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: The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI
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.
3. Principle of controllability
Published by: Ministry of Internal Affairs and Communications (MIC), the Government of Japan in AI R&D Principles
Developers should pay attention to the controllability of AI systems.
In order to assess the risks related to the controllability of AI systems, it is encouraged that developers make efforts to conduct verification and validation in advance. One of the conceivable methods of risk assessment is to conduct experiments in a closed space such as in a laboratory or a sandbox in which security is ensured, at a stage before the practical application in society.
In addition, in order to ensure the controllability of AI systems, it is encouraged that developers pay attention to whether the supervision (such as monitoring or warnings) and countermeasures (such as system shutdown, cut off from networks, or repairs) by humans or other trustworthy AI systems are effective, to the extent possible in light of the characteristics of the technologies to be adopted.
Verification and validation are methods for evaluating and controlling risks in advance. Generally, the former is used for confirming formal consistency, while the latter is used for confirming substantial validity. (See, e.g., The Future of Life Institute (FLI), Research Priorities for Robust and Beneficial Artificial Intelligence (2015)).
Examples of what to see in the risk assessment are risks of reward hacking in which AI systems formally achieve the goals assigned but substantially do not meet the developer's intents, and risks that AI systems work in ways that the developers have not intended due to the changes of their outputs and programs in the process of the utilization with their learning, etc. For reward hacking, see, e.g., Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman & Dan Mané, Concrete Problems in AI Safety, arXiv: 1606.06565 [cs.AI] (2016).
2. Transparent and explainable AI
We will be explicit about the kind of personal and or non personal data the AI systems uses as well as about the purpose the data is used for. When people directly interact with an AI system, we will be transparent to the users that this is the case.
When AI systems take, or support, decisions we take the technical and organizational measures required to guarantee a level of understanding adequate to the application area. In any case, if the decisions significantly affect people's lives, we will ensure we understand the logic behind the conclusions. This will also apply when we use third party technology.
3. Human centric AI
AI should be at the service of society and generate tangible benefits for people. AI systems should always stay under human control and be driven by value based considerations. Telefónica is conscious of the fact that the implementation of AI in our products and services should in no way lead to a negative impact on human rights or the achievement of the UN’s Sustainable Development Goals. We are concerned about the potential use of AI for the creation or spreading of fake news, technology addiction, and the potential reinforcement of societal bias in algorithms in general. We commit to working towards avoiding these tendencies to the extent it is within our realm of control.