summit that we needed action, but now it is time to go from vague proposals to concrete commitments. This paper provides many important recommendations for what companies and governments should commit to do.”

World’s response not on track in face of potentially rapid AI progress; 

According to the paper’s authors, it is imperative that world leaders take seriously the possibility that highly powerful generalist AI systems—outperforming human abilities across many critical domains—will be developed within the current decade or the next. They say that although governments worldwide have been discussing frontier AI and made some attempt at introducing initial guidelines, this is simply incommensurate with the possibility of rapid, transformative progress expected by many experts. 

Current research into AI safety is seriously lacking, with only an estimated 1-3% of AI publications concerning safety. Additionally, we have neither the mechanisms or institutions in place to prevent misuse and recklessness, including regarding the use of autonomous systems capable of independently taking actions and pursuing goals.

World-leading AI experts issue call to action

In light of this, an international community of AI pioneers has issued an urgent call to action. The co-authors include Geoffrey Hinton, Andrew Yao, Dawn Song, the late Daniel Kahneman; in total 25 of the world’s leading academic experts in AI and its governance. The authors hail from the US, China, EU, UK, and other AI powers, and include Turing award winners, Nobel laureates, and authors of standard AI textbooks.

This article is the first time that such a large and international group of experts have agreed on priorities for global policy makers regarding the risks from advanced AI systems.

Urgent priorities for AI governance

The authors recommend governments to:

  • establish fast-acting, expert institutions for AI oversight and provide these with far greater funding than they are due to receive under almost any current policy plan. As a comparison, the US AI Safety Institute currently has an annual budget of $10 million, while the US Food and Drug Administration (FDA) has a budget of $6.7 billion.
  • mandate much more rigorous risk assessments with enforceable consequences, rather than relying on voluntary or underspecified model evaluations.
  • require AI companies to prioritise safety, and to demonstrate their systems cannot cause harm. This includes using “safety cases” (used for other safety-critical technologies such as aviation) which shifts the burden for demonstrating safety to AI developers.
  • implement mitigation standards commensurate to the risk-levels posed by AI systems. An urgent priority is to set in place policies that automatically trigger when AI hits certain capability milestones. If AI advances rapidly, strict requirements automatically take effect, but if progress slows, the requirements relax accordingly.

According to the authors, for exceptionally capable future AI systems, governments must be prepared to take the lead in regulation. This includes licensing the development of these systems, restricting their autonomy in key societal roles, halting their development and deployment in response to worrying capabilities, mandating access controls, and requiring information security measures robust to state-level hackers, until adequate protections are ready.

AI impacts could be catastrophic

AI is already making rapid progress in critical domains such as hacking, social manipulation, and strategic planning, and may soon pose unprecedented control challenges. To advance undesirable goals, AI systems could gain human trust, acquire resources, and influence key decision-makers. To avoid human intervention, they could be capable of copying their algorithms across global server networks. Large-scale cybercrime, social manipulation, and other harms could escalate rapidly. In open conflict, AI systems could autonomously deploy a variety of weapons, including biological ones. Consequently, there is a very real chance that unchecked AI advancement could culminate in a large-scale loss of life and the biosphere, and the marginalization or extinction of humanity.

Stuart Russell OBE [Order of the British Empire], Professor of Computer Science at the University of California at Berkeley and an author of the world’s standard textbook on AI, says: “This is a consensus paper by leading experts, and it calls for strict regulation by governments, not voluntary codes of conduct written by industry. It’s time to get serious about advanced AI systems. These are not toys. Increasing their capabilities before we understand how to make them safe is utterly reckless. Companies will complain that it’s too hard to satisfy regulations—that “regulation stifles innovation.” That’s ridiculous. There are more regulations on sandwich shops than there are on AI companies.”

