From policing to news, how algorithms are changing our lives

Curated on Posted on by Stefaan Verhulst

Carl Miller at The National: “First, write out the numbers one to 100 in 10 rows. Cross out the one. Then circle the two, and cross out all of the multiples of two. Circle the three, and do likewise. Follow those instructions, and you’ve just completed the first three steps of an algorithm, and an incredibly ancient one. Twenty-three centuries ago, Eratosthenes was sat in the great library of Alexandria, using this process (it is called Eratosthenes’ Sieve) to find and separate prime numbers. Algorithms are nothing new, indeed even the word itself is old. Fifteen centuries after Eratosthenes, Algoritmi de numero Indorum appeared on the bookshelves of European monks, and with it, the word to describe something very simple in essence: follow a series of fixed steps, in order, to achieve a given answer to a given problem. That’s it, that’s an algorithm. Simple.

 Apart from, of course, the story of algorithms is not so simple, nor so humble. In the shocked wake of Donald Trump’s victory in the United States presidential election, a culprit needed to be found to explain what had happened. What had, against the odds, and in the face of thousands of polls, caused this tectonic shift in US political opinion? Soon the finger was pointed. On social media, and especially on Facebook, it was alleged that pro-Trump stories, based on inaccurate information, had spread like wildfire, often eclipsing real news and honestly-checked facts.
But no human editor was thrust into the spotlight. What took centre stage was an algorithm; Facebook’s news algorithm. It was this, critics said, that was responsible for allowing the “fake news” to circulate. This algorithm wasn’t humbly finding prime numbers; it was responsible for the news that you saw (and of course didn’t see) on the largest source of news in the world. This algorithm had somehow risen to become more powerful than any newspaper editor in the world, powerful enough to possibly throw an election.
So why all the fuss? Something is now happening in society that is throwing algorithms into the spotlight. They have taken on a new significance, even an allure and mystique. Algorithms are simply tools but a web of new technologies are vastly increasing the power that these tools have over our lives. The startling leaps forward in artificial intelligence have meant that algorithms have learned how to learn, and to become capable of accomplishing tasks and tackling problems that they were never been able to achieve before. Their learning is fuelled with more data than ever before, collected, stored and connected with the constellations of sensors, data farms and services that have ushered in the age of big data.

Algorithms are also doing more things; whether welding, driving or cooking, thanks to robotics. Wherever there is some kind of exciting innovation happening, algorithms are rarely far away. They are being used in more fields, for more things, than ever before and are incomparably, incomprehensibly more capable than the algorithms recognisable to Eratosthenes….(More)”

How Should a Society Be?

Curated on Posted on by Stefaan Verhulst

An Edge Conversation With Brian Christian: “This is another example where AI—in this case, machine-learning methods—intersects with these ethical and civic questions in an ultimately promising and potentially productive way. As a society we have these values in maxim form, like equal opportunity, justice, fairness, and in many ways they’re deliberately vague. This deliberate flexibility and ambiguity are what allows things to be a living document that stays relevant. But here we are in this world where we have to say of some machine-learning model, is this racially fair? We have to define these terms, computationally or numerically.

It’s problematic in the short term because we have no idea what we’re doing; we don’t have a way to approach that problem yet. In the slightly longer term—five or ten years—there’s a profound opportunity to come together as a polis and get precise about what we mean by justice or fairness with respect to certain protected classes. Does that mean it’s got an equal false positive rate? Does that mean it has an equal false negative rate? What is the tradeoff that we’re willing to make? What are the constraints that we want to put on this model-building process? That’s a profound question, and we haven’t needed to address it until now. There’s going to be a civic conversation in the next few years about how to make these concepts explicit….(More) (Video)”

Big data promise exponential change in healthcare

Curated on Posted on by Stefaan Verhulst

Gonzalo Viña in the Financial Times (Special Report: Innovation in Healthcare): “When a top Formula One team is using pit stop data-gathering technology to help a drugmaker improve the way it makes ventilators for asthma sufferers, there can be few doubts that big data are transforming pharmaceutical and healthcare systems.

