The role of corporations in addressing AI’s ethical dilemmas

Curated on Posted on by Stefaan Verhulst

Darrell M. West at Brookings: “In this paper, I examine five AI ethical dilemmas: weapons and military-related applications, law and border enforcement, government surveillance, issues of racial bias, and social credit systems. I discuss how technology companies are handling these issues and the importance of having principles and processes for addressing these concerns. I close by noting ways to strengthen ethics in AI-related corporate decisions.

Briefly, I argue it is important for firms to undertake several steps in order to ensure that AI ethics are taken seriously:

  1. Hire ethicists who work with corporate decisionmakers and software developers
  2. Develop a code of AI ethics that lays out how various issues will be handled
  3. Have an AI review board that regularly addresses corporate ethical questions
  4. Develop AI audit trails that show how various coding decisions have been made
  5. Implement AI training programs so staff operationalizes ethical considerations in their daily work, and
  6. Provide a means for remediation when AI solutions inflict harm or damages on people or organizations….(More)”.

We hold people with power to account. Why not algorithms?

Curated on Posted on by Stefaan Verhulst

Hannah Fry at the Guardian: “…But already in our hospitals, our schools, our shops, our courtrooms and our police stations, artificial intelligence is silently working behind the scenes, feeding on our data and making decisions on our behalf. Sure, this technology has the capacity for enormous social good – it can help us diagnose breast cancer, catch serial killers, avoid plane crashes and, as the health secretary, Matt Hancock, has proposed, potentially save lives using NHS data and genomics. Unless we know when to trust our own instincts over the output of a piece of software, however, it also brings the potential for disruption, injustice and unfairness.

If we permit flawed machines to make life-changing decisions on our behalf – by allowing them to pinpoint a murder suspect, to diagnose a condition or take over the wheel of a car – we have to think carefully about what happens when things go wrong…

I think it’s time we started treating machines as we would any other source of power. I would like to propose a system of regulation for algorithms, and perhaps a good place to start would be with Tony Benn’s five simple questions, designed for powerful people, but equally applicable to modern AI:

“What power have you got?
“Where did you get it from?
“In whose interests do you use it?
“To whom are you accountable?
“How do we get rid of you?”
Because, ultimately, we can’t just think of algorithms in isolation. We have to think of the failings of the people who design them – and the danger to those they are supposedly designed to serve.

Swarm AI Outperforms in Stanford Medical Study

Curated on Posted on by Stefaan Verhulst

Press Release: “Stanford University School of Medicine and Unanimous AI presented a new study today showing that a small group of doctors, connected by intelligence algorithms that enable them to work together as a “hive mind,” could achieve higher diagnostic accuracy than the individual doctors or machine learning algorithms alone.  The technology used is called Swarm AI and it empowers networked human groups to combine their individual insights in real-time, using AI algorithms to converge on optimal solutions.

As presented at the 2018 SIIM Conference on Machine Intelligence in Medical Imaging, the study tasked a group of experienced radiologists with diagnosing the presence of pneumonia in chest X-rays. This is one of the most widely performed imaging procedures in the US, with more than 1 million adults hospitalized with pneumonia each year. But, despite this prevalence, accurately diagnosing X-rays is highly challenging with significant variability across radiologists. This makes it both an optimal task for applying new AI technologies, and an important problem to solve for the medical community.

When generating diagnoses using Swarm AI technology, the average error rate was reduced by 33% compared to traditional diagnoses by individual practitioners.  This is an exciting result, showing the potential of AI technologies to amplify the accuracy of human practitioners while maintaining their direct participation in the diagnostic process.

Swarm AI technology was also compared to the state-of-the-art in automated diagnosis using software algorithms that do not employ human practitioners.  Currently, the best system in the world for the automated diagnosing of pneumonia from chest X-rays is the CheXNet system from Stanford University, which made headlines in 2017 by significantly outperforming individual practitioners using deep-learning derived algorithms.