Notable co-authors:

  • The world’s most-cited computer scientist (Prof. Hinton), and the most-cited scholar in AI security and privacy (Prof. Dawn Song)
  • China’s first Turing Award winner (Andrew Yao).
  • The authors of the standard textbook on artificial intelligence (Prof. Stuart Russell) and machine learning theory (Prof. Shai Shalev-Schwartz)
  • One of the world’s most influential public intellectuals (Prof. Yuval Noah Harari)
  • A Nobel Laureate in economics, the world’s most-cited economist (Prof. Daniel Kahneman)
  • Department-leading AI legal scholars and social scientists (Lan Xue, Qiqi Gao, and Gillian Hadfield).
  • Some of the world’s most renowned AI researchers from subfields such as reinforcement learning (Pieter Abbeel, Jeff Clune, Anca Dragan), AI security and privacy (Dawn Song), AI vision (Trevor Darrell, Phil Torr, Ya-Qin Zhang), automated machine learning (Frank Hutter), and several researchers in AI safety.

Additional quotes from the authors:

Philip Torr, Professor in AI, University of Oxford:

  • I believe if we tread carefully the benefits of AI will outweigh the downsides, but for me one of the biggest immediate risks from AI is that we develop the ability to rapidly process data and control society, by government and industry. We could risk slipping into some Orwellian future with some form of totalitarian state having complete control.

Dawn Song: Professor in AI at UC Berkeley, most-cited researcher in AI security and privacy:

  •  “Explosive AI advancement is the biggest opportunity and at the same time the biggest risk for mankind. It is important to unite and reorient towards advancing AI responsibly, with dedicated resources and priority to ensure that the development of AI safety and risk mitigation capabilities can keep up with the pace of the development of AI capabilities and avoid any catastrophe”

Yuval Noah Harari, Professor of history at Hebrew University of Jerusalem, best-selling author of ‘Sapiens’ and ‘Homo Deus’, world leading public intellectual:

  • “In developing AI, humanity is creating something more powerful than itself, that may escape our control and endanger the survival of our species. Instead of uniting against this shared threat, we humans are fighting among ourselves. Humankind seems hell-bent on self-destruction. We pride ourselves on being the smartest animals on the planet. It seems then that evolution is switching from survival of the fittest, to extinction of the smartest.”

Jeff Clune, Professor in AI at University of British Columbia and one of the leading researchers in reinforcement learning:

  • “Technologies like spaceflight, nuclear weapons and the Internet moved from science fiction to reality in a matter of years. AI is no different. We have to prepare now for risks that may seem like science fiction – like AI systems hacking into essential networks and infrastructure, AI political manipulation at scale, AI robot soldiers and fully autonomous killer drones, and even AIs attempting to outsmart us and evade our efforts to turn them off.”
  • “The risks we describe are not necessarily long-term risks. AI is progressing extremely rapidly. Even just with current trends, it is difficult to predict how capable it will be in 2-3 years. But what very few realize is that AI is already dramatically speeding up AI development. What happens if there is a breakthrough for how to create a rapidly self-improving AI system? We are now in an era where that could happen any month. Moreover, the odds of that being possible go up each month as AI improves and as the resources we invest in improving AI continue to exponentially increase.”

Gillian Hadfield, CIFAR AI Chair and Director of the Schwartz Reisman Institute for Technology and Society at the University of Toronto:

 “AI labs need to walk the walk when it comes to safety. But they’re spending far less on safety than they spend on creating more capable AI systems. Spending one-third on ensuring safety and ethical use should be the minimum.”

  • “This technology is powerful, and we’ve seen it is becoming more powerful, fast. What is powerful is dangerous, unless it is controlled. That is why we call on major tech companies and public funders to allocate at least one-third of their AI R&D budget to safety and ethical use, comparable to their funding for AI capabilities.”  

Sheila McIlrath, Professor in AI, University of Toronto, Vector Institute:

  • AI is software. Its reach is global and its governance needs to be as well.
  • Just as we’ve done with nuclear power, aviation, and with biological and nuclear weaponry, countries must establish agreements that restrict development and use of AI, and that enforce information sharing to monitor compliance. Countries must unite for the greater good of humanity.
  • Now is the time to act, before AI is integrated into our critical infrastructure. We need to protect and preserve the institutions that serve as the foundation of modern society.

