AI Ethics: towards a sustainable AI and Data business

AI and Ethics is a complex and ofthen discussed topic at different conferences, usergroups and forums. It even got picked up by the European commission. I would argue that it should actually go one step further: it should be part of every corporate responsibility strategy – just like social and environmental elements.

AI Ethics: what is it about?

Since I am heading the Data Strategy at a large enterprise, I am not only confronted with technical and use-case challenges, but also with legal and compliance topics around data. This might sound challenging and “boring”, but it isn’t neither one of them. Technical challenges are often more complex than the legal aspects of data. Many companies state that legal is blocking their data inititives, but often they simply didn’t include legal and privacy on their strategy. So what should you consider when talking about AI Ethics? Basically, it consists out of three building blocks.

Robust

The first building block of ethics is the robustness of data. This is mainly a technical challenge, but it needs to be done right in all senses. It consists of platforms that are prone to errors and vulnerabilities. It is all about access control, access logging and prevention. Data systems should track who accessed data and prevent unrightfull access. Also, it should implement the “need to know” principle: within a large enterprise, one should only access data that is relevant to his/her job purpose. After finishing the project, access should be revoked.

Ethical

Ethics in AI is an important topic, and bias happens often. There are numerous samples out when algorithms use bias. We are humans and are influenced by bias. Bias comes from how we grew up, what experiences we made in life and a lot of our environment. Bias is bad though, as it limits our thinking. In psychology, there is a term for how to overcome this: fast and slow thinking. Imagine you have a job interview (you are the interviewer). A candidate walks in and she immediately reminds you because of some aspects about a person you met years ago and had difficulties with. During the job interview, you might not like her, even though she would be different. Your brain went into fast thinking – input-output. This is built in our brains to prevent us from danger, but often drives bias. It helps us driving a car, doing sports and alike. If you see an obstacle in your way driving a car, you need to react fast. There is no time to think over it again. However, when making decisions, you need to remove bias and think slow.

Slow thinking is challenging and you fully need to overcome bias. If you let bias dominate you, you won’t be capable of doing good decisions. Coming back to the interview example, you might reject the candidate because of your bias. After some month, this person found a job at your competitor and is building more advanced models than your company. You lost a great candidate because of your bias. This isn’t good, right?

There are other aspects to ethicas and I could probably write about this an entire series. But you also need to consider other topics, such as harrasement in algorithms. If your algorithms don’t take ethics into consideration, it isn’t just about acting wrong. You will also loose the credibility with your customers and thus start to see financial impact as well!

Legal

Last but not least, your data strategy should reflect all building blocks of legal frameworks. With the right to forget, this needs to be implemented in your systems. In enterprise environments, it isn’t easy at all. There is a lot of legacy and different systems consuming data. To tackle this from a technical perspective, it is necessary to harmonize your data models. Depending on your company ownership and structure, you need to implement GDPR and/or SOX. Different industries even come with more regulations, such as the finance industry, giving you more challenges around data. It is very important to talk to your legal department and make them your friends at an early stage in order to succeed!

So what is next for AI Ethics?

I keep it with the previous statement mentioned several times: work closely with Legal and Privacy in order to achieve a responsible strategy towards data and AI. A lot of people I know claim that AI Ethics rather blocks their strategy on data, but I argue it is the other way around: just because you can do stuff with data, it doesn’t justify doing all of what you potentially could do. By the end of the day, you have customers that should trust you. Don’t miss-use this trust and build an ethical strategy on it. Work with those people that know it best – Privacy, Security and Legal. Then – and only then – you will succeed.

I also recommend you reading my post about data access.

Credits: the three pillar points weren’t invented by myself, so I want to credit those people that gave me the ideas around it: our corporate lawyer Daniel, our Privacy Officer Paul and our Legal Counsel Doris.