Monthly Archives: August 2016

Third Lecture – Ethics and Accountability

This meeting featured two presentations:

After the presentations we had a discussion:
We talked about how accountability is at the core of ethics. Ethics are not really measurable, there is no way to tell which is the best, most ethical, way of doing something. We agreed that the context in which the algorithm acts is important and that technologies may develop in unpredictable ways. Therefore intentions of designers may not be met in the long run or in different contexts. New problems and dynamics emerge. We reflected upon: How do we define the relationships between networks of machines, people and more? How do we get ethics? What about the invisibility of algorithms and how to open up the black box (i.e. understand  complex algorithm)?

We talked about designing vs developing and concluded that design has people in mind, is sometimes general (i.e. not so much about details), thinks about the context, ethics, what “it” looks and feels like and deals with the formulation of problems. It is situated and explorative. No perfect solutions exist, only design decisions with consequences. In contrast development is about solving a given problem, i.e. finding a correct “solution” (positivism). It is sometimes used as a defence to circumvent making moral choices. But during development small design decisions are done all the time (such as the default value of a variable in a program). In turn development also requires a thoughtful design approach.

The next topic of interest was “Algorithmic Regulations”. They should be about defining a set of goals, such as no gender/race/sex/.. bias or not trying to kill people, and ways of measuring these goals. According to one of the papers, accountability is about:  “How to break up the algorithm?” (deconstruction) and “governance of the algorithm” (“How can we establish accountability?” and “How can we enforce accountability?”).

We talked about algorithms being used in court, advertised as more fair than judges, and ones used by military to make life and death decisions. What does the probability of being a terrorist actually mean? What are the factors? Who chooses the factors? How are the factors fed into the system? What about correlation vs. causality/errors? What does a “like” or a visit to website really indicate about a person? “Fairness” is a social construct and thereby has different meanings in different contexts, like e.g. in different countries. Is there really a way to calculate if someone is guilty or should be killed? Is there a right or wrong / better or worse here? Why do we need an algorithms? Is it because they are more efficient? Algorithms are biased due to the development in a specific context and the political viewpoints of their builders. In this case very important decision processes are suddenly influenced and maybe even solely understood by engineers. The algorithms base their calculations on data collected through algorithms again developed by engineers. It is wrong to assume algorithms would come to “fairer” decisions, but it is still done since they are preceived as “objective”. Algorithmic judgments are executed by people but who is actually making the decisions? Who is responsible? What did the algorithm decide? How did it reach a conclusion? Was it under the control of humans all the time? Modern, complex algorithms (specially many machine learning algorithms) can only be understood to a certain point and complexity is ever increasing. Transparency is usually not a sufficient solution and limited by complexity. It is better have people/developers/users/… do the right things (i.e. be ethical). People should ask themselves constantly: “What is it the right thing to do?”.  Companies/Organisations should have an Ethos. The value of ethics in society needs increase.

Last we talked about different methods of self-protection against algorithmic governance. We came to the conclusion that this option is limited for a number of reasons such as resources of organisations developing countermeasures are usually small compared to organisation developing technology for profit and one needs to be knowledgeable about technology to use the state of the art tooling.