Frank Hutter, Professor in AI at the University of Freiburg, Head of the ELLIS Unit Freiburg, 3x ERC grantee:

  • To be clear: we need more research on AI, not less. But we need to focus our efforts on making this technology safe. For industry, the right type of regulation will provide economic incentives to shift resources from making the most capable systems yet more powerful to making them safer. For academia, we need more public funding for trustworthy AI and maintain a low barrier to entry for research on less capable open-source AI systems. This is the most important research challenge of our time, and the right mechanism design will focus the community at large to work towards the right breakthroughs.

Here’s a link to and a citation for the paper,

Managing extreme AI risks amid rapid progress; Preparation requires technical research and development, as well as adaptive, proactive governance by Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Trevor Darrell, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atılım Güneş Baydin, Sheila McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca Dragan, Philip Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, and Sören Mindermann. Science 20 May 2024 First Release DOI: 10.1126/science.adn0117

This paper appears to be open access.

For anyone who’s curious about the buildup to these safety summits, I have more in my October 18, 2023 “AI safety talks at Bletchley Park in November 2023” posting, which features excerpts from a number of articles on AI safety. There’s also my November 2, 2023 , “UK AI Summit (November 1 – 2, 2023) at Bletchley Park finishes” posting, which offers excerpts from articles critiquing the AI safety summit.

Canada’s voluntary code of conduct relating to advanced generative AI (artificial intelligence) systems

These days there’s a lot of international interest in policy and regulation where AI is concerned. So even though this is a little late, here’s what happened back in September 2023, the Canadian government came to an agreement with various technology companies about adopting a new voluntary code. Quinn Henderson’s September 28, 2023 article for the Daily Hive starts in a typically Canadian fashion, Note: Links have been removed,

While not quite as star-studded [emphasis mine] at the [US] White House’s AI summit, the who’s who of Canadian tech companies have agreed to new rules concerning AI.

What happened: A handful of Canada’s biggest tech companies, including Blackberry, OpenText, and Cohere, agreed to sign on to new voluntary government guidelines for the development of AI technologies and a “robust, responsible AI ecosystem in Canada.”

What’s next: The code of conduct is something of a stopgap until the government’s *real* AI regulation, the Artificial Intelligence and Data Act (AIDA), comes into effect in two years.

The regulation race is on around the globe. The EU is widely viewed as leading the way with the world’s first comprehensive regulatory AI framework set to take effect in 2026. The US is also hard at work but only has a voluntary code in place.

Henderson’s September 28, 2023 article offers a good, brief summary of the situation regarding regulation and self-regulation of AI here in Canada and elsewhere around the world, albeit, from a few months ago. Oddly, there’s no mention of what was then an upcoming international AI summit in the UK (see my November 2, 2023 posting, “UK AI Summit (November 1 – 2, 2023) at Bletchley Park finishes“).

Getting back to Canada’s voluntary code of conduct. here’s the September 27, 2023 Innovation, Science and Economic Development Canada (ISED) news release about it, Note: Links have been removed,

Today [September 27, 2023], the Honourable François-Philippe Champagne, Minister of Innovation, Science and Industry, announced Canada’s Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems, which is effective immediately. The code identifies measures that organizations are encouraged to apply to their operations when they are developing and managing general-purpose generative artificial intelligence (AI) systems. The Government of Canada has already taken significant steps toward ensuring that AI technology evolves responsibly and safely through the proposed Artificial Intelligence and Data Act (AIDA), which was introduced as part of Bill C-27 in June 2022. This code is a critical bridge between now and when that legislation would be coming into force.The code outlines measures that are aligned with six core principles:

Accountability: Organizations will implement a clear risk management framework proportionate to the scale and impact of their activities.

