How Copyright Law Can Fix Artificial Intelligence’s Implicit Bias Problem

Curated on Posted on by Stefaan Verhulst

Paper by Amanda Levendowski: “As the use of artificial intelligence (AI) continues to spread, we have seen an increase in examples of AI systems reflecting or exacerbating societal bias, from racist facial recognition to sexist natural language processing. These biases threaten to overshadow AI’s technological gains and potential benefits. While legal and computer science scholars have analyzed many sources of bias, including the unexamined assumptions of its often-homogenous creators, flawed algorithms, and incomplete datasets, the role of the law itself has been largely ignored. Yet just as code and culture play significant roles in how AI agents learn about and act in the world, so too do the laws that govern them. This Article is the first to examine perhaps the most powerful law impacting AI bias: copyright.

Artificial intelligence often learns to “think” by reading, viewing, and listening to copies of human works. This Article first explores the problem of bias through the lens of copyright doctrine, looking at how the law’s exclusion of access to certain copyrighted source materials may create or promote biased AI systems. Copyright law limits bias mitigation techniques, such as testing AI through reverse engineering, algorithmic accountability processes, and competing to convert customers. The rules of copyright law also privilege access to certain works over others, encouraging AI creators to use easily available, legally low-risk sources of data for teaching AI, even when those data are demonstrably biased. Second, it examines how a different part of copyright law — the fair use doctrine — has traditionally been used to address similar concerns in other technological fields, and asks whether it is equally capable of addressing them in the field of AI bias. The Article ultimately concludes that it is, in large part because the normative values embedded within traditional fair use ultimately align with the goals of mitigating AI bias and, quite literally, creating fairer AI systems….(More)”.

How We Can Stop Earthquakes From Killing People Before They Even Hit

Curated on Posted on by Stefaan Verhulst

Justin Worland in Time Magazine: “…Out of that realization came a plan to reshape disaster management using big data. Just a few months later, Wani worked with two fellow Stanford students to create a platform to predict the toll of natural disasters. The concept is simple but also revolutionary. The One Concern software pulls geological and structural data from a variety of public and private sources and uses machine learning to predict the impact of an earthquake down to individual city blocks and buildings. Real-time information input during an earthquake improves how the system responds. And earthquakes represent just the start for the company, which plans to launch a similar program for floods and eventually other natural disasters….

Previous software might identify a general area where responders could expect damage, but it would appear as a “big red blob” that wasn’t helpful when deciding exactly where to send resources, Dayton says. The technology also integrates information from many sources and makes it easy to parse in an emergency situation when every moment matters. The instant damage evaluations mean fast and actionable information, so first responders can prioritize search and rescue in areas most likely to be worst-hit, rather than responding to 911 calls in the order they are received.

One Concern is not the only company that sees an opportunity to use data to rethink disaster response. The mapping company Esri has built rapid-response software that shows expected damage from disasters like earthquakes, wildfires and hurricanes. And the U.S. government has invested in programs to use data to shape disaster response at agencies like the National Oceanic and Atmospheric Administration (NOAA)….(More)”.

Let’s create a nation of social scientists

Curated on Posted on by Stefaan Verhulst

Geoff Mulgan in Times Higher Education: “How might social science become more influential, more relevant and more useful in the years to come?

Recent debates about impact have largely assumed a model of social science in which a cadre of specialists, based in universities, analyse and interpret the world and then feed conclusions into an essentially passive society. But a very different view sees specialists in the academy working much more in partnership with a society that is itself skilled in social science, able to generate hypotheses, gather data, experiment and draw conclusions that might help to answer the big questions of our time, from the sources of inequality to social trust, identity to violence.

There are some powerful trends to suggest that this second view is gaining traction. The first of these is the extraordinary explosion of new ways to observe social phenomena. Every day each of us leaves behind a data trail of who we talk to, what we eat and where we go. It’s easier than ever to survey people, to spot patterns, to scrape the web or to pick up data from sensors. It’s easier than ever to gather perceptions and emotions as well as material facts and easier than ever for organisations to practice social science – whether investment organisations analysing market patterns, human resources departments using behavioural science, or local authorities using ethnography.

