I Impact

Sometimes, companies might be tempted to adopt new AI systems because they want to be ahead of the game and on top of the latest technological advancement, rather than because they really need them or because they benefit their business. Just because a company can use a certain AI technology, it doesn't necessarily mean that it should. Measuring the impact of AI is important to help companies to avoid unnecessary costs and potential risks deriving from the use of inadequate or inappropriate technologies.
Principle: IBE interactive framework of fundamental values and principles for the use of Artificial Intelligence (AI) in business, Jan 11, 2018

Published by Institute of Business Ethics (IBE)

Related Principles

· Transparency

As AI increasingly changes the nature of work, workers, customers and vendors need to have information about how AI systems operate so that they can understand how decisions are made. Their involvement will help to identify potential bias, errors and unintended outcomes. Transparency is not necessarily nor only a question of open source code. While in some circumstances open source code will be helpful, what is more important are clear, complete and testable explanations of what the system is doing and why. Intellectual property, and sometimes even cyber security, is rewarded by a lack of transparency. Innovation generally, including in algorithms, is a value that should be encouraged. How, then, are these competing values to be balanced? One possibility is to require algorithmic verifiability rather than full algorithmic disclosure. Algorithmic verifiability would require companies to disclose not the actual code driving the algorithm but information allowing the effect of their algorithms to be independently assessed. In the absence of transparency regarding their algorithms’ purpose and actual effect, it is impossible to ensure that competition, labour, workplace safety, privacy and liability laws are being upheld. When accidents occur, the AI and related data will need to be transparent and accountable to an accident investigator, so that the process that led to the accident can be understood.

Published by Centre for International Governance Innovation (CIGI), Canada in Toward a G20 Framework for Artificial Intelligence in the Workplace, Jul 19, 2018

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

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

5 Ensure inclusiveness and equity

Inclusiveness requires that AI used in health care is designed to encourage the widest possible appropriate, equitable use and access, irrespective of age, gender, income, ability or other characteristics. Institutions (e.g. companies, regulatory agencies, health systems) should hire employees from diverse backgrounds, cultures and disciplines to develop, monitor and deploy AI. AI technologies should be designed by and evaluated with the active participation of those who are required to use the system or will be affected by it, including providers and patients, and such participants should be sufficiently diverse. Participation can also be improved by adopting open source software or making source codes publicly available. AI technology – like any other technology – should be shared as widely as possible. AI technologies should be available not only in HIC and for use in contexts and for needs that apply to high income settings but they should also be adaptable to the types of devices, telecommunications infrastructure and data transfer capacity in LMIC. AI developers and vendors should also consider the diversity of languages, ability and forms of communication around the world to avoid barriers to use. Industry and governments should strive to ensure that the “digital divide” within and between countries is not widened and ensure equitable access to novel AI technologies. AI technologies should not be biased. Bias is a threat to inclusiveness and equity because it represents a departure, often arbitrary, from equal treatment. For example, a system designed to diagnose cancerous skin lesions that is trained with data on one skin colour may not generate accurate results for patients with a different skin colour, increasing the risk to their health. Unintended biases that may emerge with AI should be avoided or identified and mitigated. AI developers should be aware of the possible biases in their design, implementation and use and the potential harm that biases can cause to individuals and society. These parties also have a duty to address potential bias and avoid introducing or exacerbating health care disparities, including when testing or deploying new AI technologies in vulnerable populations. AI developers should ensure that AI data, and especially training data, do not include sampling bias and are therefore accurate, complete and diverse. If a particular racial or ethnic minority (or other group) is underrepresented in a dataset, oversampling of that group relative to its population size may be necessary to ensure that an AI technology achieves the same quality of results in that population as in better represented groups. AI technologies should minimize inevitable power disparities between providers and patients or between companies that create and deploy AI technologies and those that use or rely on them. Public sector agencies should have control over the data collectedby private health care providers, and their shared responsibilities should be defined and respected. Everyone – patients, health care providers and health care systems – should be able to benefit from an AI technology and not just the technology providers. AI technologies should be accompanied by means to provide patients with knowledge and skills to better understand their health status and to communicate effectively with health care providers. Future health literacy should include an element of information technology literacy. The effects of use of AI technologies must be monitored and evaluated, including disproportionate effects on specific groups of people when they mirror or exacerbate existing forms of bias and discrimination. Special provision should be made to protect the rights and welfare of vulnerable persons, with mechanisms for redress if such bias and discrimination emerges or is alleged.

Published by World Health Organization (WHO) in Key ethical principles for use of artificial intelligence for health, Jun 28, 2021