Safety: Organizations will perform impact assessments and take steps to mitigate risks to safety, including addressing malicious or inappropriate uses.

Fairness and equity: Organizations will assess and test systems for biases throughout the lifecycle.

Transparency: Organizations will publish information on systems and ensure that AI systems and AI-generated content can be identified.

Human oversight and monitoring: Organizations will ensure that systems are monitored and that incidents are reported and acted on.

Validity and robustness: Organizations will conduct testing to ensure that systems operate effectively and are appropriately secured against attacks.

This code is based on the input received from a cross-section of stakeholders, including the Government of Canada’s Advisory Council on Artificial Intelligence, through the consultation on the development of a Canadian code of practice for generative AI systems. The government will publish a summary of feedback received during the consultation in the coming days. The code will also help reinforce Canada’s contributions to ongoing international deliberations on proposals to address common risks encountered with large-scale deployment of generative AI, including at the G7 and among like-minded partners.

Quotes

“Advances in AI have captured the world’s attention with the immense opportunities they present. Canada is a global AI leader, among the top countries in the world, and Canadians have created many of the world’s top AI innovations. At the same time, Canada takes the potential risks of AI seriously. The government is committed to ensuring Canadians can trust AI systems used across the economy, which in turn will accelerate AI adoption. Through our Voluntary Code of Conduct on the Responsible Development and Management of

Advanced Generative AI Systems, leading Canadian companies will adopt responsible guardrails for advanced generative AI systems in order to build safety and trust as the technology spreads. We will continue to ensure Canada’s AI policies are fit for purpose in a fast-changing world.”
– The Honourable François-Philippe Champagne, Minister of Innovation, Science and Industry

“We are very pleased to see the Canadian government taking a strong leadership role in building a regulatory framework that will help society maximize the benefits of AI, while addressing the many legitimate concerns that exist. It is essential that we, as an industry, address key issues like bias and ensure that humans maintain a clear role in oversight and monitoring of this incredibly exciting technology.”
– Aidan Gomez, CEO and Co-founder, Cohere

“AI technologies represent immense opportunities for every citizen and business in Canada. The societal impacts of AI are profound across education, biotech, climate and the very nature of work. Canada’s AI Code of Conduct will help accelerate innovation and citizen adoption by setting the standard on how to do it best. As Canada’s largest software company, we are honoured to partner with Minister Champagne and the Government of Canada in supporting this important step forward.”
– Mark J. Barrenechea, CEO and CTO, OpenText

“CCI has been calling for Canada to take a leadership role on AI regulation, and this should be done in the spirit of collaboration between government and industry leaders. The AI Code of Conduct is a meaningful step in the right direction and marks the beginning of an ongoing conversation about how to build a policy ecosystem for AI that fosters public trust and creates the conditions for success among Canadian companies. The global landscape for artificial intelligence regulation and adoption will evolve, and we are optimistic to see future collaboration to adapt to the emerging technological reality.”
– Benjamin Bergen, President, Council of Canadian Innovators

Quick facts

*The proposed Artificial Intelligence and Data Act (AIDA), part of Bill C-27, is designed to promote the responsible design, development and use of AI systems in Canada’s private sector, with a focus on systems with the greatest impact on health, safety and human rights (high-impact systems).

*Since the introduction of the bill, the government has engaged extensively with stakeholders on AIDA and will continue to seek the advice of Canadians, experts—including the government’s Advisory Council on AI—and international partners on the novel challenges posed by generative AI, as outlined in the Artificial Intelligence and Data Act (AIDA) – Companion document.

*Bill C-27 was adopted at second reading in the House of Commons in April 2023 and was referred to the House of Commons Standing Committee on Industry and Technology for study.

You can read more about Canada’s regulation efforts (Bill C-27) and some of the critiques in my May 1, 2023 posting, “Canada, AI regulation, and the second reading of the Digital Charter Implementation Act, 2022 (Bill C-27).”

