I’ve been working with machine learning for a while and observed the discussion about AI and ethics. From the philosophical perspective the discussion is very much problem-oriented; the discussion is about “paper cuts” from using AI.
I’ve recently looked at the article from SDTimes (sdtimes.org) about AI Ethics (https://sdtimes.com/ai/ai-ethics-early-but-formative-days/) and its early days. I’ve also looked at the book behind this article: The big nine by Amy Webb (https://www.amazon.com/Big-Nine-Thinking-Machines-Humanity/dp/1541773756). It seems that the discussion there misses an important point – that AI is based on machine learning algorithms, which are applied statistical methods.
This applied nature of AI means that it is algorithms using data to make decisions. For me, as a programmer, this poses an important threat – how can I know what is ethical and what is not ethical if it is not in the data? What does ethics mean in terms of programming – how can I evaluate the ethics?
I can break it down into a few programming challenges:
- Requirements on ethics – how can requirements on ethics be expressed?
- Measurements of ethics – how can we measure that something is ethical or not?
- Implementation and traceability of ethics – where does ethics get implemented? Should I look for it in the code? Where?
In the first part, the philosophers could help a great deal. They can point us to the direction of how to reason about ethics and what kind of data we should collect or use when training ML algorithms.
In the second part, we as software engineer researchers, can help. Once we know what ethics can be, we can quantify it. We can even use statistics to approximate ethics for a given algorithm. However, I’ve not seen any approach for that.
Finally, if we know how to measure ethics, we can try to link that to code and try to approximate some sort of traceability of ethics in the program code – at least to start with. Later we can even trace the ethics requirements in the code, just as we profile functions for resources and trace safety requirements.
Well, these are just some of my throughs on the topic. As I said in the title – they are from the perspective of a programmer and researcher applying ML.
For further reading, I recommend to read a great piece of work in ACM Communications: AI judges and juries – Communications of the ACM, Vol. 61 Issue 12.