That deluge of data is a big enough shift on its own. However, it is also now being used to feed interpretive and predictive tools using artificial intelligence to predict who is most likely to go to hospital, to end up in prison, which relationships are most likely to end in divorce.

Governments are developing their own predictive tools, and have also become much more interested in systematic experimentation, with Finland and Canada in the lead,  moving us closer to Karl Popper’s vision of “methods of trial and error, of inventing hypotheses which can be practically tested…”…

The second revolution is less visible but could be no less profound. This is the hunger of many people to be creators of knowledge, not just users; to be part of a truly collective intelligence. At the moment this shift towards mass engagement in knowledge is most visible in neighbouring fields.  Digital humanities mobilise many volunteers to input data and interpret texts – for example making ancient Arabic texts machine-readable. Even more striking is the growth of citizen science – eBird had 1.5 million reports last January; some 1.5 million people in the US monitor river streams and lakes, and SETI@home has 5 million volunteers. Thousands of patients also take part in funding and shaping research on their own conditions….

We’re all familiar with the old idea that it’s better to teach a man to fish than just to give him fish. In essence these trends ask us a simple question: why not apply the same logic to social science, and why not reorient social sciences to enhance the capacity of society itself to observe, analyse and interpret?…(More)”.

UN Opens New Office to Monitor AI Development and Predict Possible Threats

Curated on Posted on by Stefaan Verhulst

Interesting Engineering: “The United Nations has created a new office in the Netherlands dedicated to the monitoring and research of Artificial Intelligence (AI) technologies. The new office will collect information about the way in which AI is impacting the world. Researchers will have a particular focus on the way AI relates to global security but will also monitor the effects of job loss from AI and automation.

Irakli Beridze, a UN senior strategic adviser will head the office. They have described the new office saying, “A number of UN organisations operate projects involving robots and AI, such as the group of experts studying the role of autonomous military robots in the realm of conventional weapons. These are temporary measures. Ours is the first permanent UN office on this subject. We are looking at the risks as well as the advantages.”….He suggests that the speed of AI technology development is of primary concern. He explains, “This can make for instability if society does not adapt quickly enough. One of our most important tasks is to set up a network of experts from business, knowledge institutes, civil society organisations and governments. We certainly do not want to plead for a ban or a brake on technologies. We will also explore how new technology can contribute to the sustainable development goals of the UN. For this, we want to start concrete projects. We will not be a talking club.”…(More).

Are robots taking our jobs?

Curated on Posted on by Stefaan Verhulst

Hasan Bakhshi et al at Nesta: “In recent years, there has been an explosion of research into the impacts of automation on work. This makes sense: artificial intelligence and robotics are encroaching on areas of human activity that were simply unimaginable a few years ago.

We ourselves have made contributions to this debate (here, here and here). In The Future of Skills, however, we argue that public dialogues that consider automation alone are dangerous and misleading.

They are dangerous, because popular narratives matter for economic outcomes, and a narrative of relentless technological displacement of labour markets risks chilling innovation and growth, at a time when productivity growth is flagging in developed countries.

They are misleading because there are opportunities for boosting growth – if our education and training systems are agile enough to respond appropriately. However, while there is a burgeoning field of research on the automatability of occupations, there is far less that focuses on skills, and even less that generates actionable insights for stakeholders in areas like job redesign and learning priorities.

There is also a need to recognise that parallel to automation is a set of broader technological, demographic, economic and environmental trends which will have profound implications for employment. In some cases, the trends will reinforce one another; in others, they will produce second-order effects which may be missed when viewed in isolation…..

Skills investment must be at the centre of any long-term strategy for adjusting to structural change. A precondition is access to good quality, transparent analysis of future skills needs, as without it, labour market participants and policymakers risk flying blind. The approach we’ve developed is a step towards improving our understanding of this vital agenda and one that invites a more pro-active reaction than the defensive one that has characterised public discussions on automation in recent years. We’d love to hear your comments….(More).”