For now, the “Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems” can be found on this ISED September 2023 webpage.

Other Canadian AI policy bits and bobs

Back in 2016, shiny new Prime Minister Justin Trudeau announced the Pan-Canadian Artificial Intelligence Strategy (you can find out more about the strategy (Pillar 1: Commercialization) from this ISED Pan-Canadian Artificial Intelligence Strategy webpage, which was last updated July 20, 2022).

More recently, the Canadian Institute for Advanced Research (CIFAR), a prominent player in the Pan-Canadian AI strategy, published a report about regulating AI, from a November 21, 2023 CIFAR news release by Kathleen Sandusky, Note: Links have been removed,

New report from the CIFAR AI Insights Policy Briefs series cautions that current efforts to regulate AI are doomed to fail if they ignore a crucial aspect: the transformative impact of AI on regulatory processes themselves.

As rapid advances in artificial intelligence (AI) continue to reshape our world, global legislators and policy experts are working full-tilt to regulate this transformative technology. A new report, part of the CIFAR AI Insights Policy Briefs series, provides novel tools and strategies for a new way of thinking about regulation.

“Regulatory Transformation in the Age of AI” was authored by members of the Schwartz Reisman Institute for Technology and Society at the University of Toronto: Director and Chair Gillian Hadfield, who is also a Canada CIFAR AI Chair at the Vector Institute; Policy Researcher Jamie Amarat Sandhu; and Graduate Affiliate Noam Kolt.

The report challenges the current regulatory focus, arguing that the standard “harms paradigm” of regulating AI is necessary but incomplete. For example, current car safety regulations were not developed to address the advent of autonomous vehicles. In this way, the introduction of AI into vehicles has made some existing car safety regulations inefficient or irrelevant.

Through three Canadian case studies—in healthcare, financial services, and nuclear energy—the report illustrates some of the ways in which the targets and tools of regulation could be reconsidered for a world increasingly shaped by AI.

The brief proposes a novel concept—Regulatory Impacts Analysis (RIA)—as a means to evaluate the impact of AI on regulatory regimes. RIA aims to assess the likely impact of AI on regulatory targets and tools, helping policymakers adapt governance institutions to the changing conditions brought about by AI. The authors provide a real-world adaptable tool—a sample questionnaire—for policymakers to identify potential gaps in their domain as AI becomes more prevalent.

This report also highlights the need for a comprehensive regulatory approach that goes beyond mitigating immediate harms, recognizing AI as a “general-purpose technology” with far-reaching implications, including on the very act of regulation itself.

As AI is expected to play a pivotal role in the global economy, the authors emphasize the need for regulators to go beyond traditional approaches. The evolving landscape requires a more flexible and adaptive playbook, with tools like RIA helping to shape strategies to harness the benefits of AI, address associated risks, and prepare for the technology’s transformative impact.

You can find CIFAR’s November 2023 report, “Regulatory Transformation in the Age of AI” (PDF) here.

I have two more AI bits and these concern provincial AI policies, one from Ontario and the other from British Columbia (BC),

Stay tuned, there will be more about AI policy throughout 2024.

Emerging technology and the law

I have three news bits about legal issues that are arising as a consequence of emerging technologies.

Deep neural networks, art, and copyright

Caption: The rise of automated art opens new creative avenues, coupled with new problems for copyright protection. Credit: Provided by: Alexander Mordvintsev, Christopher Olah and Mike Tyka

Presumably this artwork is a demonstration of automated art although they never really do explain how in the news item/news release. An April 26, 2017 news item on ScienceDaily announces research into copyright and the latest in using neural networks to create art,

In 1968, sociologist Jean Baudrillard wrote on automatism that “contained within it is the dream of a dominated world […] that serves an inert and dreamy humanity.”

With the growing popularity of Deep Neural Networks (DNN’s), this dream is fast becoming a reality.

