Information School News

On 13th October, over a hundred and twenty information professionals gathered online for the formal launch of The impact of AI, machine learning, automation and robotics on the information professions: A report for CILIP https://www.cilip.org.uk/general/custom.asp?page=researchreport The report explores how the information professions can respond to the opportunities and challenges presented by such technologies as Artificial Intelligence. Speakers included Nick Poole (Chief Executive, CILIP), Sue Lacey Bryant ( National Lead NHS Knowledge and Library Services), Sir Alan Wilson (Turing Institute), Jo Cornish (Chief Development Officer, CILIP) and Andrew Cox, the author of the report. A recording of the event can be found at: https://vimeo.com/636947943

Senior Information School Lecturer Dr Andrew Cox has recently authored a CILIP-funded research report, which aims to help information professionals to understand how AI, machine learning, process automation and robotics are either already impacting the daily work of healthcare information professionals or likely to do so in the near future. The report, sponsored by Health Education England, can be found here .

Gianmarco Ghiandoni, PhD student in our Chemoinformatics research group, recently attended and presented at the UK-QSAR conference in Cambridge. Gianmarco attended the conference and presented a part of his PhD project, which involves the development of "Reaction Class Recommender Systems in de novo Drug Design". 'These algorithms are machine learning models that have recently acquired great importance due to their effectiveness in product recommendation', Gianmarco said. 'In particular, companies such as Amazon, Netflix, Spotify, etc., have built their reputations and businesses on the top of these models. At Sheffield, we have decided to apply these methods in order to produce suggestions for decision making in automated molecular design. The results from their application indicate that recommender systems can improve the synthetic accessibility of the designed molecules whilst reducing the computational requirements.'

A couple of weeks ago I travelled to Atlanta, USA to attend ACM FAT* - an interdisciplinary conference that addresses issues of Fairness, Accountability and Transparency in Machine Learning. Officially, I was there on the hunt for potential papers and authors to invite to submit their work to Online Information Review . However, the FAT* field is also closely related to my research interests around the politics of data and algorithms, and my teaching on the Information School’s MSc Data Science . I was keen to check out what was happening in the FAT* community, and feed my findings back into my teaching and into two new projects I am working on in this field: CYCAT & supervising a new PhD student – Ruth Beresford – whose research will investigate algorithmic bias in collaboration with the Department for Work and Pensions. I was privileged to hear a number of great papers – the best of which engaged critically with issues of social context and justice. My two favourite pa...