EACL 2024 is happening in Malta this week. What a great time to look into the role of the Diversity & Inclusion Chair at a conference and what they actually do.

How does ChatGPT react when users disclose information about their sexual orientation or their gender? While the system has improved in comparison to its predecessors, there is still a lot left to be desired.

Automatic Sentiment Analysis tetermines whether a statement conveys a positive or a negative sentiment, for example in product reviews. But queer identity terms in the text lead to unintended consequences.

Bias bounties engange volunteers to hunt for biases in AI systems, framing it as a contest and offering reward money. But can company-organised events live up to the promise of co-creating fair systems?

It is easy to come up with examples for biased AI systems: the world is full of them. But what actually is bias and how can we measure it? This is a short primer.

We reflect on what the award-winning Queer in AI FAccT paper has to say about participatory governance in AI, and what lessons government leaders, agencies, and stakeholders might take away from the paper.

Scientific publications are the currecy in which academics are measured, but what if your publication record vanishes from one day to the next? Trans acadamics face this threat when they change their name. I talked to Tess Tennenbaum about the name change policies of academic publishers, surprising victories and the work that lies ahead.

Did you miss the Queer in AI workshop at ACL 2023? Here is a round-up of all presented papers to soothe the FOMO and get a taste for all the amazing queer NLP research happening at the moment.

Text-to-image systems do a terrible job at representing marginalised people, and those who market text-to-image systems scramble for mitigation strategies. But what do queer people actually want from these systems? I talked to Eddie Ugless who surveyed 35 non-cisgender people about the harms coming from text-to-image systems and how they want to be depicted by AI.

Most gender-bias papers in natural language processing operate under wrong assumptions and actively hurt the queer community. I talked to Hannah Devinney about changing this frustrating reality, what it takes to build systems that serve queer people and better ways into the future of NLP.

AI ethics toolkits offer pre-implemented bias metrics and mitigation algorithms. But do they actually help developers to build better machine learning models? A recent study shows three ways in which AI ethics toolkits fall short and what to do about it.

How do you bring your personal values into the work space? In this blog post we examine one way in which this task is is handled by employers and employees in German AI start-ups.

Have you ever wondered how to refer to non-binary people in a language that doesn’t have gender-neutral pronouns? I talked to Manuel Lardelli about his research into gender-fair translation, dealing with resistance and the complexities of language.

This week I talk to Diana Galván about building community and creating visibility with Latinx in AI.

Intersectionality can help you examine and combat harms that arise from your machine learning system. Here are seven ways to make it happen.

At what point at the ChatGPT hype cycle are we at the moment? In this blog post we look at the one thing ChatGPT lacks most and how to talk to your grandmother about it.

Bringing ethics to a world of AI optimism is no easy task. Read about how Arjun Subramonian tackles it with queer collaborators, sage mentors and their new paper on intersectionality.

ChatGPT can call me a dyke, but forbids me to use the word as input. In this blog post I talk about the clumsy attempts of the makers of large language models to avoid queer terms, lest they reveal some uncomfortable truths.

A lot of AI ethics work concentrates on the bad things happening, but what could hopeful AI building look like? In this post I talk to Vagrant Gautam about xyr trajectory in the field, about AI as a community garden and the art of balancing technical and ethics work.

Writing the opening paragraph of a machine learning paper is an unconfortable taks. Hi, my paper is the best thing you can possibly read! In this blog post we look at the societal values machine learning researchers highlight in their papers, and the uncomfortable truths they reveal.