Safe and Ethical AI (SEA) Platform Network · Linking Artificial Intelligence Principles (LAIP)

2. AI must be held to account – and so must users

Users build a relationship with AI and start to trust it after just a few meaningful interactions. With trust, comes responsibility and AI needs to be held accountable for its actions and decisions, just like humans. Technology should not be allowed to become too clever to be accountable. We don’t accept this kind of behaviour from other ‘expert’ professions, so why should technology be the exception.

Principle: The Ethics of Code: Developing AI for Business with Five Core Principles, Jun 27, 2017

Published by Sage

Related Principles

Preamble

Two of Deutsche Telekom’s most important goals are to keep being a trusted companion and to enhance customer experience. We see it as our responsibility as one of the leading ICT companies in Europe to foster the development of “intelligent technologies”. At least either important, these technologies, such as AI, must follow predefined ethical rules. To define a corresponding ethical framework, firstly it needs a common understanding on what AI means. Today there are several definitions of AI, like the very first one of John McCarthy (1956) “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” In line with other companies and main players in the field of AI we at DT think of AI as the imitation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and self correction. After several decades, Artificial Intelligence has become one of the most intriguing topics of today – and the future. It has become widespread available and is discussed not only among experts but also more and more in public, politics, etc.. AI has started to influence business (new market opportunities as well as efficiency driver), society (e.g. broad discussion about autonomously driving vehicles or AI as “job machine” vs. “job killer”) and the life of each individual (AI already found its way into the living room, e.g. with voice steered digital assistants like smart speakers). But the use of AI and its possibilities confront us not only with fast developing technologies but as well as with the fact that our ethical roadmaps, based on human human interactions, might not be sufficient in this new era of technological influence. New questions arise and situations that were not imaginable in our daily lives then emerge. We as DT also want to develop and make use of AI. This technology can bring many benefits based on improving customer experience or simplicity. We are already in the game, e.g having several AI related projects running. With these comes an increase of digital responsibility on our side to ensure that AI is utilized in an ethical manner. So we as DT have to give answers to our customers, shareholders and stakeholders. The following Digital Ethics guidelines state how we as Deutsche Telekom want to build the future with AI. For us, technology serves one main purpose: It must act supportingly. Thus AI is in any case supposed to extend and complement human abilities rather than lessen them. Remark: The impact of AI on DT jobs – may it as a benefit and for value creation in the sense of job enrichment and enlargement or may it in the sense of efficiency is however not focus of these guidelines.

Published by Deutsche Telekom in Deutsche Telekom’s guidelines for artificial intelligence, May 11, 2018

· 2) Research Funding

Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as: How can we make future AI systems highly robust, so that they do what we want without malfunctioning or getting hacked? How can we grow our prosperity through automation while maintaining people’s resources and purpose? How can we update our legal systems to be more fair and efficient, to keep pace with AI, and to manage the risks associated with AI? What set of values should AI be aligned with, and what legal and ethical status should it have?

Published by Future of Life Institute (FLI), Beneficial AI 2017 in Asilomar AI Principles, Jan 3-8, 2017

3. New technology, including AI systems, must be transparent and explainable

For the public to trust AI, it must be transparent. Technology companies must be clear about who trains their AI systems, what data was used in that training and, most importantly, what went into their algorithm’s recommendations. If we are to use AI to help make important decisions, it must be explainable.

Published by IBM in Principles for Trust and Transparency, May 30, 2018

Responsible Deployment

Principle: The capacity of an AI agent to act autonomously, and to adapt its behavior over time without human direction, calls for significant safety checks before deployment, and ongoing monitoring. Recommendations: Humans must be in control: Any autonomous system must allow for a human to interrupt an activity or shutdown the system (an “off switch”). There may also be a need to incorporate human checks on new decision making strategies in AI system design, especially where the risk to human life and safety is great. Make safety a priority: Any deployment of an autonomous system should be extensively tested beforehand to ensure the AI agent’s safe interaction with its environment (digital or physical) and that it functions as intended. Autonomous systems should be monitored while in operation, and updated or corrected as needed. Privacy is key: AI systems must be data responsible. They should use only what they need and delete it when it is no longer needed (“data minimization”). They should encrypt data in transit and at rest, and restrict access to authorized persons (“access control”). AI systems should only collect, use, share and store data in accordance with privacy and personal data laws and best practices. Think before you act: Careful thought should be given to the instructions and data provided to AI systems. AI systems should not be trained with data that is biased, inaccurate, incomplete or misleading. If they are connected, they must be secured: AI systems that are connected to the Internet should be secured not only for their protection, but also to protect the Internet from malfunctioning or malware infected AI systems that could become the next generation of botnets. High standards of device, system and network security should be applied. Responsible disclosure: Security researchers acting in good faith should be able to responsibly test the security of AI systems without fear of prosecution or other legal action. At the same time, researchers and others who discover security vulnerabilities or other design flaws should responsibly disclose their findings to those who are in the best position to fix the problem.

Published by Internet Society, "Artificial Intelligence and Machine Learning: Policy Paper" in Guiding Principles and Recommendations, Apr 18, 2017

3. Reward AI for ‘showing its workings’

Any AI system learning from bad examples could end up becoming socially inappropriate – we have to remember that most AI today has no cognition of what it is saying. Only broad listening and learning from diverse data sets will solve for this. One of the approaches is to develop a reward mechanism when training AI. Reinforcement learning measures should be built not just based on what AI or robots do to achieve an outcome, but also on how AI and robots align with human values to accomplish that particular result.

Published by Sage in The Ethics of Code: Developing AI for Business with Five Core Principles, Jun 27, 2017