GlaxoSmithKline employs online technology and a data algorithm developed by F1’s elite McLaren Applied Technologies team to minimise the risk of leakage from its best-selling Ventolin (salbutamol) bronchodilator drug.

Using multiple sensors and hundreds of thousands of readings, the potential for leakage is coming down to “close to zero”, says Brian Neill, diagnostics director in GSK’s programme and risk management division.

This apparently unlikely venture for McLaren, known more as the team of such star drivers as Fernando Alonso and Jenson Button, extends beyond the work it does with GSK. It has partnered with Birmingham Children’s hospital in a £1.8m project utilising McLaren’s expertise in analysing data during a motor race to collect such information from patients as their heart and breathing rates and oxygen levels. Imperial College London, meanwhile, is making use of F1 sensor technology to detect neurological dysfunction….

Big data analysis is already helping to reshape sales and marketing within the pharmaceuticals business. Great potential, however, lies in its ability to fine tune research and clinical trials, as well as providing new measurement capabilities for doctors, insurers and regulators and even patients themselves. Its applications seem infinite….

The OECD last year said governments needed better data governance rules given the “high variability” among OECD countries about protecting patient privacy. Recently, DeepMind, the artificial intelligence company owned by Google, signed a deal with a UK NHS trust to process, via a mobile app, medical data relating to 1.6m patients. Privacy advocates say this as “worrying”. Julia Powles, a University of Cambridge technology law expert, asks if the company is being given “a free pass” on the back of “unproven promises of efficiency and innovation”.

Brian Hengesbaugh, partner at law firm Baker & McKenzie in Chicago, says the process of solving such problems remains “under-developed”… (More)“

Misinformation on social media: Can technology save us?

Curated on Posted on by Stefaan Verhulst

 at the Conversation: “…Since we cannot pay attention to all the posts in our feeds, algorithms determine what we see and what we don’t. The algorithms used by social media platforms today are designed to prioritize engaging posts – ones we’re likely to click on, react to and share. But a recent analysis found intentionally misleading pages got at least as much online sharing and reaction as real news.

This algorithmic bias toward engagement over truth reinforces our social and cognitive biases. As a result, when we follow links shared on social media, we tend to visit a smaller, more homogeneous set of sources than when we conduct a search and visit the top results.

Existing research shows that being in an echo chamber can make people more gullible about accepting unverified rumors. But we need to know a lot more about how different people respond to a single hoax: Some share it right away, others fact-check it first.

We are simulating a social network to study this competition between sharing and fact-checking. We are hoping to help untangle conflicting evidence about when fact-checking helps stop hoaxes from spreading and when it doesn’t. Our preliminary results suggest that the more segregated the community of hoax believers, the longer the hoax survives. Again, it’s not just about the hoax itself but also about the network.

Many people are trying to figure out what to do about all this. According to Mark Zuckerberg’s latest announcement, Facebook teams are testing potential options. And a group of college students has proposed a way to simply label shared links as “verified” or not.

Some solutions remain out of reach, at least for the moment. For example, we can’t yet teach artificial intelligence systems how to discern between truth and falsehood. But we can tell ranking algorithms to give higher priority to more reliable sources…..

We can make our fight against fake news more efficient if we better understand how bad information spreads. If, for example, bots are responsible for many of the falsehoods, we can focus attention on detecting them. If, alternatively, the problem is with echo chambers, perhaps we could design recommendation systems that don’t exclude differing views….(More)”

Talent Gap Is a Main Roadblock as Agencies Eye Emerging Tech

Curated on Posted on by Stefaan Verhulst

Theo Douglas in GovTech: “U.S. public service agencies are closely eyeing emerging technologies, chiefly advanced analytics and predictive modeling, according to a new report from Accenture, but like their counterparts globally they must address talent and complexity issues before adoption rates will rise.