The Swarm AI system, which combines real-time human insights with AI technology, was 22% more accurate in binary classification than the software-only CheXNet system.  In other words, by connecting a group of radiologists into a medical “hive mind”, the hybrid human-machine system was able to outperform individual human doctors as well as the state-of-the-art in deep-learning derived algorithms….(More)”.

Don’t forget people in the use of big data for development

Curated on Posted on by Stefaan Verhulst

Joshua Blumenstock at Nature: “Today, 95% of the global population has mobile-phone coverage, and the number of people who own a phone is rising fast (see ‘Dialling up’)1. Phones generate troves of personal data on billions of people, including those who live on a few dollars a day. So aid organizations, researchers and private companies are looking at ways in which this ‘data revolution’ could transform international development.

Some businesses are starting to make their data and tools available to those trying to solve humanitarian problems. The Earth-imaging company Planet in San Francisco, California, for example, makes its high-resolution satellite pictures freely available after natural disasters so that researchers and aid organizations can coordinate relief efforts. Meanwhile, organizations such as the World Bank and the United Nations are recruiting teams of data scientists to apply their skills in statistics and machine learning to challenges in international development.

But in the rush to find technological solutions to complex global problems there’s a danger of researchers and others being distracted by the technology and losing track of the key hardships and constraints that are unique to each local context. Designing data-enabled applications that work in the real world will require a slower approach that pays much more attention to the people behind the numbers…(More)”.

Safe artificial intelligence requires cultural intelligence

Curated on Posted on by Stefaan Verhulst

Gillian Hadfield at TechCrunch: “Knowledge, to paraphrase British journalist Miles Kington, is knowing a tomato is a fruit; wisdom is knowing there’s a norm against putting it in a fruit salad.

Any kind of artificial intelligence clearly needs to possess great knowledge. But if we are going to deploy AI agents widely in society at large — on our highways, in our nursing homes and schools, in our businesses and governments — we will need machines to be wise as well as smart.

Researchers who focus on a problem known as AI safety or AI alignment define artificial intelligence as machines that can meet or beat human performance at a specific cognitive task. Today’s self-driving cars and facial recognition algorithms fall into this narrow type of AI.

But some researchers are working to develop artificial general intelligence (AGI) — machines that can outperform humans at any cognitive task. We don’t know yet when or even if AGI will be achieved, but it’s clear that the research path is leading to ever more powerful and autonomous AI systems performing more and more tasks in our economies and societies.

Building machines that can perform any cognitive task means figuring out how to build AI that can not only learn about things like the biology of tomatoes but also about our highly variable and changing systems of norms about things like what we do with tomatoes.

Humans live lives populated by a multitude of norms, from how we eat, dress and speak to how we share information, treat one another and pursue our goals.

For AI to be truly powerful will require machines to comprehend that norms can vary tremendously from group to group, making them seem unnecessary, yet it can be critical to follow them in a given community.

Tomatoes in fruit salads may seem odd to the Brits for whom Kington was writing, but they are perfectly fine if you are cooking for Koreans or a member of the culinary avant-garde.  And while it may seem minor, serving them the wrong way to a particular guest can cause confusion, disgust, even anger. That’s not a recipe for healthy future relationships….(More)”.

Constitutional Democracy and Technology in the age of Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Paul Nemitz at Royal Society Philosophical Transactions: “Given the foreseeable pervasiveness of Artificial Intelligence in modern societies, it is legitimate and necessary to ask the question how this new technology must be shaped to support the maintenance and strengthening of constitutional democracy.

This paper first describes the four core elements of today’s digital power concentration, which need to be seen in cumulation and which, seen together, are both a threat to democracy and to functioning markets. It then recalls the experience with the lawless internet and the relationship between technology and the law as it has developed in the internet economy and the experience with GDPR before it moves on to the key question for AI in democracy, namely which of the challenges of AI can be safely and with good conscience left to ethics, and which challenges of AI need to be addressed by rules which are enforceable and encompass the legitimacy of democratic process, thus laws.

The paper closes with a call for a new culture of incorporating the principles of Democracy, Rule of law and Human Rights by design in AI and a three level technological impact assessment for new technologies like AI as a practical way forward for this purpose….(More).

