Using AI Effectively at Work

These resources highlight key issues surrounding the ethical implications of AI. The report on AI adoption in science provides an overview of how AI is shaping research trends. The article on labour exploitation in the AI industry discusses the ethical concerns of underpaid gig workers who power AI systems. There are valuable insights into ‘AI for social good’, offering a critical view of its impact, and practical advice on prompt engineering for effective LLM use. Another article examines the environmental cost of training large AI models, while another explores how big data in criminal justice challenges established criminal procedures.

1. This report gives a comprehensive overview of AI adoption in scientific research, with insights into future trends.
   Reference: Hajkowicz, S., Naughtin, C., Sanderson, C., Schleiger, E., Karimi, S., Bratanova, A., & Bednarz, T. (2022). Artificial intelligence for science – Adoption trends and future development pathways. CSIRO Data61, Brisbane, Australia.
2. This resource highlights the often-overlooked exploitation of gig workers in the AI industry, arguing that addressing labour abuses, such as underpaid and highly surveilled data labellers, content moderators, and delivery drivers, should be central to AI ethics efforts, rather than solely focusing on debiasing data and ensuring transparency.
   Reference: Williams, A., Miceli, M., & Gebru, T. (2022, October 13). The exploited labor behind artificial intelligence: Supporting transnational worker organizing should be at the center of the fight for “ethical AI”. Noema.
3. This article explores the potential of ‘AI for social good’.
   Reference: Moorosi, N., Sefala, R., & Luccioni, S. (2023, December). AI for whom? Shedding critical light on AI for social good. In NeurIPS 2023 Computational Sustainability: Promises and Pitfalls from Theory to Deployment.
4. This guide offers practical, hands-on advice for effectively using LLMs, and is regularly updated.
   Reference: Anthropic. (2024). Prompt engineering overview.
5. This article examines the environmental impact of AI development, focusing on the energy consumption of training large models and the need for more sustainable AI practices.
   Reference: Strubell, E., Ganesh, A. and McCallum, A., 2020, April. Energy and policy considerations for modern deep learning research. In Proceedings of the AAAI conference on artificial intelligence (Vol. 34, No. 09, pp. 13693-13696).
6. This article explores how big data, algorithmic analytics, and machine learning are transforming criminal justice by reshaping how crime is understood and addressed, while simultaneously undermining regulatory safeguards, abolishing case-specific subjectivity, and challenging established criminal procedure rules.
   Reference: Završnik, A., 2021. Algorithmic justice: Algorithms and big data in criminal justice settings. European Journal of criminology, 18(5), pp.623-642.