The Responsible AI Bulletin #29: Incident response for frontier models, language differences in gender bias in ChatGPT, human-AI societal pitfalls.

Welcome to this edition of The Responsible AI Bulletin, a weekly agglomeration of research developments in the field from around the Internet that caught my attention - a few morsels to dazzle in your next discussion on AI, its ethical implications, and what it means for our future.

For those looking for more detailed investigations into research and reporting in the field of Responsible AI, I recommend subscribing to the AI Ethics Brief, published by my team at the Montreal AI Ethics Institute, an international non-profit research institute with a mission to democratize AI ethics literacy.

Deployment corrections: An incident response framework for frontier AI models

Recent history features plenty of cases where AI models have behaved or been used in unintended ways after model deployment. As AI capabilities progress and the scale of adoption of AI systems grows, the impacts of model deployments may become increasingly significant–and this may especially be the case for leading AI developers, such as OpenAI, Google DeepMind, Anthropic, Microsoft, Google, Amazon, and Meta. While AI developers can adopt several safety practices before deployment (such as red-teaming, risk assessment, and fine-tuning) to reduce the likelihood of incidents, these practices are unlikely to pre-empt all potential issues.

To manage this gap, this paper recommends that leading AI developers establish the capacity for “deployment corrections”–a set of tools to rapidly restrict access to a deployed model for all or part of its functionality and/or users. This would facilitate appropriate and fast responses to a) dangerous capabilities or behaviors identified in post-deployment risk assessment and monitoring and b) serious incidents. The paper also describes practices that can lower the barrier to making decisive, appropriate decisions on deployment corrections. 

Continue reading here.

How Prevalent is Gender Bias in ChatGPT? – Exploring German and English ChatGPT Responses

By introducing ChatGPT with its intuitive user interface (UI), OpenAI opened the world of state-of-the-art natural language processing to non-IT users. Users do not need a computer science background to interact with the system. Instead, they have a natural language conversation in the UI. Many users utilize the system to help with their daily work: Writing texts, checking grammar and spelling, and even fact-checking their work. However, non-IT users tend to see the system as a “magical box” that knows all the answers and believe that because machines do not make mistakes, neither does ChatGPT. This lack of critical usage is problematic in everyday use.

We prompt ChatGPT in German and English from a neutral, female, and male perspective to examine the differences in responses. We inspect three prompts in depth after broadly prompting the system to define the problem space. ChatGPT is a good tool to use for drafting texts. However, it still has problems with gender-neutral language and tends to overcorrect if a prompt contains gender. In the end, we still need humans to check the work of machines.

Continue reading here.

Human-AI Interactions and Societal Pitfalls

Generative artificial intelligence (AI) systems have improved at a rapid pace. For example, ChatGPT recently showcased its advanced capacity to perform complex tasks and human-like behaviors. However, have you noticed that content generated with the help of AI may not be the same as content generated without AI? In particular, the boost in productivity may come at the expense of users’ idiosyncrasies, such as personal style and tastes, preferences we would naturally express without AI. To better align our intentions with AI’s outputs (i.e., output fidelity), we have to spend more time and effort (i.e., communication cost) to edit our prompts or revise the AI-generated output ourselves. But what is the impact of this tradeoff at the individual and aggregate levels?

To study this effect, we propose a Bayesian framework in which rational users decide how much information to share with the AI, facing a trade-off between output fidelity and communication cost. We show that the interplay between these individual-level decisions and AI training may lead to societal challenges. Outputs may become more homogenized, especially when the AI is trained on AI-generated content. And any AI bias may become societal bias. A solution to the homogenization and bias issues is facilitating human-AI interactions, enabling personalized outputs without sacrificing productivity. 

Continue reading here.

Comment and let me know what you liked and if you have any recommendations on what I should read and cover next week. You can learn more about my work here. See you soon!