The report, Emerging Technologies in Public Service, compiled a nine-nation survey of IT officials across all levels of government in policing and justice, health and social services, revenue, border services, pension/Social Security and administration, and was released earlier this week.

It revealed a deep interest in emerging tech from the public sector, finding 70 percent of agencies are evaluating their potential — but a much lower adoption level, with just 25 percent going beyond piloting to implementation….

The revenue and tax industries have been early adopters of advanced analytics and predictive modeling, he said, while biometrics and video analytics are resonating with police agencies.

In Australia, the tax office found using voiceprint technology could save 75,000 work hours annually.

Closer to home, Utah Chief Technology Officer Dave Fletcher told Accenture that consolidating data centers into a virtualized infrastructure improved speed and flexibility, so some processes that once took weeks or months can now happen in minutes or hours.

Nationally, 70 percent of agencies have either piloted or implemented an advanced analytics or predictive modeling program. Biometrics and identity analytics were the next most popular technologies, with 29 percent piloting or implementing, followed by machine learning at 22 percent.

Those numbers contrast globally with Australia, where 68 percent of government agencies have charged into piloting and implementing biometric and identity analytics programs; and Germany and Singapore, where 27 percent and 57 percent of agencies respectively have piloted or adopted video analytic programs.

Overall, 78 percent of respondents said they were either underway or had implemented some machine-learning technologies.

The benefits of embracing emerging tech that were identified ranged from finding better ways of working through automation to innovating and developing new services and reducing costs.

Agencies told Accenture their No. 1 objective was increasing customer satisfaction. But 89 percent said they’d expect a return on implementing intelligent technology within two years. Four-fifths, or 80 percent, agreed intelligent tech would improve employees’ job satisfaction….(More).

The ethical impact of data science

Curated on Posted on by Stefaan Verhulst

Theme issue of Phil. Trans. R. Soc. A compiled and edited by Mariarosaria Taddeo and Luciano Floridi: “This theme issue has the founding ambition of landscaping data ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (including artificial intelligence, artificial agents, machine learning and robots) and corresponding practices (including responsible innovation, programming, hacking and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data ethics builds on the foundation provided by computer and information ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the level of abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations—the interactions among hardware, software and data—rather than on the variety of digital technologies that enable them. And it emphasizes the complexity of the ethical challenges posed by data science. Because of such complexity, data ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of data science and its applications within a consistent, holistic and inclusive framework. Only as a macroethics will data ethics provide solutions that can maximize the value of data science for our societies, for all of us and for our environments….(More)”

Table of Contents:

  • The dynamics of big data and human rights: the case of scientific research; Effy Vayena, John Tasioulas
  • Facilitating the ethical use of health data for the benefit of society: electronic health records, consent and the duty of easy rescue; Sebastian Porsdam Mann, Julian Savulescu, Barbara J. Sahakian
  • Faultless responsibility: on the nature and allocation of moral responsibility for distributed moral actions; Luciano Floridi
  • Compelling truth: legal protection of the infosphere against big data spills; Burkhard Schafer
  • Locating ethics in data science: responsibility and accountability in global and distributed knowledge production systems; Sabina Leonelli
  • Privacy is an essentially contested concept: a multi-dimensional analytic for mapping privacy; Deirdre K. Mulligan, Colin Koopman, Nick Doty
  • Beyond privacy and exposure: ethical issues within citizen-facing analytics; Peter Grindrod
  • The ethics of smart cities and urban science; Rob Kitchin
  • The ethics of big data as a public good: which public? Whose good? Linnet Taylor
  • Data philanthropy and the design of the infraethics for information societies; Mariarosaria Taddeo
  • The opportunities and ethics of big data: practical priorities for a national Council of Data Ethics; Olivia Varley-Winter, Hetan Shah
  • Data science ethics in government; Cat Drew
  • The ethics of data and of data science: an economist’s perspective; Jonathan Cave
  • What’s the good of a science platform? John Gallacher

