How can we set future ethical standards for ICT, Big Data, AI and robotics?

July 11, 2019

josepine-fernow-siennaDo you use Google Maps to navigate in a new city? Ask Siri, Alexa or OK Google to play your favourite song? To help you find something on Amazon? To read a text message from a friend while you are driving your car? Perhaps your car is fitted with a semi-autonomous adaptive cruise control system… If any software or machine is going to perform in any autonomous way, it needs to collect data. About you, where you are going, what songs you like, your shopping habits, who your friends are and what you talk about. This begs the question:  are we willing to give up part of our privacy and personal liberty to enjoy the benefits technology offers.

It is difficult to predict the consequences of developing and using new technology. Policymakers struggle to assess the ethical, legal and human rights impacts of using different kinds of IT systems. In research, in industry and our homes. Good policy should be helpful for everyone that holds a stake. We might want it to protect ethical values and human rights, make research and development possible, allow technology transfer from academia to industry, make sure both large and smaller companies can develop their business, and make sure that there is social acceptance for technological development.

The European Union is serious about developing policy on the basis of sound research, rigorous empirical data and wide stakeholder consultation. In recent years, the Horizon2020 programme has invested € 10 million in three projects looking at the ethics and human rights implications of emerging digital technologies: PANELFIT, SHERPA and SIENNA.

The first project, PANELFIT (which is short for Participatory Approaches to a New Ethical and Legal Framework for ICT), will develop guidelines on the ethical and legal issues of ICT research and innovation. The second, SHERPA (stands for Shaping the ethical dimensions of Smart Information Systems (SIS) – A European Perspective), will develop tools to identify and address the ethical dimensions of smart information systems (SIS), which is the combination of artificial intelligence (AI) and big data analytics. SIENNA (short for Stakeholder-informed ethics for new technologies with high socio-economic and human rights impact), will develop research ethics protocols, professional ethical codes, and better ethical and legal frameworks for AI and robotics, human enhancement technologies, and human genomics.

SSP-graphic

All three projects involve experts, publics and stakeholders to co-create outputs, in different ways. They also support the European Union’s vision of Responsible Research and Innovation (RRI). SIENNA, SHERPA and PANELFIT recently published an editorial in the Orbit Journal, inviting stakeholders and publics to engage with the projects and contribute to the work.

Want to read more? Rowena Rodrigues and Anaïs Resseguier have written about some of the issues raised by the use of artificial intelligence on Ethics Dialogues (The underdog in the AI and ethical debate: human autonomy), and you can find out more about the SIENNA project in a previous post on the Ethics Blog (Ethics, human rights and responsible innovation).

Want to know more about the collaboration between SIENNA, SHERPA and PANELFIT? Read the editorial in Orbit (Setting future ethical standards for ICT, Big Data, AI and robotics: The contribution of three European Projects), or watch a video from our joint webinar on May 20, 2019 on YouTube (SIENNA, SHERPA, PANELFIT: Setting future ethical standards for ICT, Big Data, SIS, AI & Robotics).

Want to know how SIENNA views the ethical impacts of AI and robotics? Download infographic (pdf) and read our state-of-the-art review for AI & robotics (deliverable report).

AI-robotics-ifographic

Josepine Fernow

This post in Swedish

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Can a robot learn to speak?

May 29, 2018

Pär SegerdahlThere are self-modifying computer programs that “learn” from success and failure. Chess-playing computers, for example, become better through repeated games against humans.

Could a similar robot also learn to speak? If the robot gets the same input as a child gets when it learns to speak, should it not be possible in principle?

Notice how the question zigzags between child and machine. We say that the robot learns. We say that the child gets input. We speak of the robot as if it were a child. We speak of the child as if it were a robot. Finally, we take this linguistic zigzagging seriously as a fascinating question, perhaps even a great research task.

An AI expert and prospective father who dreamed of this great research task took the following ambitious measures. He equipped his whole house with cameras and microphones, to document all parent-child interactions during the child’s first years. Why? He wanted to know exactly what kind of linguistic input a child gets when it learns to speak. At a later stage, he might be able to give a self-modifying robot the same input and test if it also learns to speak.

How did the project turn out? The personal experience of raising the child led the AI ​​expert to question the whole project of teaching a robot to speak. How could a personal experience lead to the questioning of a seemingly serious scientific project?

Here, I could start babbling about how amiably social children are compared to cold machines. How they learn in close relationships with their parents. How they curiously and joyfully take the initiative, rather than calculatingly await input.

The problem is that such babbling on my part would make it seem as if the AI ​​expert simply was wrong about robots and children. That he did not know the facts, but now is more well-informed. It is not that simple. For the idea behind ​​the project presupposed unnoticed linguistic zigzagging. Already in asking the question, the boundaries between robots and children are blurred. Already in the question, we have half answered it!

We cannot be content with responding to the question in the headline with a simple, “No, it cannot.” We must reject the question as nonsense. Deceitful zigzagging creates the illusion that we are dealing with a serious question, worthy of scientific study.

This does not exclude, however, that computational linguistics increasingly uses self-modifying programs, and with great success. But that is another question.

Pär Segerdahl

Beard, Alex. How babies learn – and why robots can’t compete. The Guardian, 3 April 2018

This post in Swedish

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