Welcome to Energsoft!

 Fairness relates to not just the system, the technical component, but it relates to the societal context in which the order deployed. And that means that balance in the context of AI systems is a fundamentally sociotechnical challenge. Climate change is not a joke, and it means that we have got to have a greater diversity of people developing and deploying AI systems to get fresh ideas and new perspectives around the globe.  

 And what we see is that the assumptions and decisions made by teams at every stage of the AI development and deployment lifecycle can introduce biases. For example, is Panasonic battery is better than LG or some small company brand in Zurich? And that is why this is such an important topic.  

 Energsoft platform trustworthiness is not something that we can just sort of delegate to one or two people and call it quits and move on. No! AI accountability is something that everybody must be thinking about actively with all stakeholders always to improve our process.  

 Transparency and intelligibility can help us achieve a diverse range of goals, so things from mitigating unfairness and machine learning systems help developers debug their AI systems to getting more trust from our users. There are two sides to transparency.  

 In part, transparency means that the people who create AI systems should be open about how and why they are using AI and open about the limitations of their operations. Transparency also means that people should be able to understand the behavior of AI systems. Prediction bias is what you often hear, referred to as interpretability or intelligibility. The choice of a training data set determines the behavior of a machine learning model. 

 So how can we bring more transparency to our data? Datasheets for datasets help data creators understand and uncover potential biases in their data that they may have missed or unintentional assumptions that they were making. And they help dataset consumers determine if a dataset is right for their needs. We have put together an initial set of questions that we think cover the critical information that a datasheet should include.  

 Reliability and safety are a concern for every AI system we develop. We need to make sure that the systems we are developing are consistent with the design ideas we have and working in a way that is compatible with our values and principles. And this requires that our systems, our models, are not creating harm in the world. And if there are situations where they may be making mistakes, we push products out there with quantified and well-understood risks and damages in a way that we share that with our users.  

 Our customer's trust depends on the guiding principle we have concerning reliability and safety, and it is a concept that applies to every AI product we have in the company. When we think about safety, the first examples that come to mind are self-driving cars. But it's not even limited to those physical systems, physical agents, and we worry about harm to human lives when a mission learning model is making predictions about people's health, in hospitals, you know when they are making predictions about the diagnosis. 

 Wrong systems can lead to harm for people, so those are the cases that we worry about because the threat is to human lives.   

But this does not mean that this issue is only for those physical systems. Reliability is a big concern, and small mistakes may pile up when an order gets used many times across a large group of people, and that is why it is a concern for everything that we build.

 Despite all the complexity, with these new models and with new technology that can be somewhat unpredictable and somewhat hard to interpret, we are still accountable for how our technology impacts the world. Second, Energsoft thinks about accountability as the structure that we put in place so that we make sure that we consistently are enacting our principles and that we're taking them into account in everything that we do.  

 Also, part of our accountability is to help our customers and partners be accountable. We have a set of principles that guide how we develop and how we sell and how we advocate for regulation on facial recognition. Because we feel like all three of those pieces are critical to be accountable. 

 We think it has a lot of great uses, and we also believe there is a lot of applications that could interfere with people's civil liberties or push society in a direction that we're not interested in supporting. So today, we have a set of guidelines that they can follow and think about these sorts of considerations at every step of the life cycle, and that's a first in this company. Energsoft believes in many other places outside this company as well.