 

Teaching an Algorithm to Understand Right and Wrong

Curated on Posted on by Stefaan Verhulst

Greg Satell at Harvard Business Review: “In his Nicomachean Ethics, Aristotle states that it is a fact that “all knowledge and every pursuit aims at some good,” but then continues, “What then do we mean by the good?” That, in essence, encapsulates the ethical dilemma. We all agree that we should be good and just, but it’s much harder to decide what that entails.

Since Aristotle’s time, the questions he raised have been continually discussed and debated. From the works of great philosophers like Kant, Bentham, andRawls to modern-day cocktail parties and late-night dorm room bull sessions, the issues are endlessly mulled over and argued about but never come to a satisfying conclusion.

Today, as we enter a “cognitive era” of thinking machines, the problem of what should guide our actions is gaining newfound importance. If we find it so difficult to denote the principles by which a person should act justly and wisely, then how are we to encode them within the artificial intelligences we are creating? It is a question that we need to come up with answers for soon.

Designing a Learning Environment

Every parent worries about what influences their children are exposed to. What TV shows are they watching? What video games are they playing? Are they hanging out with the wrong crowd at school? We try not to overly shelter our kids because we want them to learn about the world, but we don’t want to expose them to too much before they have the maturity to process it.

In artificial intelligence, these influences are called a “machine learning corpus.”For example, if you want to teach an algorithm to recognize cats, you expose it to thousands of pictures of cats and things that are not cats. Eventually, it figures out how to tell the difference between, say, a cat and a dog. Much as with human beings, it is through learning from these experiences that algorithms become useful.

However, the process can go horribly awry, as in the case of Microsoft’s Tay, aTwitter bot that the company unleashed on the microblogging platform. In under a day, Tay went from being friendly and casual (“Humans are super cool”) to downright scary (“Hitler was right and I hate Jews”). It was profoundly disturbing.

Francesca Rossi, an AI researcher at IBM, points out that we often encode principles regarding influences into societal norms, such as what age a child needs to be to watch an R-rated movie or whether they should learn evolution in school. “We need to decide to what extent the legal principles that we use to regulate humans can be used for machines,” she told me.

However, in some cases algorithms can alert us to bias in our society that we might not have been aware of, such as when we Google “grandma” and see only white faces. “There is a great potential for machines to alert us to bias,” Rossi notes. “We need to not only train our algorithms but also be open to the possibility that they can teach us about ourselves.”…

Another issue that we will have to contend with is that we will have to decide not only what ethical principles to encode in artificial intelligences but also how they are coded. As noted above, for the most part, “Thou shalt not kill” is a strict principle. Other than a few rare cases, such as the Secret Service or a soldier, it’s more like a preference that is greatly affected by context….

As pervasive as artificial intelligence is set to become in the near future, the responsibility rests with society as a whole. Put simply, we need to take the standards by which artificial intelligences will operate just as seriously as those that govern how our political systems operate and how are children are educated.

It is a responsibility that we cannot shirk….(More)

Understanding the four types of AI, from reactive robots to self-aware beings

Curated on Posted on by Stefaan Verhulst

 at The Conversation: “…We need to overcome the boundaries that define the four different types of artificial intelligence, the barriers that separate machines from us – and us from them.

Type I AI: Reactive machines

The most basic types of AI systems are purely reactive, and have the ability neither to form memories nor to use past experiences to inform current decisions. Deep Blue, IBM’s chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine.

Deep Blue can identify the pieces on a chess board and know how each moves. It can make predictions about what moves might be next for it and its opponent. And it can choose the most optimal moves from among the possibilities.

But it doesn’t have any concept of the past, nor any memory of what has happened before. Apart from a rarely used chess-specific rule against repeating the same move three times, Deep Blue ignores everything before the present moment. All it does is look at the pieces on the chess board as it stands right now, and choose from possible next moves.

