This post features two peer-reviewed articles about defining organizational AI governance and exploring the role of responsible AI in ESG investing. The recently published articles are authored by the AIGA research team from the University of Turku.
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.
Algorithmic decision-making is increasing rapidly across industries as well as in public services. By default, AI systems such as machine learning or deep learning produce outputs with no explanation or context. As the predicted outcomes turn into recommendations, decisions or direct actions, humans tend to look for justification. Explainable AI (XAI) provides cues to how and why the decision was made, helping humans to understand and interact with the AI system.
When we advocate for privacy, we tend to concentrate on the negative consequences of privacy violations [56; 32; 19; 50]. These portrayals are extremely important, but they paint only one half of the picture. Privacy also brings about net-positive advantages for individuals and organizations. These advantages can act as powerful internal incentives, driving privacy adoption. A key addition to external incentives like regulation and public pressure.