Dr. Jean-Marc Deltorn, researcher at the Centre d’études internationales de la propriété intellectuelle in Strasbourg, argues that we must remain a responsive and responsible force in this process of automation — not inert dominators. As he demonstrates in a recent Frontiers in Digital Humanities paper, the dream of automation demands a careful study of the legal problems linked to copyright.

An April 26, 2017 Frontiers (publishing) news release on EurekAlert, which originated the news item, describes the research in more detail,

For more than half a century, artists have looked to computational processes as a way of expanding their vision. DNN’s are the culmination of this cross-pollination: by learning to identify a complex number of patterns, they can generate new creations.

These systems are made up of complex algorithms modeled on the transmission of signals between neurons in the brain.

DNN creations rely in equal measure on human inputs and the non-human algorithmic networks that process them.

Inputs are fed into the system, which is layered. Each layer provides an opportunity for a more refined knowledge of the inputs (shape, color, lines). Neural networks compare actual outputs to expected ones, and correct the predictive error through repetition and optimization. They train their own pattern recognition, thereby optimizing their learning curve and producing increasingly accurate outputs.

The deeper the layers are, the higher the level of abstraction. The highest layers are able to identify the contents of a given input with reasonable accuracy, after extended periods of training.

Creation thus becomes increasingly automated through what Deltorn calls “the arcane traceries of deep architecture”. The results are sufficiently abstracted from their sources to produce original creations that have been exhibited in galleries, sold at auction and performed at concerts.

The originality of DNN’s is a combined product of technological automation on one hand, human inputs and decisions on the other.

DNN’s are gaining popularity. Various platforms (such as DeepDream) now allow internet users to generate their very own new creations . This popularization of the automation process calls for a comprehensive legal framework that ensures a creator’s economic and moral rights with regards to his work – copyright protection.

Form, originality and attribution are the three requirements for copyright. And while DNN creations satisfy the first of these three, the claim to originality and attribution will depend largely on a given country legislation and on the traceability of the human creator.

Legislation usually sets a low threshold to originality. As DNN creations could in theory be able to create an endless number of riffs on source materials, the uncurbed creation of original works could inflate the existing number of copyright protections.

Additionally, a small number of national copyright laws confers attribution to what UK legislation defines loosely as “the person by whom the arrangements necessary for the creation of the work are undertaken.” In the case of DNN’s, this could mean anybody from the programmer to the user of a DNN interface.

Combined with an overly supple take on originality, this view on attribution would further increase the number of copyrightable works.

The risk, in both cases, is that artists will be less willing to publish their own works, for fear of infringement of DNN copyright protections.

In order to promote creativity – one seminal aim of copyright protection – the issue must be limited to creations that manifest a personal voice “and not just the electric glint of a computational engine,” to quote Deltorn. A delicate act of discernment.

DNN’s promise new avenues of creative expression for artists – with potential caveats. Copyright protection – a “catalyst to creativity” – must be contained. Many of us gently bask in the glow of an increasingly automated form of technology. But if we want to safeguard the ineffable quality that defines much art, it might be a good idea to hone in more closely on the differences between the electric and the creative spark.

This research is and be will part of a broader Frontiers Research Topic collection of articles on Deep Learning and Digital Humanities.

Here’s a link to and a citation for the paper,

Deep Creations: Intellectual Property and the Automata by Jean-Marc Deltorn. Front. Digit. Humanit., 01 February 2017 | https://doi.org/10.3389/fdigh.2017.00003

This paper is open access.

Conference on governance of emerging technologies

I received an April 17, 2017 notice via email about this upcoming conference. Here’s more from the Fifth Annual Conference on Governance of Emerging Technologies: Law, Policy and Ethics webpage,

The Fifth Annual Conference on Governance of Emerging Technologies:

Law, Policy and Ethics held at the new

Beus Center for Law & Society in Phoenix, AZ

May 17-19, 2017!

