(Preamble)

Automated decision making algorithms are now used throughout industry and government, underpinning many processes from dynamic pricing to employment practices to criminal sentencing. Given that such algorithmically informed decisions have the potential for significant societal impact, the goal of this document is to help developers and product managers design and implement algorithmic systems in publicly accountable ways. Accountability in this context includes an obligation to report, explain, or justify algorithmic decision making as well as mitigate any negative social impacts or potential harms. We begin by outlining five equally important guiding principles that follow from this premise: Algorithms and the data that drive them are designed and created by people There is always a human ultimately responsible for decisions made or informed by an algorithm. "The algorithm did it" is not an acceptable excuse if algorithmic systems make mistakes or have undesired consequences, including from machine learning processes.
Principle: Principles for Accountable Algorithms, Jul 22, 2016 (unconfirmed)

Published by Fairness, Accountability, and Transparency in Machine Learning (FAT/ML)

Related Principles

Transparency and explainability

There should be transparency and responsible disclosure to ensure people know when they are being significantly impacted by an AI system, and can find out when an AI system is engaging with them. This principle aims to ensure responsible disclosure when an AI system is significantly impacting on a person’s life. The definition of the threshold for ‘significant impact’ will depend on the context, impact and application of the AI system in question. Achieving transparency in AI systems through responsible disclosure is important to each stakeholder group for the following reasons for users, what the system is doing and why for creators, including those undertaking the validation and certification of AI, the systems’ processes and input data for those deploying and operating the system, to understand processes and input data for an accident investigator, if accidents occur for regulators in the context of investigations for those in the legal process, to inform evidence and decision‐making for the public, to build confidence in the technology Responsible disclosures should be provided in a timely manner, and provide reasonable justifications for AI systems outcomes. This includes information that helps people understand outcomes, like key factors used in decision making. This principle also aims to ensure people have the ability to find out when an AI system is engaging with them (regardless of the level of impact), and are able to obtain a reasonable disclosure regarding the AI system.

Published by Department of Industry, Innovation and Science, Australian Government in AI Ethics Principles, Nov 7, 2019

· 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

· 8. Robustness

Trustworthy AI requires that algorithms are secure, reliable as well as robust enough to deal with errors or inconsistencies during the design, development, execution, deployment and use phase of the AI system, and to adequately cope with erroneous outcomes. Reliability & Reproducibility. Trustworthiness requires that the accuracy of results can be confirmed and reproduced by independent evaluation. However, the complexity, non determinism and opacity of many AI systems, together with sensitivity to training model building conditions, can make it difficult to reproduce results. Currently there is an increased awareness within the AI research community that reproducibility is a critical requirement in the field. Reproducibility is essential to guarantee that results are consistent across different situations, computational frameworks and input data. The lack of reproducibility can lead to unintended discrimination in AI decisions. Accuracy. Accuracy pertains to an AI’s confidence and ability to correctly classify information into the correct categories, or its ability to make correct predictions, recommendations, or decisions based on data or models. An explicit and well formed development and evaluation process can support, mitigate and correct unintended risks. Resilience to Attack. AI systems, like all software systems, can include vulnerabilities that can allow them to be exploited by adversaries. Hacking is an important case of intentional harm, by which the system will purposefully follow a different course of action than its original purpose. If an AI system is attacked, the data as well as system behaviour can be changed, leading the system to make different decisions, or causing the system to shut down altogether. Systems and or data can also become corrupted, by malicious intention or by exposure to unexpected situations. Poor governance, by which it becomes possible to intentionally or unintentionally tamper with the data, or grant access to the algorithms to unauthorised entities, can also result in discrimination, erroneous decisions, or even physical harm. Fall back plan. A secure AI has safeguards that enable a fall back plan in case of problems with the AI system. In some cases this can mean that the AI system switches from statistical to rule based procedure, in other cases it means that the system asks for a human operator before continuing the action.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

1. Demand That AI Systems Are Transparent

A transparent artificial intelligence system is one in which it is possible to discover how, and why, the system made a decision, or in the case of a robot, acted the way it did. In particular: A. We stress that open source code is neither necessary nor sufficient for transparency – clarity cannot be obfuscated by complexity. B. For users, transparency is important because it builds trust in, and understanding of, the system, by providing a simple way for the user to understand what the system is doing and why. C. For validation and certification of an AI system, transparency is important because it exposes the system’s processes for scrutiny. D. If accidents occur, the AI will need to be transparent and accountable to an accident investigator, so the internal process that led to the accident can be understood. E. Workers must have the right to demand transparency in the decisions and outcomes of AI systems as well as the underlying algorithms (see principle 4 below). This includes the right to appeal decisions made by AI algorithms, and having it reviewed by a human being. F. Workers must be consulted on AI systems’ implementation, development and deployment. G. Following an accident, judges, juries, lawyers, and expert witnesses involved in the trial process require transparency and accountability to inform evidence and decision making. The principle of transparency is a prerequisite for ascertaining that the remaining principles are observed. See Principle 2 below for operational solution.

Published by UNI Global Union in Top 10 Principles For Ethical Artificial Intelligence, Dec 11, 2017