Working paper: Dublin EFMC European Facility Management Conference 2019
A position paper to support my presentation at the 27th European Facility Management Conference.
Title: Creating an ethical framework for applying artificial intelligence in FM
AI Ethics: Effects on human life
“These technologies and the systems they enable are rapidly shifting behaviours and creating new rules for human interaction by virtue of the incentives and boundaries built into their design.”
(World Economic Forum, 2018).
Even the applications of narrow AI will affect radically to our lives and how we see our world. The initial discussion is how much power we want to give to technology and how much we let technology to direct our society and societal development. We still understand the digitalization of society very little.
AI Ethics: Employment and Economic Impacts
The change and challenge AI sets for our employment and distribution of income is potentially enormous (Frey & Osborne, 2017). The tasks and jobs can be automated and replaced are high, and under our current economic paradigm, this possibility will be utilized by our companies and the public sector. Critics of this view usually refer to the Luddites and are promising that a lot of new jobs will be available.
AI Ethics: Data centralization and privacy
Established approaches to privacy have become less and less effective because they are focused on previous metaphors of computing, ones where adversaries were primarily human.
(AI Now, 2017).
AI Ethics: Safety and Security
The development and implementation of AI create threats to our safety and security. These threats include small, practical safety and security issues, but also global concerns on our safety in a more significant meaning. One must learn to understand better safety and security matters related to the technology, starting from the common forms of cybersecurity.
AI Ethics: Algorithm and Data Biases
Algorithm and data biases can cause discrimination and unfair decisions. The transparency of algorithmic decision-making is an essential area of ethical AI. Algorithms and data can easily contain biases whose origin is in human decision-making, and these biases are difficult to detect, perceive and fix. (Campolo, Sanfilippo, Whittaker & Crawford, 2017).
Practical tips for companies to take AI ethics into account
The debate around the themes of AI ethics is heated. We are starting to find the main question marks and ethical concerns, and ideas to solve those. Some of the old debates will continue possibly forever, for example, security vs. privacy, advanced AI and superintelligence, technology philosophical discussion, the correct form of cutting the discrimination out, but that's just the nature of ethics.
A conceptual framework for categorization of ethical risks in machine learning and artificial intelligence
I have formed a conceptual framework for identifying and categorizing ethical risks related to machine learning and artificial intelligence. The starting point for this framework is to facilitate and clarify the big picture of the ethical risks. The framework contains the key categories of the ethical risks, most usable lenses for evaluating the larger development and scaling the field by selected variables.
Summary of AI and robotics ethics, part II: reports, guidelines, strategies
In this series:
1. Summary of AI and robotics ethics, part I: laws and principles
2. Summary of AI and robotics ethics, part II: reports, guidelines, strategies
3. Summary of AI and robotics ethics, part III: selected articles (coming)
A collection of reports, guidelines and strategies on AI Ethics. Ping me at Twitter, if any is missing!
Governmental
Summary of AI and robotics ethics, part I: laws and principles:
In this series:
1. Summary of AI and robotics ethics, part I: laws and principles
2. Summary of AI and robotics ethics, part II: reports, guidelines, strategies
3. Summary of AI and robotics ethics, part III: selected articles (coming)