Principles Of Artificial Intelligence Pdf – Wednesday, May 22, 2019, 12:26 PM OECD. OECD Principles on Artificial Intelligence – May 2019 – eng (pdf) Selected
The OECD and partner countries today formally adopted the first set of intergovernmental policy guidelines on artificial intelligence (AI), agreeing to adhere to international standards aimed at ensuring that AI systems are designed to be robust, safe, fair and reliable.
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The 36 OECD member countries, along with Argentina, Brazil, Colombia, Costa Rica, Peru and Romania, signed the OECD Principles on Artificial Intelligence at the organization’s annual Council of Ministers meeting, which will take place today and tomorrow in Paris and this year is focused on “Using the digital transition for sustainable for development”. Led by an expert panel of more than 50 members from governments, academia, business, civil society, international organizations, the technology community and trade unions, the Principles include five values-based principles for the responsible deployment of trusted AI and five recommendations. for public policy and international cooperation. They aim to guide governments, organizations and individuals in designing and managing artificial intelligence systems in ways that put the best interests of people first and ensure that designers and operators are held accountable for their proper performance.
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“Artificial intelligence is revolutionizing the way we live and work and is delivering extraordinary benefits to our societies and economies. However, it raises new challenges as well as anxieties and ethical concerns. This puts the onus on governments to ensure that AI systems are designed to respect our values and laws, so that people can trust that their security and privacy will be paramount,” said OECD Secretary-General Angel Guria. : “These principles will become a global starting point for trustworthy artificial intelligence, so that we can harness its capabilities in ways that deliver the best outcomes for all.” (Read the full speech.)
The principles of AI have the support of the European Commission, whose high-level expert group has developed ethical guidelines for trustworthy artificial intelligence, and they will be part of the discussion at the G20 leaders’ summit in Japan. OECD digital policy experts will build on the principles over the coming months to create practical guidelines for their implementation.
Although not legally binding, existing OECD principles in other policy areas have been influential in setting international standards and helping governments develop national legislation. For example, the OECD Privacy Guidelines, which set limits on the collection and use of personal data, underlie many privacy laws and frameworks in the US, Europe and Asia. The OECD Principles of Corporate Governance endorsed by the G20 have become an international standard for policymakers, investors, companies and other stakeholders working on institutional and regulatory frameworks for corporate governance.
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For more details, journalists are invited to contact Catherine Bremer from the OECD Press Office (+33 1 45 24 80 97).
Working with more than 100 countries, the OECD is a global policy forum that promotes policies to improve the economic and social well-being of people around the world.
Other materials in this category: “OECD. Improving economic efficiency and climate mitigation outcomes through international carbon pricing coordination – May 2019 – bg (pdf) OIES. Has Saudi Arabia’s balancing act just gotten easier? – May 2019 – eng (pdf) »Organizations are increasingly looking at the processes, guidelines and governance structures needed to achieve reliable AI. Here are some basic guiding principles that can guide leaders as they think about the ethical implications of AI.
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Artificial intelligence (AI) is emerging as a leading issue in business, public affairs, science, healthcare and education. Algorithms are being developed to help pilot cars, aim weapons, perform tedious or dangerous work, participate in conversations, recommend products, improve collaboration, and make consistent decisions in fields such as law, lending, medicine, university admissions, and employment. But while the technologies enabling artificial intelligence are advancing rapidly, the societal impacts are only beginning to be understood.
Until recently, it seemed fashionable to argue that societal values should conform to the natural evolution of technology, that technology should shape rather than be shaped by social norms and expectations. For example, Stewart Brand stated in 1984 that “information wants to be free.”
In 1999, a Silicon Valley executive told a group of reporters: “You have zero privacy…get over it!”
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But this orthodoxy has been undermined by an ever-expanding catalog of ethical issues with the technology. Although artificial intelligence is not the only technology being used, it tends to capture the lion’s share of discussions about ethical implications.