This type of intelligence involves the computer perceiving the world directly and acting on what it sees. It doesn’t rely on an internal concept of the world. In a seminal paper, AI researcher Rodney Brooks argued that we should only build machines like this. His main reason was that people are not very good at programming accurate simulated worlds for computers to use, what is called in AI scholarship a “representation” of the world….

Type II AI: Limited memory

This Type II class contains machines can look into the past. Self-driving cars do some of this already. For example, they observe other cars’ speed and direction. That can’t be done in a just one moment, but rather requires identifying specific objects and monitoring them over time.

These observations are added to the self-driving cars’ preprogrammed representations of the world, which also include lane markings, traffic lights and other important elements, like curves in the road. They’re included when the car decides when to change lanes, to avoid cutting off another driver or being hit by a nearby car.

But these simple pieces of information about the past are only transient. They aren’t saved as part of the car’s library of experience it can learn from, the way human drivers compile experience over years behind the wheel…;

Type III AI: Theory of mind

We might stop here, and call this point the important divide between the machines we have and the machines we will build in the future. However, it is better to be more specific to discuss the types of representations machines need to form, and what they need to be about.

Machines in the next, more advanced, class not only form representations about the world, but also about other agents or entities in the world. In psychology, this is called “theory of mind” – the understanding that people, creatures and objects in the world can have thoughts and emotions that affect their own behavior.

This is crucial to how we humans formed societies, because they allowed us to have social interactions. Without understanding each other’s motives and intentions, and without taking into account what somebody else knows either about me or the environment, working together is at best difficult, at worst impossible.

If AI systems are indeed ever to walk among us, they’ll have to be able to understand that each of us has thoughts and feelings and expectations for how we’ll be treated. And they’ll have to adjust their behavior accordingly.

Type IV AI: Self-awareness

The final step of AI development is to build systems that can form representations about themselves. Ultimately, we AI researchers will have to not only understand consciousness, but build machines that have it….

While we are probably far from creating machines that are self-aware, we should focus our efforts toward understanding memory, learning and the ability to base decisions on past experiences….(More)”

AI Ethics: The Future of Humanity 

Curated on Posted on by Stefaan Verhulst

Report by sparks & honey: “Through our interaction with machines, we develop emotional, human expectations of them. Alexa, for example, comes alive when we speak with it. AI is and will be a representation of its cultural context, the values and ethics we apply to one another as humans.

This machinery is eerily familiar as it mirrors us, and eventually becomes even smarter than us mere mortals. We’re programming its advantages based on how we see ourselves and the world around us, and we’re doing this at an incredible pace. This shift is pervading culture from our perceptions of beauty and aesthetics to how we interact with one another – and our AI.

Infused with technology, we’re asking: what does it mean to be human?

Our report examines:

• The evolution of our empathy from humans to animals and robots
• How we treat AI in its infancy like we do a child, allowing it space to grow
• The spectrum of our emotional comfort in a world embracing AI
• The cultural contexts fueling AI biases, such as gender stereotypes, that drive the direction of AI
• How we place an innate trust in machines, more than we do one another (Download for free)”

 

Power to the People: Addressing Big Data Challenges in Neuroscience by Creating a New Cadre of Citizen Neuroscientists

Curated on Posted on by Stefaan Verhulst
Jane Roskams and Zoran Popović in Neuron: “Global neuroscience projects are producing big data at an unprecedented rate that informatic and artificial intelligence (AI) analytics simply cannot handle. Online games, like Foldit, Eterna, and Eyewire—and now a new neuroscience game, Mozak—are fueling a people-powered research science (PPRS) revolution, creating a global community of “new experts” that over time synergize with computational efforts to accelerate scientific progress, empowering us to use our collective cerebral talents to drive our understanding of our brain….(More)”