The Montreal AI Ethics Institute uses their website to promote research on AI Ethics, and project AIGA is honored to be featured on this platform. We wrote a short piece to summarize our findings from a paper “Defining organizational AI governance”. The original paper was published in AI and Ethics journal earlier this year. “We …
Samuli Laato was recently awarded an early-career award on his work conducted within the AIGA consortium. Samuli and co-authors presented a paper “AI Governance in the System Development Life Cycle: Insights on Responsible Machine Learning Engineering” at the CAIN’22 conference, which combines artificial intelligence and software engineering.
If you are the slightest bit aware of ethical AI or AI governance, you’ve probably heard about principles such as transparency, explainability, fairness, non-maleficence, accountability or privacy. It is easy to agree with these principles – the real question is how should we translate them into meaningful actions?
For years, the American hiring company HireVue had used a controversial AI application to analyze candidates’ facial features and movements during job interviews. In January 2021, the company had undergone an independent audit that proved its algorithms to be unbiased, or so they claimed. The case received public attention when critics argued that the hiring company had misrepresented the audit results.
Were the job candidates assessed fairly by the algorithms? Who should have ensured that the auditing itself was unbiased? The algorithmic auditing industry is emerging and questions like these reveal its complex nature.
The Artificial Intelligence Governance and Auditing (AIGA) project invites you to a live seminar and networking event on November 11. The seminar speakers represent the major Finnish AI initiatives. After the seminar, there is a chance to network while enjoying coffee and snacks. The seminar is open for all, but requires registration.
The AI services and products are developed to answer our needs today – or at least in the near future. As the years go by, the needs will change and the technology might be used very differently to what was initially thought. In this blog post, we argue that responsible AI development also involves doing our best to imagine such unexpected uses. It is important that we explore, critique, and discuss the way today’s technologies might shape the future.
Fair use data is one of the key elements of responsible AI. We shouldn’t only care about the quality of the data, but also how it was retrieved (mind you, often there are important connections between the two). In the digital economy, personal data is currency. Platforms like Facebook or Snapchat appear free but, as we are finally becoming aware, they are not. Are we, as users, paying too high of a prize for these services? In this blog post, we wish to show that re-shifting the flows of personal data is possible.