Using big data to predict suicide risk among Canadian youth

Curated on Posted on by Stefaan Verhulst

SAS Insights “Suicide is the second leading cause of death among youth in Canada, according to Statistics Canada, accounting for one-fifth of deaths of people under the age of 25 in 2011. The Canadian Mental Health Association states that among 15 – 24 year olds the number is an even more frightening at 24 percent – the third highest in the industrialized world. Yet despite these disturbing statistics, the signals that an individual plans on self-injury or suicide are hard to isolate….

Team members …collected 2.3 million tweets and used text mining software to identify 1.1 million of them as likely to have been authored by 13 to 17 year olds in Canada by building a machine learning model to predict age, based on the open source PAN author profiling dataset. Their analysis made use of natural language processing, predictive modelling, text mining, and data visualization….

However, there were challenges. Ages are not revealed on Twitter, so the team had to figure out how to tease out the data for 13 – 17 year olds in Canada. “We had a text data set, and we created a model to identify if people were in that age group based on how they talked in their tweets,” Soehl said. “From there, we picked some specific buzzwords and created topics around them, and our software mined those tweets to collect the people.”

Another issue was the restrictions Twitter places on pulling data, though Soehl believes that once this analysis becomes an established solution, Twitter may work with researchers to expedite the process. “Now that we’ve shown it’s possible, there are a lot of places we can go with it,” said Soehl. “Once you know your path and figure out what’s going to be valuable, things come together quickly.”

The team looked at the percentage of people in the group who were talking about depression or suicide, and what they were talking about. Horne said that when SAS’ work went in front of a Canadian audience working in health care, they said that it definitely filled a gap in their data — and that was the validation he’d been looking for. The team also won $10,000 for creating the best answer to this question (the team donated the award money to two mental health charities: Mind Your Mind and Rise Asset Development)

What’s next?

That doesn’t mean the work is done, said Jos Polfliet. “We’re just scraping the surface of what can be done with the information.” Another way to use the results is to look at patterns and trends….(More)”

Advancing Urban Health and Wellbeing Through Collective and Artificial Intelligence: A Systems Approach 3.0

Curated on Posted on by Stefaan Verhulst

Policy brief by Franz Gatzweiler: “Many problems of urban health and wellbeing, such as pollution, obesity, ageing, mental health, cardiovascular diseases, infectious diseases, inequality and poverty (WHO 2016), are highly complex and beyond the reach of individual problem solving capabilities. Biodiversity loss, climate change, and urban health problems emerge at aggregate scales and are unpredictable. They are the consequence of complex interactions between many individual agents and their environments across urban sectors and scales. Another challenge of complex urban health problems is the knowledge approach we apply to understand and solve them. We are challenged to create a new, innovative knowledge approach to understand and solve the problems of urban health. The positivist approach of separating cause from effect, or observer from observed, is insufficient when human agents are both part of the problemand the solution.

Problems emerging from complexity can only be solved collectively by applying rules which govern complexity. For example, the law of requisite variety (Ashby 1960) tells us that we need as much variety in our problemsolving toolbox as there are different types of problemsto be solved, and we need to address these problems at the respective scale. No individual, hasthe intelligence to solve emergent problems of urban health alone….

  • Complex problems of urban health and wellbeing cause millions of premature deaths annually and are beyond the reach of individual problem-solving capabilities.
  • Collective and artificial intelligence (CI+AI) working together can address the complex challenges of urban health
  • The systems approach (SA) is an adaptive, intelligent and intelligence-creating, “data-metabolic” mechanism for solving such complex challenges
  • Design principles have been identified to successfully create CI and AI. Data metabolic costs are the limiting factor.
  • A call for collaborative action to build an “urban brain” by means of next generation systems approaches is required to save lives in the face of failure to tackle complex urban health challenges….(More)”.

Free Speech in the Algorithmic Society: Big Data, Private Governance, and New School Speech Regulation

Curated on Posted on by Stefaan Verhulst

Paper by Jack Balkin: “We have now moved from the early days of the Internet to the Algorithmic Society. The Algorithmic Society features the use of algorithms, artificial intelligence agents, and Big Data to govern populations. It also features digital infrastructure companies, large multi-national social media platforms, and search engines that sit between traditional nation states and ordinary individuals, and serve as special-purpose governors of speech.

