Paper short abstract:

We survey the current state of open source AI and show that open data, models, and products are a critical first step towards enabling future critical AI research such as probing bias in actual training data (Birhane et al 2023), as well as new data-driven STS methodologies.

Paper long abstract:

When Open AI pulled the plug on a dozen of their language models in January 2024, they rendered more than thousand research papers that used the models unreproducible, devalued, and devoid of core scientific principles (Liesenfeld et al 2023). The incident is a forewarning of what AI research may look like in a world of industry-led, proprietary, closed source technology. The solution is a healthy open source AI ecosystem. Our survey of open source generative AI tracks efforts to build more open, transparent and accountable alternative to the likes of ChatGPT and DALL-E (Liesenfeld and Dingemanse 2024). In this contribution we show in two case studies how open technologies can spark new controversies and enable new research methods (Llama2 vs Big Science Workshop's BloomZ, and DALL-E versus Stable Diffusion).

We argue that open data, models, and products are a critical first step towards enabling future critical AI research such as probing bias in actual training data (Birhane et al 2023), as well as new data-driven methodologies such as controversy mapping or EMCA studies on AI (Mlynar et al 2024).

References

Birhane, A et al, 2023. Into the LAIONs Den: Investigating Hate in Multimodal Datasets.

Mlynář, Jakub, et al. forthcoming. AI within situated action: A scoping review of ethnomethodological and conversation analytic studies.

Liesenfeld, A. et al, 2023. Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators.

Liesenfeld, A., Lopez, A. and Dingemanse, M., under review. A European Open Source Generative AI Index under the EU AI Act.