Google launches new search engine to help scientists find the datasets they need

Curated on Posted on by Stefaan Verhulst

James Vincent at The Verge: “The service, called Dataset Search, launches today, and it will be a companion of sorts to Google Scholar, the company’s popular search engine for academic studies and reports. Institutions that publish their data online, like universities and governments, will need to include metadata tags in their webpages that describe their data, including who created it, when it was published, how it was collected, and so on. This information will then be indexed by Google’s search engine and combined with information from the Knowledge Graph. (So if dataset X was published by CERN, a little information about the institute will also be included in the search.)

Speaking to The Verge, Natasha Noy, a research scientist at Google AI who helped created Dataset Search, says the aim is to unify the tens of thousands of different repositories for datasets online. “We want to make that data discoverable, but keep it where it is,” says Noy.

At the moment, dataset publication is extremely fragmented. Different scientific domains have their own preferred repositories, as do different governments and local authorities. “Scientists say, ‘I know where I need to go to find my datasets, but that’s not what I always want,’” says Noy. “Once they step out of their unique community, that’s when it gets hard.”

Noy gives the example of a climate scientist she spoke to recently who told her she’d been looking for a specific dataset on ocean temperatures for an upcoming study but couldn’t find it anywhere. She didn’t track it down until she ran into a colleague at a conference who recognized the dataset and told her where it was hosted. Only then could she continue with her work. “And this wasn’t even a particularly boutique depository,” says Noy. “The dataset was well written up in a fairly prominent place, but it was still difficult to find.”

An example search for weather records in Google Dataset Search.
 Image: Google

The initial release of Dataset Search will cover the environmental and social sciences, government data, and datasets from news organizations like ProPublica. However, if the service becomes popular, the amount of data it indexes should quickly snowball as institutions and scientists scramble to make their information accessible….(More)”.

Reflecting the Past, Shaping the Future: Making AI Work for International Development

Curated on Posted on by Stefaan Verhulst

USAID Report: “We are in the midst of an unprecedented surge of interest in machine learning (ML) and artificial intelligence (AI) technologies. These tools, which allow computers to make data-derived predictions and automate decisions, have become part of daily life for billions of people. Ubiquitous digital services such as interactive maps, tailored advertisements, and voice-activated personal assistants are likely only the beginning. Some AI advocates even claim that AI’s impact will be as profound as “electricity or fire” that it will revolutionize nearly every field of human activity. This enthusiasm has reached international development as well. Emerging ML/AI applications promise to reshape healthcare, agriculture, and democracy in the developing world. ML and AI show tremendous potential for helping to achieve sustainable development objectives globally. They can improve efficiency by automating labor-intensive tasks, or offer new insights by finding patterns in large, complex datasets. A recent report suggests that AI advances could double economic growth rates and increase labor productivity 40% by 2035. At the same time, the very nature of these tools — their ability to codify and reproduce patterns they detect — introduces significant concerns alongside promise.

In developed countries, ML tools have sometimes been found to automate racial profiling, to foster surveillance, and to perpetuate racial stereotypes. Algorithms may be used, either intentionally or unintentionally, in ways that result in disparate or unfair outcomes between minority and majority populations. Complex models can make it difficult to establish accountability or seek redress when models make mistakes. These shortcomings are not restricted to developed countries. They can manifest in any setting, especially in places with histories of ethnic conflict or inequality. As the development community adopts tools enabled by ML and AI, we need a cleareyed understanding of how to ensure their application is effective, inclusive, and fair. This requires knowing when ML and AI offer a suitable solution to the challenge at hand. It also requires appreciating that these technologies can do harm — and committing to addressing and mitigating these harms.

ML and AI applications may sometimes seem like science fiction, and the technical intricacies of ML and AI can be off-putting for those who haven’t been formally trained in the field. However, there is a critical role for development actors to play as we begin to lean on these tools more and more in our work. Even without technical training in ML, development professionals have the ability — and the responsibility — to meaningfully influence how these technologies impact people.