Many concerns about AI-powered technologies are widespread. AI algorithms embedded in digital and social media technologies can amplify public bias, accelerate the spread of news and misinformation, heighten public reaction, distract us, and harm mental well-being.
AI technologies are being weaponized, experts warn. Semi-autonomous cars are reportedly breaking down in ways owners didn’t expect.
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And while fears of “smart” technology stealing people’s jobs are often overblown, respected economists highlight growing inequality and lack of opportunity for some segments of the workforce due to technology-driven changes in the workplace.
Partly due to such concerns, there are growing calls for AI to be designed and adopted in ways that reflect important cultural values. In a recent editorial, contributor Steven Schwartzman urged companies to take the lead in addressing the ethical issues surrounding artificial intelligence. He commented: “If we are to realize the incredible potential of AI, we must also develop AI in a way that increases public confidence that AI is beneficial to society. We need to have a framework to deal with impacts and ethics.”
Indeed, a large number of ethical frameworks for artificial intelligence have emerged in recent years. For example, a team at the Swiss ETH Zurich University recently analyzed no less than 84 AI ethics statements from various companies, government agencies, universities, NGOs and other organizations.
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While the team found some inconsistencies, there was also encouraging convergence in the broad principles expressed. In another similar experiment, the AI4People group, led by Luciano Floridi, analyzed six high-profile AI ethics statements. They concluded that a set of four stable, higher-level ethical principles—benevolence, nonmaleficence, justice, and autonomy—undertook much of the essence of these six declarations.
These four principles are rooted in major schools of ethical philosophy and have in fact been widely accepted in the field of bioethics for several decades.
Perhaps unsurprisingly, they adapt well to the AI context. Writing in the Harvard Data Science Review, Floridi and co-author Josh Coles note: “Of all the fields of applied ethics, bioethics is the one that most closely resembles digital ethics, dealing ecologically with new forms of agents, patients, and environments. “
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Renowned computational social scientist Matthew Salganik recently advocated the same basic principles in his book Bit by Bit to help data scientists assess the ethical implications of working with human-generated behavioral data. Salganik commented: “In some cases, a principles-based approach leads to clear, actionable solutions. And when it doesn’t, it clarifies the trade-offs involved, which is critical to striking the right balance. Also, the principles-based approach is general enough that it will be useful no matter where you work.
This essay attempts to demonstrate that ethical principles can serve as design principles for organizations seeking to implement innovative AI technologies that are economically viable, beneficial, fair, and autonomous for individuals and societies. Specifically, we propose impact, justice, and autonomy as three key principles that can usefully guide discussions around the ethical implications of AI.
Achieving ethical, reliable, and beneficial AI requires that ethical discussions be based on a scientific understanding of the relative strengths and weaknesses of both machine intelligence and human cognition. In short, being “smart” about AI means being “smart” about AI. For example, discussing ways to promote safe and reliable AI requires understanding why AI technologies, often built through forms of extensive statistical analysis such as deep learning, succeed in some contexts but fail in others. Similarly, discussions of algorithmic fairness should be informed by an appreciation of both the biases and “noise” that affect human drone decisions, as well as an understanding of the trade-offs involved in different notions of algorithmic “fairness”. In any case, ethical discussions are more effective when they are informed by relevant science.
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At the same time, this essay does not prescribe how to apply the basic principles. Organizations differ in their goals and operational contexts and will therefore adopt different declarations, frameworks, rule sets and checklists to help guide the responsible development of AI technologies. Moreover, the application of fundamental principles to specific problems often requires evaluating trade-offs between alternatives, the perceived relative importance of which varies among individuals, organizations, and societies. We suggest that understanding the underlying principles can help people and organizations more effectively create ethical frameworks and think about specific issues.
Two widely recognized ethical principles are nonmaleficence (“do no harm”) and beneficence (“do only good”). These principles are based on the “consequential” ethical theory advocated by John Stuart Mill and Jeremy Bentham, which holds that the moral quality of an action depends on its consequences.
Lack of malevolence means that the AI avoids causing both foreseeable and unintended harm. Examples of the former might include armed AI,
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