✍️ Column by Masa Sweidan (@masasweidan), our Business Development Manager.
The fascinating and ever-changing world of AI Ethics is one that encompasses a variety of principles that continue to shape the development and use of technology. Perhaps its greatest appeal is that it unites such diverse disciplines in an effort to create more holistic solutions for complex problems that arise from the collection of data to train machine learning models. It is therefore not surprising that the very nature of AI Ethics can be overwhelming for anyone who is beginning to explore all that the field has to offer. Hours spent sifting through the detailed research on niche topics or the surface-level buzzwords can be quite confusing and frustrating at times. In order to help you navigate this intersection of AI and ethics in various contexts, here is a curated list of our top 10 recommendations that can kickstart your exciting journey.
- Explaining the Principles to Practices Gap in AI. Summary written by Abhishek Gupta.
“As many principles permeate the development of AI to guide it into ethical, safe, and inclusive outcomes, we face a challenge. There is a significant gap in their implementation in practice. This paper outlines some potential causes for this challenge in corporations: misalignment of incentives, the complexity of AI’s impacts, disciplinary divide…”
- Artificial Intelligence and the Privacy Paradox of Opportunity, Big Data and The Digital Universe. Summary written by Connor Wright.
“Thanks to the pandemic, internet connectivity is increasing, and companies more efficiently sharing our data, even our most private data…isn’t. This paper explores data privacy in an AI-enabled world. Data awareness has increased since 2019, but the fear remains that Smith’s findings will stay too relevant for too long.”
- The Algorithm Audit: Scoring the Algorithms That Score Us. Summary written by Dr. Andrea Pedeferri.
“Is it right for an AI to decide who can get bail and who can’t? This paper proposes a general model for an algorithm audit that is able to provide clear and effective results while also avoiding some of the drawbacks of the approaches offered so far. The model involves ethical analysis of algorithms into a set of practical steps and deliverables.”
- Owning Ethics: Corporate Logics, Silicon Valley, and the Institutionalization of Ethics. Summary written by Nga Than.
“This paper outlines the role of ‘ethics owners,’ a new occupational group in the tech industry, whose jobs are to examine ethical consequences of technological innovations. The authors highlight competing logics that they have to navigate, and two ethical pitfalls that might result from those different imperatives.”
- The Chief AI Ethics Officer: A Champion or a PR Stunt? by Masa Sweidan.
“We have reached a point where the far-reaching impacts of AI’s ability to identify, prioritize and predict can be felt in virtually every industry. Over the last couple of years, both researchers and practitioners have established that the power relations embedded in these systems can deepen existing biases, affect access to reliable information and shape free speech.”
- Algorithmic Impact Assessments – What Impact Do They Have? Summary written by Dr. Iga Kozlowska.
“Algorithmic Impact Assessments (AIAs) are a useful tool to help AI system designers, developers and procurers to analyze the benefits and potential pitfalls of algorithmic systems. To be effective in addressing issues of transparency, fairness, and accountability, the authors of this article argue that the impacts identified in AIAs need to as closely represent harms as possible.”
- In AI We Trust: Ethics, Artificial Intelligence, and Reliability. Summary written by Dr. Andrea Pedeferri
“The European Commission’s High-level Expert Group on AI (HLEG) has developed guidelines for a trustworthy AI, assuming that AI is something that has the capacity to be trusted. But should we make that assumption? Apparently no, according to this paper, where the author argues that AI is not the type of thing that has the capacity to be trustworthy or untrustworthy: the category of ‘trust’ simply does not apply to AI, so we should stop talking about ‘trustworthy AI’ altogether.”
“Would you relate to a chatbot or voice assistant more if they were female? Would such conversational AI help you feel less lonely? Our event summary of our collaboration with Salesforce sets out to discuss just that.”
- Exploring the Under-Explored Areas in Teaching Tech Ethics Today by Dr. Marianna Ganapini.
“Join us again for some new exciting ideas on how to shape curriculum design in the ethics of tech space. This month Chris McClean shares his experience as the global lead for digital ethics at Avanade, and we are excited to learn more about how it trains tech and business professionals to recognize the most pressing ethical challenges.”
- Algorithmic Bias: On the Implicit Biases of Social Technology. Summary written by Abhishek Gupta
“The paper presents a comparative analysis of biases as they arise in humans and machines with an interesting set of examples to boot. Specifically, taking a lens of cognitive biases in humans as a way of better understanding how biases arise in machines and how they might be combatted is essential as AI-enabled systems become more widely deployed.”
Hopefully this list can act as a starting point to introduce some of the main themes that you are bound to come across in the field of AI Ethics. As always, feel free to reach out to me if you have any feedback or questions!