You don’t need to be an ML or AI expert to shape the development and use of these tools. All of us can learn to ask the hard questions that will keep solutions working for, and not against, the development challenges we care about. Development practitioners already have deep expertise in their respective sectors or regions. They bring necessary experience in engaging local stakeholders, working with complex social systems, and identifying structural inequities that undermine inclusive progress. Unless this expert perspective informs the construction and adoption of ML/AI technologies, ML and AI will fail to reach their transformative potential in development.

This document aims to inform and empower those who may have limited technical experience as they navigate an emerging ML/AI landscape in developing countries. Donors, implementers, and other development partners should expect to come away with a basic grasp of common ML techniques and the problems ML is uniquely well-suited to solve. We will also explore some of the ways in which ML/AI may fail or be ill-suited for deployment in developing-country contexts. Awareness of these risks, and acknowledgement of our role in perpetuating or minimizing them, will help us work together to protect against harmful outcomes and ensure that AI and ML are contributing to a fair, equitable, and empowering future…(More)”.

AI and Big Data: A Blueprint for a Human Rights, Social and Ethical Impact Assessment

Curated on Posted on by Stefaan Verhulst

Alessandro Mantelero in Computer Law & Security Review: “The use of algorithms in modern data processing techniques, as well as data-intensive technological trends, suggests the adoption of a broader view of the data protection impact assessment. This will force data controllers to go beyond the traditional focus on data quality and security, and consider the impact of data processing on fundamental rights and collective social and ethical values.

Building on studies of the collective dimension of data protection, this article sets out to embed this new perspective in an assessment model centred on human rights (Human Rights, Ethical and Social Impact Assessment-HRESIA). This self-assessment model intends to overcome the limitations of the existing assessment models, which are either too closely focused on data processing or have an extent and granularity that make them too complicated to evaluate the consequences of a given use of data. In terms of architecture, the HRESIA has two main elements: a self-assessment questionnaire and an ad hoc expert committee. As a blueprint, this contribution focuses mainly on the nature of the proposed model, its architecture and its challenges; a more detailed description of the model and the content of the questionnaire will be discussed in a future publication drawing on the ongoing research….(More)”.

Origin Privacy: Protecting Privacy in the Big-Data Era

Curated on Posted on by Stefaan Verhulst

Paper by Helen Nissenbaum, Sebastian Benthall, Anupam Datta, Michael Carl Tschantz, and Piot Mardziel: “Machine learning over big data poses challenges for our conceptualization of privacy. Such techniques can discover surprising and counteractive associations that take innocent looking data and turns it into important inferences about a person. For example, the buying carbon monoxide monitors has been linked to paying credit card bills, while buying chrome-skull car accessories predicts not doing so. Also, Target may have used the buying of scent-free hand lotion and vitamins as a sign that the buyer is pregnant. If we take pregnancy status to be private and assume that we should prohibit the sharing information that can reveal that fact, then we have created an unworkable notion of privacy, one in which sharing any scrap of data may violate privacy.

Prior technical specifications of privacy depend on the classification of certain types of information as private or sensitive; privacy policies in these frameworks limit access to data that allow inference of this sensitive information. As the above examples show, today’s data rich world creates a new kind of problem: it is difficult if not impossible to guarantee that information does notallow inference of sensitive topics. This makes information flow rules based on information topic unstable.

We address the problem of providing a workable definition of private data that takes into account emerging threats to privacy from large-scale data collection systems. We build on Contextual Integrity and its claim that privacy is appropriate information flow, or flow according to socially or legally specified rules.

As in other adaptations of Contextual Integrity (CI) to computer science, the parameterization of social norms in CI is translated into a logical specification. In this work, we depart from CI by considering rules that restrict information flow based on its origin and provenance, instead of on it’s type, topic, or subject.

We call this concept of privacy as adherence to origin-based rules Origin Privacy. Origin Privacy rules can be found in some existing data protection laws. This motivates the computational implementation of origin-based rules for the simple purpose of compliance engineering. We also formally model origin privacy to determine what security properties it guarantees relative to the concerns that motivate it….(More)”.