The Algorithmic Society presents two central problems for freedom of expression. First, Big Data allows new forms of manipulation and control, which private companies will attempt to legitimate and insulate from regulation by invoking free speech principles. Here First Amendment arguments will likely be employed to forestall digital privacy guarantees and prevent consumer protection regulation. Second, privately owned digital infrastructure companies and online platforms govern speech much as nation states once did. Here the First Amendment, as normally construed, is simply inadequate to protect the practical ability to speak.

The first part of the essay describes how to regulate online businesses that employ Big Data and algorithmic decision making consistent with free speech principles. Some of these businesses are “information fiduciaries” toward their end-users; they must exercise duties of good faith and non-manipulation. Other businesses who are not information fiduciaries have a duty not to engage in “algorithmic nuisance”: they may not externalize the costs of their analysis and use of Big Data onto innocent third parties.

The second part of the essay turns to the emerging pluralist model of online speech regulation. This pluralist model contrasts with the traditional dyadic model in which nation states regulated the speech of their citizens.

In the pluralist model, territorial governments continue to regulate the speech directly. But they also attempt to coerce or co-opt owners of digital infrastructure to regulate the speech of others. This is “new school” speech regulation….(More)”.

These 16 companies want to make technology work for everyone

Curated on Posted on by Stefaan Verhulst

MIT Sloan School Press Release: “One company helps undocumented people create a digital identity. Another uses artificial intelligence to help students transition to college. Yet another provides free training to budding tech pros.

These organizations are just a few of the many that are using technology to solve problems and help people all over the world — and they are all finalists in the MIT Initiative on the Digital Economy’s second annual Inclusive Innovation Challenge. During a time of great technological innovation, many people are not benefiting from this progress. The challenge is recognizing companies that are using technology to improve opportunities for working people…..

Here are the finalists:

AdmitHub
Did you know that of the students who have been admitted to college each spring, 14 percent don’t actually attend come fall? Or that of those who do attend, 48 percent haven’t graduated six years later. Boston-based AdmitHub created a virtual assistant powered by artificial intelligence to help students navigate the financial, academic, and social situations that accompany going to college, and they do it all through text messaging, communicating with students on their terms and easing the transition to college.

African Renewable Energy Distributor Ltd.
This company has developed solar-powered, portable kiosks where people can charge their phones, access Wi-Fi, or access an intranet while offline. Using a micro franchise business model, the Rwanda-based company hopes to empower women and people with disabilities who can run the kiosks.

AID:Tech
More than two billion people worldwide have no legal identity, something that is necessary for accessing public and financial services. Aid:Tech aims to end that, by providing a platform for undocumented people to create a digital ID using blockchain so that every transaction is secure and traceable. Aid:Tech is based out of Dublin, with offices in New York and London….(More)”

Plato and the Nerd. The Creative Partnership of Humans and Technology

Curated on Posted on by Stefaan Verhulst

MITPress: “In this book, Edward Ashford Lee makes a bold claim: that the creators of digital technology have an unsurpassed medium for creativity. Technology has advanced to the point where progress seems limited not by physical constraints but the human imagination. Writing for both literate technologists and numerate humanists, Lee makes a case for engineering—creating technology—as a deeply intellectual and fundamentally creative process. Explaining why digital technology has been so transformative and so liberating, Lee argues that the real power of technology stems from its partnership with humans.

Lee explores the ways that engineers use models and abstraction to build inventive artificial worlds and to give us things that we never dreamed of—for example, the ability to carry in our pockets everything humans have ever published. But he also attempts to counter the runaway enthusiasm of some technology boosters who claim everything in the physical world is a computation—that even such complex phenomena as human cognition are software operating on digital data. Lee argues that the evidence for this is weak, and the likelihood that nature has limited itself to processes that conform to today’s notion of digital computation is remote.

Lee goes on to argue that artificial intelligence’s goal of reproducing human cognitive functions in computers vastly underestimates the potential of computers. In his view, technology is coevolving with humans. It augments our cognitive and physical capabilities while we nurture, develop, and propagate the technology itself. Complementarity is more likely than competition….(More)”.