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Governance Principles for the New Generation Artificial Intelligence--Developing Responsible Artificial Intelligence

Governance Principles for the New Generation Artificial Intelligence--Developing Responsible Artificial Intelligence National Governance Committee for the New Generation Artificial Intelligence The global development of Artificial Intelligence (AI) has reached a new stage, with features such as cross-disciplinary integration, human-machine coordination, open and collective intelligence, and etc., which are profoundly changing our daily lives and the future of humanity.

In order to promote the healthy development of the new generation of AI, better balance between development and governance, ensure the safety, reliability and controllability of AI, support the economic, social, and environmental pillars of the UN sustainable development goals, and to jointly build a human community with a shared future, all stakeholders concerned with AI development should observe the following principles: 1.

Through technology advancement and management improvement, prejudices and discriminations should be eliminated as much as possible in the process of data acquisition, algorithm design, technology development, and product development and application.

Shared Responsibility AI developers, users and other related stakeholders should have a high sense of social responsibility and self-discipline, and should strictly abide by laws, regulations, ethical principles, technical standards and social norms.

With full respect for the principles and practices of AI development in various countries, international dialogues and cooperation should be encouraged to promote the formation of an international AI governance framework with broad consensus.

Artificial intelligence reinforces power and privilege

What do a Yemeni refugee in the queue for food aid, a checkout worker in a British supermarket and a depressed university student have in common?

Advanced nations and the world's biggest companies have thrown billions of dollars behind AI - a set of computing practices, including machine learning that collate masses of our data, analyse it, and use it to predict what we would do.

In our interview, he recalled years of debate with Minsky about whether AI was real or a myth: 'At one point, [Minsky] said to me, 'Look, whatever you think about this, just play along, because it gets us funding, this'll be great.' And it's true, you know ...

And if you went into the funders and you said, 'We're going to make these machines smarter than people some day and whoever isn't on that ride is going to get left behind and big time.

During President Barack Obama's drone wars, suspicion didn't even need to be personal - in a 'signature strike', it could be a nameless profile, generated by an algorithm, analysing where you went and who you talked to on your mobile phone.

Now a similar logic pervades the modern marketplace, the sense that total certainty and zero risk - that is, zero risk for the class of people Lanier describes as 'closest to the biggest computer' - is achievable and desirable.

Credit agencies and insurers want to build a better profile to understand whether you might get heart disease, or drop out of work, or fall behind on payments.

This originally meant that the skills and advantages of connected citizens in rich nations would massively outrun poorer citizens without computers and the Internet.

This drove policies like One Laptop Per Child - and it drives newer ones, like Digital ID, the aim to give everyone on Earth a unique identity, in the name of economic participation.

We can do better than to split society into those who can afford privacy and personal human assessment - and everyone else, who gets number-crunched, tagged, and sorted.

Unless we head off what Shoshana Zuboff calls 'the substitution of computation for politics' - where decisions are taken outside of a democratic contest, in the grey zone of prediction, scoring, and automation - we risk losing control over our values.

Responsible Artificial Intelligence

Intelligent systems that act autonomously and learn independently are regarded as key technologies for the next wave of industrial innovation.

Artificial Intelligence and Street Art: Tram Talk 21.06.2019, 4-5 p.m. and 7-9 p.m.Tram route 4 For the ‘Researching City Walls’ project, artists Smy and Fritz Boogie talked with robotics expert Wolfram Burgard, professor of autonomous intelligent systems at the University of Freiburg, about robotics, autonomous vehicles and machine learning.

Wall Talk: Yukie Nagai / Sare 24.06.2019, 6.15 p.m.Kulturaggregat e.V., Hildastraße 5, 79102 Freiburg At the heart of the ‘Researching City Walls’ project are large-scale murals in the center of Freiburg: each of these murals was created by one street artist and one scientist exchanging ideas and working in tandem.

24.06.2019, 8.15 p.m.Kollegiengebäude I, Lecture Theater 1199, Platz der Alten Synagoge 3, 79098 Freiburg Watches that record health data, self-organizing work processes, self-driving cars, and robots that explore remote planets: these are all examples of a networked world of intelligent technical systems which has become commonplace in recent years.

In his lecture from the ‘Freiburg Horizons’ series at FRIAS, Klaus Mainzer, emeritus professor of philosophy and philosophy of science at the Technical University of Munich, looks at the question of whether artificial intelligence will replace humans – and argues that it has to prove itself as a servant of society.

Understanding artificial intelligence ethics and safety

To make sure the impact of your AI project is positive and does not unintentionally harm those affected by it, you and your team should make considerations of AI ethics and safety a high priority.

AI ethics is a set of values, principles, and techniques that employ widely accepted standards to guide moral conduct in the development and use of AI systems.

These harms rarely arise as a result of a deliberate choice - most AI developers do not want to build biased or discriminatory applications or applications which invade users’ privacy.

The main ways AI systems can cause involuntary harm are: The field of AI ethics mitigates these harms by providing project teams with the values, principles, and techniques needed to produce ethical, fair, and safe AI applications.

This involves building a culture of responsible innovation as well as a governance architecture to bring the values and principles of ethical, fair, and safe AI to life.

To make sure they are fully incorporated into your project you should establish a governance architecture consisting of a: You should understand the framework of ethical values which support, underwrite, and motivate the responsible design and use of AI.

The Alan Turing Institute calls these the ‘FAST Track Principles’: Carefully reviewing the FAST Track Principles helps you: If your AI system processes social or demographic data, you should design it to meet a minimum level of discriminatory non-harm.

You should make sure designers and users remain aware of: Designers and implementers of AI systems should be able to: To assess these criteria in depth, you should consult The Alan Turing Institute’s guidance on AI ethics and safety.

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