Approaching
Beneficial Artificial Intelligence in the Context of […]

The following is concerned with the topic of beneficial artificial intelligence. “Beneficial”, in this context, means that systems that use AI act in a way that is desired by humans. Therefore two models will be suggested: the first approaches a classification for aspects of beneficiality in artificially intelligent systems. The second one explores methods to capture and counter ethical problem spaces. The defined models will be explored in three speculative use cases to examplify and refine them.

Topic

Master Thesis

Timeframe

Mar - July 2019

Insitution

Hochschule für Gestaltung, Schwäbisch Gmünd

Students

Mark F. Meyer, Nico Göckeritz

Supervision

Prof. Dr. Ulrich Barnhöfer, Prof. David Oswald

The impact areas of AI are growing constantly as agents advance in capabilities

As soon as AI systems influence our daily life – and in many ways they already do – it becomes essential to design them to act in a beneficial way now and looking forward. Establishing ethics as an integral part of design, economics, engineering and development will take time and lots of dedicated work. The constant increase in intelligence makes work on ethical system a relevant issue long before ASI has been attained. A system does not need to be super-intelligent to cause serious issues. For example as the autonomy in operation of those agents increas- es utterly new questions for example in the area of accountability for action will arise.

we believe that…

Technology & ethics have to be established as a singular vision to enable beneficial artificial outcomes. We believe, that future development of AI must serve an overall purpose. It must preserve and enhance human life, as well as preserve and enhance the planet and its eco-system. There are great chances for AI to do so, but there are also numer-ous risks where AI could lead to an extremely negative outcome. It is of utmost importance to focus our efforts on the positive chances AI can enable us and to specifi- cally target the risks that might occur so that they can be avoided. We do not believe it is a viable option to consider reversing or degenerating the develop-ment of AI, as the pace of development is already fairly high. This unavoidable development strengthens our focus and the overall relevance on working towards the beneficial use of AI.

Approaching
Beneficial Artificial Intelligence in the Context of […]

Framing Beneficial…

Approaching beneficial AI requires approaching what is to be considered beneficial. Maximizing beneficiality in an artificial agent’s behaviour can be seen as a funda-mental goal in ensuring its safe operation and the promotion of goals considered broadly beneficial. To structure different actions and cluster steps relevant in approaching a system’s ben- eficial behaviour, the diverse aspects that contribute to the process can be classified in a model. The layers represent key areas of focus when designing beneficial agents. This model is not a conclusive instruction to achieve beneficial beha-viour, but rather is sup- posed to give guidance and establish beneficiality in the developer’s thinking.

Feature 1

Beneficiality depends on the use case, those affected by an agents operation and their values

Feature 2

Beneficial AI is a multidiciplinary endeavour that cannot be solved, only approached

Feature 3

Beneficial behaviour has to be established from the beginning as an integral part of the system

… Suggesting a Framework to
approach beneficial

Crafting around beneficiality is an interdiciplinary, iterative process involving many diciplines

The debate about how a “common beneficial” should be defined takes place on multiple layers across many disciplines and spans a long timeframe. Though general rough understanding of what is beneficial exists in many cases (e.g., no human should come to harm from an action), it is important to note that actions that are considered beneficial by an individual are not necessarily the most ethical actions. It can be assumed that there is no such thing as an “absolute perfect beneficial” for all affected parties in a situation, consequently a lot of consideration has to be placed on defining the agent’s value system to guide the process of finding a sufficient beneficial for its actions. Furthermore ensuring beneficial behaviour is a recursive endeavour, not a linear process. All of the pillars have to be regularly revisited as circumstances change (e.g. an agent’s level of intelligence increases).

Problem space are complex ethical issues in the way of beneficial intelligence

Feature 1

Problem spaces can only be approached and their effects minimized but not solved.

Feature 2

Problem spaces are often hidden patterns and have to be actively uncovered to be approached.

Feature 3

Problem spaces have many causes and have to be app-roached by multiple measures.

Regarding the potential issues that arise with the creation of artificially intelligent systems we suggest a classification for bigger ethical problems. Approaching these issues is essential as they conflict with the goal of an agent’s beneficial behavior in action. The concept of problem spaces and the suggested approaches repre-sent an ongoing process and shall serve as guidance and inspiration for further work by ourselves and others. Problem spaces are not necessarily the most obvious problems one might think about when developing an artificial agent, but those with potential long term consequences that can result in serious implications for individuals and society. Problem space are high level patterns, existing across a broad range of similar scenarios and manifest themselves as concrete problems in different use cases.

… Approaching problem spaces

Problem Spaces

Counter Measures

Beneficial Goals

We define a problem space as a class of issues that arise when working on intelligent artificial agents that are supposed to act in a beneficial way. Problem spaces are not easy to define concrete issues but rather a space of underlying overarching patterns that manifest themselves in more concrete problems in different use cases.

As problem spaces are abstract issues with many different causes a single approach is not suitable: problem spaces have to be separated into individual smaller issues that can then be approached by different actions. These actions are the connecting piece between abstract spaces and idealised goals.

Each problem space has a counterpart that describes an ideal state. This idealized goal is based on what is considered beneficial and / or morally desirable. It’s not necessarily the complete elimination of the original problem – it rather describes a sufficient stage of a feature, so that it is beneficial.

in the Context of […]

To refine the models and demonstrate how the process of framing and approaching problem spaces can be applied to the creation of artificially intelligent systems we created three rough use cases. Since the underlying objective is to design the applications in the use case in a beneficial way the ‘pillars of beneficiality’ were used as guidance in regard to what should be achieved. The three use cases are set in different scenarios, each taking one step further into the future. The “beneficial layer” in each use case explains the different counter measures that help countering the problem spaces.