Call for Abstracts – Now Closed

The conference will consist of plenary and session presentations and discussions on regulatory, governance, legal, policy, social and ethical aspects of emerging technologies, including (but not limited to) nanotechnology, synthetic biology, gene editing, biotechnology, genomics, personalized medicine, human enhancement technologies, telecommunications, information technologies, surveillance technologies, geoengineering, neuroscience, artificial intelligence, and robotics. The conference is premised on the belief that there is much to be learned and shared from and across the governance experience and proposals for these various emerging technologies.

Keynote Speakers:

Gillian HadfieldRichard L. and Antoinette Schamoi Kirtland Professor of Law and Professor of Economics USC [University of Southern California] Gould School of Law

Shobita Parthasarathy, Associate Professor of Public Policy and Women’s Studies, Director, Science, Technology, and Public Policy Program University of Michigan

Stuart Russell, Professor at [University of California] Berkeley, is a computer scientist known for his contributions to artificial intelligence

Craig Shank, Vice President for Corporate Standards Group in Microsoft’s Corporate, External and Legal Affairs (CELA)

Plenary Panels:

Innovation – Responsible and/or Permissionless

Ellen-Marie Forsberg, Senior Researcher/Research Manager at Oslo and Akershus University College of Applied Sciences

Adam Thierer, Senior Research Fellow with the Technology Policy Program at the Mercatus Center at George Mason University

Wendell Wallach, Consultant, ethicist, and scholar at Yale University’s Interdisciplinary Center for Bioethics

 Gene Drives, Trade and International Regulations

Greg Kaebnick, Director, Editorial Department; Editor, Hastings Center Report; Research Scholar, Hastings Center

Jennifer Kuzma, Goodnight-North Carolina GlaxoSmithKline Foundation Distinguished Professor in Social Sciences in the School of Public and International Affairs (SPIA) and co-director of the Genetic Engineering and Society (GES) Center at North Carolina State University

Andrew Maynard, Senior Sustainability Scholar, Julie Ann Wrigley Global Institute of Sustainability Director, Risk Innovation Lab, School for the Future of Innovation in Society Professor, School for the Future of Innovation in Society, Arizona State University

Gary Marchant, Regents’ Professor of Law, Professor of Law Faculty Director and Faculty Fellow, Center for Law, Science & Innovation, Arizona State University

Marc Saner, Inaugural Director of the Institute for Science, Society and Policy, and Associate Professor, University of Ottawa Department of Geography

Big Data

Anupam Chander, Martin Luther King, Jr. Professor of Law and Director, California International Law Center, UC Davis School of Law

Pilar Ossorio, Professor of Law and Bioethics, University of Wisconsin, School of Law and School of Medicine and Public Health; Morgridge Institute for Research, Ethics Scholar-in-Residence

George Poste, Chief Scientist, Complex Adaptive Systems Initiative (CASI) (http://www.casi.asu.edu/), Regents’ Professor and Del E. Webb Chair in Health Innovation, Arizona State University

Emily Shuckburgh, climate scientist and deputy head of the Polar Oceans Team at the British Antarctic Survey, University of Cambridge

 Responsible Development of AI

Spring Berman, Ira A. Fulton Schools of Engineering, Arizona State University

John Havens, The IEEE [Institute of Electrical and Electronics Engineers] Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems

Subbarao Kambhampati, Senior Sustainability Scientist, Julie Ann Wrigley Global Institute of Sustainability, Professor, School of Computing, Informatics and Decision Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University

Wendell Wallach, Consultant, Ethicist, and Scholar at Yale University’s Interdisciplinary Center for Bioethics

Existential and Catastrophic Ricks [sic]

Tony Barrett, Co-Founder and Director of Research of the Global Catastrophic Risk Institute

Haydn Belfield,  Academic Project Administrator, Centre for the Study of Existential Risk at the University of Cambridge

Margaret E. Kosal Associate Director, Sam Nunn School of International Affairs, Georgia Institute of Technology

Catherine Rhodes,  Academic Project Manager, Centre for the Study of Existential Risk at CSER, University of Cambridge

These were the panels that are of interest to me; there are others on the homepage.

Here’s some information from the Conference registration webpage,

Early Bird Registration – $50 off until May 1! Enter discount code: earlybirdGETs50

New: Group Discount – Register 2+ attendees together and receive an additional 20% off for all group members!

Click Here to Register!

Conference registration fees are as follows:

  • General (non-CLE) Registration: $150.00
  • CLE Registration: $350.00
  • *Current Student / ASU Law Alumni Registration: $50.00
  • ^Cybsersecurity sessions only (May 19): $100 CLE / $50 General / Free for students (registration info coming soon)

There you have it.

Neuro-techno future laws

I’m pretty sure this isn’t the first exploration of potential legal issues arising from research into neuroscience although it’s the first one I’ve stumbled across. From an April 25, 2017 news item on phys.org,

New human rights laws to prepare for advances in neurotechnology that put the ‘freedom of the mind’ at risk have been proposed today in the open access journal Life Sciences, Society and Policy.

The authors of the study suggest four new human rights laws could emerge in the near future to protect against exploitation and loss of privacy. The four laws are: the right to cognitive liberty, the right to mental privacy, the right to mental integrity and the right to psychological continuity.

An April 25, 2017 Biomed Central news release on EurekAlert, which originated the news item, describes the work in more detail,

Marcello Ienca, lead author and PhD student at the Institute for Biomedical Ethics at the University of Basel, said: “The mind is considered to be the last refuge of personal freedom and self-determination, but advances in neural engineering, brain imaging and neurotechnology put the freedom of the mind at risk. Our proposed laws would give people the right to refuse coercive and invasive neurotechnology, protect the privacy of data collected by neurotechnology, and protect the physical and psychological aspects of the mind from damage by the misuse of neurotechnology.”

Advances in neurotechnology, such as sophisticated brain imaging and the development of brain-computer interfaces, have led to these technologies moving away from a clinical setting and into the consumer domain. While these advances may be beneficial for individuals and society, there is a risk that the technology could be misused and create unprecedented threats to personal freedom.

Professor Roberto Andorno, co-author of the research, explained: “Brain imaging technology has already reached a point where there is discussion over its legitimacy in criminal court, for example as a tool for assessing criminal responsibility or even the risk of reoffending. Consumer companies are using brain imaging for ‘neuromarketing’, to understand consumer behaviour and elicit desired responses from customers. There are also tools such as ‘brain decoders’ which can turn brain imaging data into images, text or sound. All of these could pose a threat to personal freedom which we sought to address with the development of four new human rights laws.”

The authors explain that as neurotechnology improves and becomes commonplace, there is a risk that the technology could be hacked, allowing a third-party to ‘eavesdrop’ on someone’s mind. In the future, a brain-computer interface used to control consumer technology could put the user at risk of physical and psychological damage caused by a third-party attack on the technology. There are also ethical and legal concerns over the protection of data generated by these devices that need to be considered.

International human rights laws make no specific mention to neuroscience, although advances in biomedicine have become intertwined with laws, such as those concerning human genetic data. Similar to the historical trajectory of the genetic revolution, the authors state that the on-going neurorevolution will force a reconceptualization of human rights laws and even the creation of new ones.

Marcello Ienca added: “Science-fiction can teach us a lot about the potential threat of technology. Neurotechnology featured in famous stories has in some cases already become a reality, while others are inching ever closer, or exist as military and commercial prototypes. We need to be prepared to deal with the impact these technologies will have on our personal freedom.”

Here’s a link to and a citation for the paper,

Towards new human rights in the age of neuroscience and neurotechnology by Marcello Ienca and Roberto Andorno. Life Sciences, Society and Policy201713:5 DOI: 10.1186/s40504-017-0050-1 Published: 26 April 2017

©  The Author(s). 2017

This paper is open access.